Hye-Jin Cho

The Bank Capital Regulation (BCR) Model

Keywords: risks, Portfolio composition, Minimum equity capital regulation

The motivation of this article is to induce the bank capital management solution for banks and regulation bodies on commercial banks. The goal of the paper is intended to mitigate the risk of a banking area and also provide the right incentive for banks to support the real economy.

Keyword. Demand Deposit, On-balance-sheet risks and off-balance-sheet

Part I Introduciton

In Europe, after the Basel 1 (1988) accord, the banking supervision Accords in Basel, Switzerland, Basel 2 (1999) and Basel 3 (2010) have been evolved. The issuer, the Basel Committee on Banking Supervision (BCBS), advised about credit risk (1988.07) at the Basel 1 and amended about market risk (1996.01) with the Basel 1 Amendment. In the revised framework of the Basel 2, operational risk (2004.06) was introduced and enhanced at the Basel 3 (2010.12). In this overall perspective, these Basel Capital Rules have been enhanced up to the Basel 3. For example, the scope of operational risk is enlarged. This requires some reasonable motivation since banks face the situation to manage the cost to follow banking capital regulation rules. Of particular significance is that the government needs to regulate banks to prevent panic from the systemic banking crisis.

Much interest has been aroused in cascading failure of bank run prevention. For example, [START_REF] Friedman | A Monetary History of the United States, 1857-1960[END_REF] observe large costs imposed on the economy of United States caused by bank runs in the 1930s. Upon on much more recent data, in systemically important banking crises of the world from 1970 to 2007, the average net recapitalization cost to the government was 6% of GDP (Gross Domestic product). Fiscal costs associated with crisis management were averaged 13% for GDP (16% of GDP if expense recoveries are ignored), and economic output losses were averaged about 20% of GDP during the first four years of the crisis. Otherwise, if the government decides to adopt the Basel capital regulation framework, the adoption cost will influence the economy of countries. Either households or banks, related parties to economy should pay for the Basel capital regulation as the preventive method in the banking business cycle. An OECD (The Organisation for Economic Cooperation and Development) study released on 17 February 2011, estimated that the medium-term impact of the Basel III implementation on GDP growth would be in the range of 0.05% to 0.15% per year. Economic output would be mainly affected by increase in banks' lending spreads, as banks pass a rise in bank funding costs, due to higher capital requirements, to their customers. Therefore, the situation is that banks are struggling to manage the regulation cost and the government wants to defend about nationwide contagion of other banks.

Insofar as systemic risk is concerned, risks that firms, households and reserve banks and commercial banks face are highly linked to each other. Seen from this point of view, the banking industry and the monetary policy have particular relevance to systemic risk. Undoubtedly, it goes without saying that financial contagion problems through international banks should be measured in the manner of systemic risk management. With the macroprudential approach, it is highly probable that systemic risk is explained by the static model of the general equilibrium. Because on-balancesheet and off-balance-sheet (OBS) risks are structually segmented annually or periodically by financial statements containing the balancesheet. Even though domino effects or contagions can be understood as movements having the future tendency to figure out solutions toward the future, scope of regulation should be articulated by categorization of on-balance-sheet and off-balance-sheet (OBS) risks in the static model. Thus the macroprudential approach to financial regulation covering on-balance-sheet and off-balance-sheet (OBS) risks is intimately linked with the general equilibrium (GE) approach.

On-balance-sheet risks are presented in a fourfold manner and are divided into credit risk, market risk, liquidity risk and systemic risk. Assets of banks have credit risk and market risk. Credit risk is the risk that a borrower will default on any type of debt by failing to make required payments. Market risk is the risk of losses in positions arising from movements in market prices. In case of liquidity risk, there are two major situations. One is emergency capacity of banks. When an illiquidity event takes place, an affected bank typically must borrow funds at interest rates exceeding those paid by other institutions. Another is about the stability of the banking system in case of inducing a large number of depositors to seek withdrawals. I would say liquidity risk in regard of demand deposit is on-the-balance-sheet risks of banks. Credit, market and liquidity risk are portrayed in considerable individual details but systemic risk is negative externality or adverse spillover effect stemming from transaction in which they were not participants. Distinguished from credit risk containing sovereign risk (government risk), counterparty risk (unincorporated entities' risk exposed to financial risk, usually referring to governments, national banks), systemic risk is the risk of collapse of an entire financial system or the entire market, as opposed to risk associated with any individual entity, group or component of a system. George G. Kaufman and Kenneth E. Scott (2003) define "systemic risk" in imprecise terms as below:

"Systemic risk refers to the risk or probability of breakdowns in an entire system, as opposed to breakdowns in individual parts or components, and is evidenced by comovements (correlations) among most or all the parts."

Darryll [START_REF] Hendricks | Defining Systemic Risk[END_REF], who is a practitioner, suggests a more theoretical definition from sciences in which the term originated:

"A systemic risk is the risk of a phase transition from one equilibrium to another, much less optimal equilibrium, characterized by multiple self-reinforcing feedback mechanisms making it difficult to reverse." Banks engage in a number of activities that yield income, entail expenses and manage risks from on-balance-sheet risks to off-balance-sheet risks. Depending on bank characteristics, Banks extend loan commitments, security loans and trade derivative securities. Through extended loan commitments, the borrower has a guarantee of credit at a given interest rate whenever desired during the specific period. The bank receives interest income on the portion of the credit line that the borrower draws upon, and the bank receives non-interest income on the unused portion. Whereas a loan commitment obligates a bank to bring a loan onto its balance sheet upon customers' requests, securitization permits a bank to remove loans from a balance sheet. Trading derivative securities also proved to be significant source of revenues. This claim is supported by the survey of David Van Hoose (2010), by the end of 2008, United States banks held a notional amount of derivatives totally more than 190 trillion dollar, of which about 150 trillion dollar of derivatives' exposure was comprised of interest rate contracts.

In relation to what I have previously said, the general equilibrium (GE) approach is useful to understand the overall mechanism of financial transaction and economic policy. The opposite mechanism of assets and liabilities on bank characteristic in the figure II.1, is different from usual balance sheets of firms. That is, banks regardless of central banks or commercial banks have characteristic that loans are assets of banks and deposits are liabilities.

Figure II.2 Freixas-Rochet (1999) All of these figures are emphasized by capital circulation of the Freixas-Rochet model (1999) in the figure II.2. In conjunction with the Freixas-Rochet model, the equilibrium is detected with the general equilibrium approach with direct relevance to circulation of securities and stability of deposits. In the paper, the general equilibrium appoach is upgraded with an adroit mixture of the balance sheet concept evaluating the value of economic entities and the income statement concept considering the profit to support the existence of financial entities.

Part III Model

Saving preference of Consumers

A microeconomic theory of banks had not been existed before the microeconomic foundation related to banks in the early 1970s. I would suggest add a banking sector at the Arrow general equilibrium model (1953) under the complete financial market assumption. The main purpose of the model has been to explore solutions about the problem of households, firms, banks and regulation bodies.

The two-period model (t=0,1,2) with a unique physical good initially owned by consumers in the economy in which a continuum of ex-ante identical agents is each endowed with one unit of goods at the period t=0, and this good is to be consumed at each period of t=1 and t=2. The consumer chooses her consumption profile (C 1, C 2 ) and the allocation of her savings S between bank deposits D h and securities Σ s∈Ω P s B s h , in a way that maximize her utility function µ under her budget constraints:

Max µ(C 1, C 2 ) C 1 + Σ s∈Ω P s B s h + D h + S h -Σ s∈Ω P s B s h -D h = W 1 C 2 = Π f +Π b +(1+r) Σ s∈Ω P s B s h + (1+r D )D h + (1+r h )S h -(1+r) Σ s∈Ω P s B s h -(1+r D )D h
Where W 1 for her initial endowment of the consumption good, Π f +Π b for respectively profits of the firm and of the bank (distributed to the consumer-stockholder at t=2).

B h denotes for securities, D h for bank deposits. S h denotes for savings. r, r D , r h are interest rates paid by securities, deposits and savings. For each future state of the world s (s∈ω) one can determine the price P s the contingent claim that pays one unit of accounts in state s and nothing otherwise.

The consumer has a well-defined set of desires ("preference"), which can be represented by a numerical utility function. In addition, we assume that the consumer chooses optimally, in the sense that they choose the option with the highest utility of those available to them. It implies that a consumer is solving an optimization problem. An optimization problem has three key components.

a. Selected object The consumer chooses her consumption profile (C 1 , C 2 ) and allocation of her savings S h between bank deposits D h and securities B h . If real assets S h -Σ s∈Ω P s B s h -D h is non-negative, it implies real assets are sufficient to support the household economy.

b. The objective function The consumer maximizes her utility function µ. µ is assumed to be increasing and concave. Notice that preferences are contingent states and do not fit the standard Von Neumann-Morgenstern representation. Incompleteness of preference becomes apparent that decision makers cannot make a decision in the ambiguous situation. However, even though one individual or one factor doesn't decide, the regulator decides the policy with a foretaste of what is to come after annual closing accounts.

c. Constraints

Cash-in-advance 0<D h ≤W 1 , The paper will be based on the Cash-in-advance constraint. This approach that was introduced by Clower (1967) is the requirement that each consumer or firm must have sufficient available cash before they can buy goods.

Price of security h under

d. Arguments

There are concerns about savings that are substituted into consumption by the household like Covas-Fujuta (2010). [START_REF] Diaz | General Equilibrium Implications Of The Capital Adequacy Regulation For Banks[END_REF] adds no capital requirement at basics to reduce consumption and increase savings by the household. [START_REF] Haslag | On Fed Watching And Central Bank Transparency In An Overlapping Generations Model[END_REF] assumed that return to money is positively related to the money growth rate that is a random variable, gross real returns to savings are random. His realized gross real return to savings indicates that the gross real return to savings is a weighted sum of capital and fiat money (which derives its value from government regulation or law, so called as 'fiat currency'). The weight is the share of agent's asset.

In the Waller model ( 2004), Savings are very passively selected by the household depending upon decisions at the previous period. Middle-aged agents have already earned their wage income, as the wage during period t was determined by the previous period's interest rates (a level of the capital stock). Also, they have already decided how much to consume and save (since savings are a fixed fraction of wage income), but they

have not yet decided how to allocate their savings between capital and fiat currency. What these middle aged agents want at this point is just the highest possible interest rate between period t and t + 1, so that they can obtain the best possible return on their savings and can thus consume as much as possible during their period of old age. In the third period of life, retired agents consume their savings and exit the model.

Practically, Christensen-Meh-Moran (2011, Bank of Canada) mentioned, at the timing of events, households deposit savings at banks that use these funds as well as their own net worth to finance the entrepreneur's projects. In the investment frame, exiting (failing to return from the project) agents sell their capital for consumption goods and surviving agents buy this capital as part of their consumption-savings decision.

However, in reality, even though the agent has a house, they need to spend expenditure for renting, maintenance and extension of houses. Savings and real asset portion are large enough to make the loan from banks. It is hard to explain price fluctuation of houses and savings on the economy by depending upon the interest rate of capital stock and deposit, or perfect substitution of consumption. For households, the preference of savings is the matter about existence of household economy making future benefits and directly affecting to the welfare of any individual.

House-price appreciation by the model of Goodhart-Kashrap-Tsomocos (2012) is impressive. Reducing deposit defaults induces more savings circulated by the bank and less self-insurance and by the end, the reduction in self-insurance reduces the number of houses for sale in a good state, which means that house-price appreciation in the boom is higher than otherwise. Most of all, the market incompleteness with deadweight costs of default distorts the housing market. Wealthy agents endowed with houses make their saving decisions accounting for the possibility that deposits will not be fully repaid. When default penalties for banks are low, then households internalize risks putting less wealth into the banking system and hold more in the form of houses. This choice increases the supply of houses that is available in boom, which lowers house prices and raise welfare for agents entering the housing market at that time. To insure that house price reduction in the bad state of the world, households P and F are also presumed to have lower wealth. Likewise, the non-bank is endowed with lower capital in period 1 as well as in the bad state of the world. This model describes the house bubble phenomenon interestingly.

In the model of [START_REF] Lucas | Comment On Financial Intermediation and Monetary Policy In A General Equilibrium Banking Model[END_REF], to support the incompleteness of markets, he pointed out savings that the young split their savings between bank deposits, which promise a fixed nominal return, and a bank equity, which yields an uncertain real dividend. In addition, because a constant fraction of initial wealth is saved, there is no distortion due to fixed nominal interest payments on deposits. Hence regardless of deposits, the bank equity is related to the real effect of monetary policy.

In the paper, at the framework of securities, deposits and real assets with savings, firstly, the relation between savings and real assets (especially houses) can be much more attached. Secondly, deposits included in the total saving amount which are escaped from the one-sided thinking that deposits are equal to savings and can be perfectly substituted to consumption. Thirdly, Securities at uncertainty are affecting to the investment portfolio of households. These dynamics are explained by the following academical detail.

Households choose (C 1 , C 2 , S 1 , S 2 , R 1 ) taking prices (W 1 , W 2 ) as given. Formally, if we consider the 4-factor model containing banks, because banks have the special financial structure having deposits as liabilities and loans as assets, we need to have the different mindset from generally accepted accounting principles about debit and credit accounts. The general equilibrium (GE) diagram is similar with the balance sheet. Distinguishably, the money flow in the paper starts at the bank transaction which is "deposits and borrowings" as liabilities and "claims to corporate" as assets.

Then, deposits of household are the amount to be accumulated in banks. Conveniently, riskier investments than deposits for household are categorized as securities.

Savings are moving in the real household economy by capital circulation of securities, deposits and real assets. In addition, the welfare of each household is moving from dynamics of accumulated savings. In the overlapping generation model, wealth is always given and manages the household economy easily by each generation. In reality, one who didn't get a house as a legacy, she should spend quite a long time to have a house or rent a house. Attention about fundamental wealth related to non-working capital of a household economy should be emphasized. If the consumer spends his income entirely, the total amount W 1 may be spent totally and can be bounded. In this manner, the household always has their fundamental welfare to manage the household economy and consumption can be naturally deduced from the framework of househould economy. That is, W 1 is the budget constraint and the market information is incomplete so we cannot deduce the real variable of consumption in reality. W 1 and incompleted market information are not enough to deduce the real variable of consumption. In terms of the working capital, we are not so surprised to know that capital has various liquidities. For example, a household wants to spend money on a daily basis but should reduce the amount of money by spending it to have fundamental living basis like houses. In the paper, the financial budget constraint is bounded within the W 1 except for S 1 because S 1 is the summation of securities, deposits and real assets. These are vital factors to operate household economy related to financial markets. The working capital concept at the household economy is evoked within the general equilibrium in the paper. In the perspective that the industrial characteristics of banks mainly operated by capital, it makes sense to us that consideration about the household economy should be balanced with the working capital concept of banks.

Then, why we need to define return R 1 in this model? Simply, we think of three variables of W for endowment, S for savings and C for consumption. We assume that the concept of the golden-rule saving rate in Chapter 1 of Barro and Sala-i-Martin, n denotes for the constant exogenous population growth rate, and d is for the constant exogenous rate of depreciation of capital. We know the house saving rate is 5% (usually 2-11%, OECD, Economic Outlook n.91, June 2012). The amount of savings in the economy should grow as population grows. To support this insured saving amount caused by population growth, the return should be enough to make profits to cover the insurance expense in the economy with the general equilibrium (GE) perspective. Then, we can assume the worst senario like a bank-run case. Even though the fix amount of saving deposits is secured, the loss amount induced from total savings by the endinsured savings will go to the consumption part. In this overall perspective of an equilibrium, insuring savings of households seems plausible to support the economy of households, yet it requires further understanding about the profitable dynamic mechanism to operate the economic cycle. I would say the original form is mainly based on the BCR general equilibrium model diagram presenting as the balancesheet format as it can be because on-balance-sheet factors are reflected by two regards -the money flow perspective (profit) and the economic status perspective (net present value).

Original form (savings containing real assets)

Max µ(C 1, C 2 ) C 1 + Σ s∈Ω P s B s h + D h + S h -Σ s∈Ω P s B s h -D h = W 1 C 2 = Π f +Π b +(1+r) Σ s∈Ω P s B s h + (1+r D )D h + (1+r h )S h -(1+r) Σ s∈Ω P s B s h -(1+r D )D h
Simplified form for calculation (savings containing real assets)

Max µ(C 1, C 2 ) C 1 =W 1 -S 1 C 2 =R 1 +W 2 -(1+r)S 1 -S 2
We have the first order condition of consumption at period 1 and 2 as below.

∂! ∂!! = µ'(C 1, C 2 ) -λ = 0 ∂! ∂!! = µ'(C 1, C 2 ) -λ = 0
We know the Lagrange multiplier is 0 as λ = 0, by the

∂! ∂!!
, the first order condition of savings at period 1 and by the

∂! ∂!!
, the first order condition of return from initial investment. In addition, we get λ(1+r 1 ) = 0 by the

∂! ∂!!
, the first order conditon of savings at period 1.

Therefore, what is get from checking the first order condition of household problem is that households consume today or tomorrow regardless of a banking money flow and it is not affected by the return of initial savings. µ'(C 1, C 2 ) = 0 denotes as time indifference about consumption preference. Households operate the household economy related to banks regardless of consumption for today or tomorrow. We conclude consumption choice is not affected by return of initial savings r 1 at the frame of banking money flow related to household.

The house expense portion in deposits held with MFIs (Monetary Financial Institutions) is the asset that can induce the future benefit and considered as priority to invest for a long time. For example, among loans to households of MFIs (5231 billion euros), loans for house purchase are 74%, 3858.1 billion euros in 2013. Note that we focus on the money transaction started from the bank balance sheet, in the concept of savings, House fee is deduced from S h -Σ s∈Ω P s B s h -D h Where h is the period of household. If we suppose the house fee is not contained in savings as below.

Simplified form for calculation (savings without real assets)

Max µ(C 1, C 2 ) = 0 C 1 + Σ 1∈Ω P 1 B 1 h + D 1 = W 1 R 1 +C 2 +(1+r) Σ 1∈Ω P 1 B 1 h + (1+r 1 )D 1 -(1+r) -Σ 2∈Ω P 2 B 2 h -D 2 =W 2 Also, C 1 = (W 1 -S 1 )+(S 1 -Σ 1∈Ω P 1 B 1 h + D 1
The first-order condition

∂! ∂!! = µ'(C 1, C 2 ) -λ = 0 ∂! ∂!! = µ'(C 1, C 2 )-λ = 0 ∂! ∂!! λ = 0 ∂! ∂!! -λ (1+r 1 ) = 0 ∂! ∂!! λ = 0 µ'(C 1, C 2 )=0 ∂! ∂ Σ!∈Ω!!!!! -λ (1+r 1 ) = 0 ∂! ∂ Σ!∈Ω!!!!! λ = 0 ∂! ∂!! = λ (Σ 1∈Ω P 1 B 1 h + D 1 ) = 0
In conclusion, µ'(C 1, C 2 )=0 (time indifference about consumption preference) is the same condition regardless of real assets whether it is contained in savings or not.

Consumption choice is not affected by the interest rate r 1 for initial savings (same condition regardless of real assets whether is contained in savings or not) and summation of securities and deposits Σ 1∈Ω P 1 B 1 h +D 1 . Evidently, this condition appears in this banking model when we ignore real assets which is most stable in the household economy and can be interpreted as the big portion expense and the intangible asset producing future benefits. Therefore, with the condition that real assets are contained in savings, we can explain the house economy affected by the proportion of securities and deposits.

First intention to choose the general equilibrium model in the paper is to offer understandable method to the academic field and professional field. In the practice, the reserve bank has many methods and even they want more methods together in the weighted way. As I can do, I am intending to use real variables than random variables and a lot of Lagrange methods which is very general way to use the general equilibrium. The saving composition matter is more specifically supported by the following empirical data.

The deposit amount traded is different depending upon factor composition of economic models. For example, the European Central Bank announces the Euro areas' deposit amounts for the 4th quarter in 2013 in the Euro areas. Gross saving amount of households is 2521.3 billion euros (growth rate: 2.4). Deposits by non-financial corporations are 1870.7 billion euros. (growth rate: 6.7). Deposits by Insurance corporations and pension funds (financial intermediaries) are 653.2 billion euros. (growth rate: -5.3). Deposits by other financial intermediaries are 1854.1 billion euros (growth rate -3.1). Deposits by government are 440.8 billion euros (growth rate: -1.8). Deposits by non-euro area residents are 2522.9 billion euros (growth rate: -11.2). Therefore, without consideration about deposits by non-financial corporations (1870.7), the comparison between deposits by household (2521.3) and deposits by financial intermediaries (653.2+1854.1=2507.3) is naive explanation.

Loans for house purchase is 3858.1 billion euros. (growth rate: 0.7). It is Longterm liability affecting the existence of household economy. In addition, the total (7341.7) of deposits by insurance corporations, pension funds (653.2, -5.3), other financial intermediaries (1854.1, -3.1), non-financial corporations (1870.7), government (440.8), non-euro area residents (2522.9) are. Also, total (7752.2) of deposits by household (2521.3, 2.4) and Loans for house purchase (3858.1, 0.7) and other loans (796.7, -1.6), consumer credit (576.1, -3.0) are. Savings S h are the sum of Securities Σ s∈Ω P s B s h , Deposits D h , Real assets S h -Σ s∈Ω P s B s h -D h . Households try to control the balance of assets and liabilities because in the situation of uncertainty, to maintain enough Deposits D h for the economic existence of households, households need to invest on securities as of Σ s∈Ω P s B s h posed on uncertainty conditions. Mainly, real assets imply the budget for houses which can afford to manage the residence and invested real assets. For example, if the household has an apartment and there is the redundancy after spending the investment on securities and deposits, it can be the maintenance fee for house decoration or big furniture purchases.

The importance of portion for houses is considerable. Otherwise, if real assets are negative, hence, savings of households are less than the amount of securities and deposits. Even though, the amount of operation in the household is enough with securities and deposits. In the conservatism on the house budget, we can consider the effect on housing. In the paper, Houses of households is considered as future economic assets that support each member of households to make productions.

Through the general equilibrium approach, the link from the bank problem to the household problem is naturally connected. Also, the following technical finding by usage of same accounts at the household problem induces direction of regulation on banks like the portfolio analysis and inital GDP consideration. The following are technical findings with saving preference.

Suppose that ∃ s∈ ⎨G, B⎬, G=Good, B=Bad. In the case 1, risk aversion is a s below. (resp. risk-taking case) Ω= ⎨G, B⎬, C 1 +Σ s∈Ω P s=B B s=B h +D h +S h -Σ s∈Ω P s=G B s=G h -D h =W 1

In this case, portfolio analysis should be detected by regulation because the situation can be changed depending on the status.

In the case 2, incompleteness preference can be considered.

Ω= ⎨G, B, no choice⎬, no choice can be selected by the choquet expectation CEν(µ(x)) = min Π∈core(ν) E Π (µ(x)) where core(ν) = Π⎨ΔS :

Π(A) ≥ ν(A) for all A⊆S⎬ C 1 +0+D h +S h -0-D h =W 1 and D h offset, hence C 1 + !! !!!!

=W

Initial GDP is caused by partition of initial endowment that is combination of consumption set. Hence, regulation direction is originated from initial GDP in this case.

Borrowing composition of Firms -Additional session is in the Part IV.6

The firm chooses its investment level I and its financing (through Real Assets Σ s∈Ω P s B s h + D h , Liabilities to banks Σ s∈Ω P s B s h + D h -L fr (Or Liabilities to central banks L fr ) in a way that maximizes its profit:

Max Π f (P f ) Π f =ƒ(I)+r f (Σ s∈Ω P s B s h + D h )-r L bank (D h + Σ s∈Ω P s B s h -L fr )-r L fr L fr I=S h =D h +Σ s∈Ω P s B s h
Where ƒ denotes the production function of the representative firm. r ƒ is the premium of firm's real assets. r L bank , r L fr are interest rates on bank loans and federal reserve bank loan. D h denotes for bank deposits. B h denotes for securities. Especially B fr denotes for securities of federal reserve banks. L fr are loans claimed by the firm to the federal reserve bank. For each future state of the world s (s∈ω), one can determine the price P s of the contingent claim that pays one unit of accounts in a state s and nothing otherwise. I is the investment level and S h denotes for savings.

Interior Solution P ƒ has an interior solution only when: r ƒ= r L bank =r L fr

The Modigliani-Miller (MM) theorem projects a theme of a theorem on capital structure. The basic theorem states that, under a certain market price process (the classical random work) and an efficient market, in the absence of taxes, bankruptcy costs, agency costs and asymmetric information, the value of a firm is unaffected by how that firm is financed. Firms are indifferent about the composition of borrowings. Given the proposition II of the Modigliani-Miller (MM) theorem, a higher debt-to-equity ratio leads to a higher required return on equity because of the higher risk involved for equity-holders in a company with debt. r E =r 0 + ! ! (r 0 -r D ) where r E is the required rate of return on equity or cost of equity, r 0 is the company unlevered cost of capital (ie. Assume no leverage, r D is the required rate of return on borrowings or cost of debt and ! ! is the debt-to-equity ratio under two assumptions: (1) no transaction costs exist. (2) individuals and corporations borrow at the same rates. However, on the surface, given same ratios of ! ! , two different sized banks are distortly intepreted. Even though this proposition is induced in the absence of the bankrupcy costs, merges of banks like "too big to fail", so called "the size game among big banks, are considerable. Thus it is certainly worth inquiring D+E behind it's apprant dynamics of ! ! .

The BCR model provides a key with which to unlock riddles of firms' borrowing compositions. The borrowing composition of firms imparted dynamics with the preference to maintain Real Assets Σ s∈Ω P s B s h + D h. Regardless of equilibrium, firms prefer to loan from central banks (so called as bonds) than commercial banks. Among the D h and Σ s∈Ω P s B s h , Firms prefer to have D h because of financial stability and preference about certainty. The model reflects the tendancy of banks toward the big size with Real Assets. In the economic existence respect of banks, banks have responsibility to operate the dynamics of the debt-to-equity ratio ! ! and maintain the economic entity of Real Assets D+E in the economy of countries.

Given the firm's problem, we have ambiguity about change of firms because of investments or loan status. Precise explanation about the relation with commercial banks and central banks should be offered. In the general equilibrium (GE), firms choose labor cost and manage the capital for production or business process but labor effect is hard to be clarified with certain connection of commercial banks and federal banks. Hence, the transaction like loan movements (i.e. liabilities to banks, liabilities to central banks, investments) can be selected to explain in this paper. Additionally, Investments is regarded as Real assets to support existence of business entities. It implies firms want to acquire investment budget to maintain real assets that can be requisite for existence of firms. Therefore, by having borrowing preference to have much more stability between liabilities to banks and central banks (so called as bonds), firms pursue to obtain stability to acquire the investment up to the stability of Investments which can be equal to Real Assets. Thus, we can explain dynamics of investments and loans with the firm's property.

There are many arguments to explain the ambiguity of firms with informational asymmetry, shock absorbed by effective capitals, securities, technical shocks, and interest rates on loans and borrowing constraints.

Boyd-Chang-Smith ( 2004) points out two informational asymmetry problems of firms: The moral hazard problem arises because any borrower's project choice is not observable. Also, the costly state verification (CSV) problem arises because, for either type of projects, the return of investments cannot be freely observed by any agent other than the project owner.

In the [START_REF] Nelson | Macroprudential Capital Regulation In General Equilibrium[END_REF] model, at the production function of cobb-douglas standard form, there is a shock variable to the quality of physical capitals. When we face the unanticipated exogenous declines in the productive capacity of physical capitals, available "Effective capitals" for use in the production is diminished. This intends to consider the effect on banks since banks hold claims on physical capitals directly on their balance sheets, this will be loss for banks, which must be absorbed or passed on to outside creditors.

In the [START_REF] Dewartripont | Macroeconomic Shocks And Banking Regulation[END_REF] model, he argues that securities are characterized not only by income rights but also by control rights. Optimal corporate choices are the time-inconsistency. Investors in control of corporate choices must face an incentive that differs from firm-value maximization. So a banking manager has no financial resources to cover an investment cost and turns to investors for financing. The capital structure -that is, the allocation among investors of contingent cash-flow and control rights -is designed at this financial stage.

Covas Fujita (2010) mentioned that the technology shock is distributed as standard normal distribution. Labor and capital rental markets are assumed to be competitive. [START_REF] Diaz | General Equilibrium Implications Of The Capital Adequacy Regulation For Banks[END_REF] thinks that since interest rates on loans are greater or equal than the discount rate, firms prefer to use internal sources (i.e. cash flows) rather than external financing. In addition, he induces that capital depreciation is paid out of firm's cash flow and net investment is entirely financed with debting. In the model of [START_REF] Nuno | Bank Leverage Cycles. P.7. H6.Money Stock Measures[END_REF], they assumed that the firm can only borrow from banks located on the same island.

In the static model of general equilibrium (GE), if we know the GDP endowment as the exogenous factor, we can calculate more at the firm's problem. Indeed, the analysis about GDP like Consumption to GDP, Government Expenditure to GDP, Fixed Capital Formation to GDP, Export to GDP, Net Export to GDP, Money to GDP except for inflation rate and nominal interest rate are used with the general equilibrium (GE) approach.

The effort to figure out ambiguity of firms and overal perspective analysis display a coherent structural and compositional understanding. Then, how we can measure the firm's productivity relating to the banking area in general equilibrium (GE)? The classical viewpoint that there are three basic factors of production, (land, labor and captal) at the production function. Total-factor productivity (TFP) is different from the traditional calculation measured by inputs of labor and capital. The TFP calculation is measured as a Solow residual affecting in total output and not caused by inputs. The equation Y = A × K α × L β in Cobb-Douglas form represents total output (Y) as a function of total-factor productivity (A), capital input (K), labor input (L), and the two inputs' respective shares of output (α and β are the capital input share of contribution for K and L respectively). The problem is that units of A do not admit a simple economic intepretation. We have two ways to calculate TFP (Chen, 2011). Firstly, we obtain the TFP measurement by estimating a production function. Secondly, we establish a model to find the determinant of TFP and uncover whether financial factors exert any effects on TFP. Materials and energy are secondary factors because they are from land, labor and capital.

It can be puzzled whether duplicated or missed amounts are existed in the general equilibrium (GE). The equity portion as capitals is in the liabilities of firms and the wage portion as expense is in the eliminated account of firms. With this production function measure, we are talking about the exact asset amount of firms that is the part of equity at the same time. Let's go back to the definition of the production function. Factors of production are inputs to the production process. Finished goods are the output and the relationship of input and output is the production function. The important point is that we cannot exaggerate too much about money. Indeed, the classical viewpoint is that money is not contained in capital because it is not directly produce any good so it is hard to be related to consumptions of goods at the problem of household.

In the paper, firstly, we are focusing on "capital" including the "financial capital." Financial capital is raised to operate and expand a business and it is net worth (assets minus liabilities) including money borrowed from others. Originally, "Capital" means goods that can help produce other goods in the future, the result of investment. Considering a labor is not realistic because the number of employees at the firm is hard to be considered at the banking problem. Already we consider equity (asset-liability) is the result after considering labor cost. Redundant firm size variable can evoke the biased information if we pursue obtain the general equilibrium (GE) in this model. It's true that wage is the large portion of input at the firm and should be considered distinguishly. However, for the special industry like banking, we need to clarify needed variables to figure out the problem in the academic field for future and in the realistic world for present.

If we contain banks and federal banks in the banking model, we add the real asset variable. It implies the capital concept is naturally inducing the working capital concept. Adding the shock variable to the quality of physical capital [START_REF] Nelson | Macroprudential Capital Regulation In General Equilibrium[END_REF], Gertler-Karadi 2011, Gertler-Kiyotaki 2010) is hard to be measured and needed to be predicted with a lot of unexpected uncertain situation.

Also, the conservative business cycle is deduced. If we assume that multiplier µ exists, µ(D h +B h )>D h +B h -L fr >L fr . This assumption exactly reflects the preference of safetier capital type like real assets > a government bond (lower interest rate on a bond than a loan) > a loan.

Demand Deposit of Bank

Scope of Bank Domestically chartered commercial banks, country branches and agencies of foreign banks, Edge Act corporation.

The bank chooses its supply of loans to firms D h +B fr +L fr , its demand for deposits D h , and the borrowing B fr -L fr in a way that maximized its profit:

Max Π b (P b ) Π b = r L Bank (D h +Σ s∈Ω P s B s h -L fr ) -r Lfr (Σ s∈Ω P s B s fr -L fr ) -r D D h
where r L Bank , r Lfr are interest rates on bank loans and federal reserve bank loan. D h denotes for bank deposits. R D is the interest rate paid by deposits. B s h denotes for securities. Especially B s fr denotes for securities of federal reserve banks. L fr are loans claimed by the firm to the federal reserve bank.

The bank maximizes the profit by choosing its supply of loans L + , its demand for deposits D -and the issuance

Σ s∈Ω P s B s b. Max Π b (P b ) Π b = r L L + + r Σ s∈Ω P s B s b -r D D -L + = Σ s∈Ω P s B s b +D -
The part of banks' problem provides a context which capital circulation within banks is required by other main factors of economy. The problem of banks is presented without equities of banks in fairly balanced debits and credits of banks. This main issue has been to handle the demand deposit in the banking area and it related to the money supply closely. In the data of Board of Governors of the Federal Reserve System, demand deposit and money stock data have been collected from Demand Deposit, Currency and Related items (J.3, Semi monthly) in 1960 to Money Stock Measures in 2012.

Under the fractional reserve banking, deposit is important indicator for economy because of money multiplier effect. In the formula of moneysupply=currency+deposits, demand deposit which has highest liquidity among deposits on the balance sheet of banks is directly related to the M1 of central banks. Diamond and Dybvig model (1983) explains why bank runs occur at an undesirable equilibrium and why banks issue demand deposits that are more liquid than their assets by providing better risk sharing among people who need to consume at different random times. The key to describe the rationality both for the existence of banks and for their vulnerability to runs is the illiquidity of assets, especially by the demand deposit. His conclusion on the bank runs as better indicator of economic distress than money supply is too quick because there is the duplicated section of deposits and money supply. A bank run is the sudden withdrawal of deposits of just one bank and money supply contains the currency section.

In case of bank runs, the government of country should prepare the recovery solution for economy. Regularly, given information about money supply, the government can figure out about both moving of currency and deposits. Krugman (2006) points this out that deposits are usually considered part of the narrowly defined money supply, as they can be used, via checks and drafts, and a means of payment for goods and services and to settle debts. The money supply of a country is usually held to consist of currency plus demand deposits. In most countries, demand deposits account for a majority of the money supply. To explain the correlation between deposit (demand deposit) and money supply, bank runs can be interpreted as the sudden constraint of deposit and money supply. We research on indicators of economic crisis so economic crisis is not the indicator to analyse the status of economy. [START_REF] Gorton | Financial Intermediaries And Liquidity Creation[END_REF] assume that the uniqueness of demand deposits roles as a desirable medium of exchange so the existence of demand for privately produced riskless trading securities induces issuing demand deposits by banks. Actually, under the fractional reserve banking, a bank deposit is not a bailment that implies physical possession of personal property. It moves safely upon the banking revenue process.

Firstly, the property of customer was deposited. In turn, the customer receives an asset called the deposit account. Finally, the deposit account is the liability of the bank on its balance sheet. On the balance sheet of Liabilities of all commercial banks in the United States (2014.01), 70% is the deposit account. The circulation of deposits is important in economy. David Vanhoose (2010) categories the deposit into three sections like transaction deposit, large-denomination time deposit, saving deposits and smalldenomination time deposits, at the United States commercial bank liability and equity capital. Transaction deposit contains non-interest-bearing demand deposits. Transaction deposit is 6% among total liabilities and equity capital of bank balance sheet.

Dewatripont-Tirole (2012) points out that deposit insurance is the prevention of banks runs following the [START_REF] Diamond | Bank Runs, Deposit Insurance, And Liquidity[END_REF].

In the model of Boyd-Chang-Smith ( 2004), even though project return is safe because of a large number of borrowers, he assumes possibility for banks to fail.

Regardless of a single borrower and aggregate of borrower, potential bankers can suggest needless to operate the bank. s In the model of [START_REF] Covas | Procyclicality Of Capital Requirements In a General Equilibrium Model of Liquidity Dependence[END_REF], the bank can raise funds through either deposits or equity so holding equity involves the equity issuance cost.

Diaz model ( 2005) also try to select the considerable sources. For example, firms only source of financing is bank lending the bank can claim the full amount of firm's cash flow. The equity of banks moves under upper limit of dividend (under the hypothesis that the bank can turn equity into dividend with restriction) because of balance sheet constraint.

Goodhart-Kashrap-Tsomocos ( 2012) mentioned shadow banking. The securitized loans, called mortgage backed securities (MBS) can be sold to the non-bank and the non-bank will finance the purchase with a repo loan from the bank (that will have the MBS as collateral)

Federal Reserve Banks and general equilibrium (GE)

The Federal Reserve Bank chooses its investment level I and its financing (through real assets D h +Σ s∈Ω P s B s h , Liabilities to bank D h +Σ s∈Ω P s B s h -L fr (or Liabilities to central bank L fr ) in a way that maximizes its profit:

Max Π b (P b ) Π f = f (I) + r f (D h +Σ s∈Ω P s B s h ) -r L Bank (D h +Σ s∈Ω P s B s fr -L fr ) -r L fr L fr I=S h Where ƒ denotes the production function of the representative firm. r f is the premium of firm's real assets. r L Bank , r L fr are interest rates on bank loans and federal reserve bank loan. D h denotes for bank deposits. B h denotes for securities. Especially B fr denotes for securities of federalreserve banks. L fr are loans claimed by the firm to the federal reserve bank. For each future state of the world s (s ∈ ω), one can determine the price P s of the contingent claim that pays one unit of account in state s and nothing otherwise. I is the investment level and S h denotes for savings.

Interior Solution P f has an interior solution only when: r f = r L Bank = r L fr

General Equilibrium (GE)

General equilibrium (GE) is characterized by a vector of interest rates (r, r D, r h , r f, , r L Bank , r L fr ) and three vectors of demand and supply levels (C 1, C 2, Σ s∈Ω P s B s h , D h ) for the consumer, (I, Σ s∈Ω P s B s h , D h , L fr ) for the firm, (L fr, Σ s∈Ω P s B s h , D h, Σ s∈Ω P s B s fr ) for the bank and (D h , Σ s∈Ω P s B s h , L fr ) for federal reserve banks. Each agent behaves optimally. (i.e., his or her decisions solve P h , P f or P b respectively. If some accumulated variables are not negative, for example, the components Investment I, Savings S h , L fr are not negative, there is the equilibrium in the economy and the existence of each factors like firms, Househoulds, Banks, Federal Reserve Banks is fulfilled. The size of banks is affecting on each agent because equity capitals depend on previous deposits (additional explanation in Part IV, 7). Depending the change of bank size influencing on total deposits D h , the liability of firms is affected by liabilities to banks D h +Σ s∈Ω P s B s fr -L fr , deposits of household D h and real assets of households and firms. This is supported by the following the process of equity capital multiplication.

n n = 0 n = 1 n = 2 n = 3 • • • n = k • • • n → ∞ Deposits D0 = 1 D1 = (1 -β -K) D2 = (1 -β -K) 2 D3 = (1 -β -K) 3 • • • Dk = (1 -β -K) k • • • D∞ = 0 total deposits 1 D = K + β Borrowings - B1 = (1 -β) B2 = (1 -β)(1 -β -K) B3 = (1 -β)(1 -β -K) 2 • • • Bk = (1 -β)(1 -β -K) k-1 • • • B∞ = 0 total borrowings 1 -β K + β OptimizedEquityCapital - OEC1 = K OEC2 = K(1 -β -K) OEC3 = K(1 -β -K) 2 • • • OECk = K(1 -β -K) k-1 • • • OEC∞ = 0 total optimized equity capital K K + β
Thus, in order to obtain a mean return of 0.84%, 0.90%, the bank must invest -61.6%, -61%, -60.4 of total funds in the risky portfolio and 161.6%, 161%, 160.4% in stable portfolio.

Standard deviation that implies the probability, to get mean of returns, is also increasing. Hart and Jaffee (1974) analyzed the properties of the feasible and efficient set with the assumption that the initial equity capital is zero (i.e. K=0). However, it is possible that the intermediary's equity is zero in the substantial degree of leverage (high liabilities to equity ratios

!"#$%&'()$%(* !!!!"#!!"#
). Then, we should assume that the equity is negliglible.

In the paper as the same as [START_REF] Kahane | Capital Adequacy And The Regulation Of Financial Intermediaries[END_REF], we assume the equity is positive (K > 0) so that the opportunity set does not pas through the origin (i.e. the vector of Deposit D, Borrowing B, Optimized Equity Capital = 0 give an infeasible solution).

Then theoretical superior limit for deposits is defined by the following:

Deposits = Σ ∞ n=0 (1-K-β) = ! !!β
Theoretically, superior limit for the equity capital by the firm is defined by the following:

OptimizedEquityCapital = K × Deposit = ! !!β
And the theoretical superior limit for total borrowings in banks is defined by the following:

Borrowings = (1-β) × Deposits = !!β !!β
The process described above by the geometric series can be represented, where Borrowings at stage k are a function of deposits at the precedent stage:

Optimized Equity Capital at stage k is a function of the deposits at the precedent stage: OEC k = K × D k-1 Hence, if the optimized equity capital depends on the initial deposit and assume the terminal condition of bank is liquidation of bank deposits.

(result) Hence, Optimized Equity Capital depends on the previous deposit. In addition, deposit insurance cost also increases because deposit insurance depends on the number of household.

Deposits at stage k are the difference between additional borrowings and optimized equity capital relative to the same stage: D k = B k -OEC k

In the model of [START_REF] Gorton | Bank Capital Regulation In General Equilibrium[END_REF], bank size is given. In the theorem of Modigliani-Miller, the size and composition of banks' balance sheets have no impact on other agents. However, as population grows, insured deposits will increase. Then, the bank size should grow so bank size growth concern should be measured.

K index for the indicator of risk taking

Define the equity capital ratio with respect to total liabilities and equity capital, !"#$%&'()$%(* !!!!"#!!"# , K∈(0,1), the borrowing (from federal banks) ratio !"#!!"# !!!!"#!!"# , β∈(0,1).

Suppose the demand for funds is unlimied, by summing up two quantities, the theoretical equity capital multiplier is defined as: where the equity capital ratio with respect to total liabilities and equity capital, !"#$%&'()$%(* !!!!"#!!"# k is the index to decline to increase the risk at the portfolio of commercial banks. The deposit is fixed at total 1 and borrowings have the constraint cannot be negative value beyond the minimum borrowings β. For example, if deposit=1, the minimum of required equity = 10%, borrowings = 0.3. To increase the k index, the bank should increase the deposit beyond the initial deposit level (1 in this simulation) or allocate the borrowing portfolio.

Conclusion

The minimum capital requirement is a necessary condition for banking sector stability to raise the quality, consistency and transparency of the capital base. However, it has friction with the portfolio management. By using effects of increasing the equity at the portfolio composition, reducing procyclicality (to the financial shocks) and promoting the countercyclical buffer are pursued.

In the Basel 3 system, the risk coverage framework intends to capture all material risks by using counterparty credit risk formula weighted on the external rating of the counter party. Exposure measures contain on-balance sheet, repurchase agreements and securities finance, derivatives and off-balance sheet (OBS) items. In the paper, rather than enlarging the risk contagion, related factors and risk affection scope are detected without overstatement by using the general equilibrium (GE) model and deposit affection to the optimized equity capital. Deposits are in the large portion at the household, firm and banks. To explain risk coverage, by proving correlation of optimized equity capital upon the previous deposit level, the paper aims to ensure that banking-sector-capital requirements take account of the macro-financial environment in which each substantial economic entity operates.

Basel 3 introduced a minimum leverage ratio. The leverage ratio was calculated by dividing Tier1 capital by the bank's average total consolidated assets. In the paper, k index is suggested as the indicator of risk taking. Within the liability, three major fractions like deposits, borrowings and optimized equity capital are considered as the complementary of minimum capital requirement. Assets of commercial banks are mainly consisted with loans and securities. Because the optimized equity capital grows and deposits is restricted by change. Borrowings that are the difference between asset and deposit+equity capital should be checked whether borrowings cover the optimized equity by k index or not.

The combination of portfolio composition test, deposit-equity optimization and k index enables bounding the bank capital regulation problems.

Figure

  Figure II.1 Vanhoose 2008 -Asset/Liability of banks

  Each market clearing I=S (Good market) D h (Firm) -D h (Firm) + D h (Household) -D h (Household) + D h (Bank) -D h (Bank) (Deposit market) L fr (Firm) -L fr (Firm) -L fr (Bank) + L fr (Firm) + L fr (FR:Federal Reserve Banks) -L fr (is clear in this model that the only possible equilibrium is such that all interest rates are equal: r = r L = r D (result)Arrow (1953) If firms and households have unrestricted access to perfect financial markets, then at the competitive equilibrium, banks make zero profit and the size and composition of balance sheet (banks) have no impact on other economic agents.(result)Cho (2014)

  restriction of borrowing] Then, Borrowings can be executed between Deposit 1 and Restriction β [Balance sheet equality constraint] D n = B n -OEC n

  $%&'%!!"#$%$&'!"#$%&'()*%&)+ !"##"$%&'(!!"#$%$&'()*+$#,-."$#./ =

  of required equity is raised from 10% to 15%, k index was as below:!!!.!" !.!!!.!"

  Empirical balance when the capital of a household economy is concerned Ref: European Central Bank, the 4th quarter in 2013

		(billion euros, growth rate)
	Loans for house purchase	insurance corporations and pension funds
	(3858.1, 0.7)	(653.2, -5.3)
	other loans (796.7, -1.6)	other financial intermediaries (1854.1, -3.1)
	consumer credit (576.1, -3.0)	non-financial corporations are (1870.7, 6.7)
		government (440.8, -1.8)
		non-euro area residents (2522.9, -11.2)
	total 7341.7	total 7752.2

Part IV Effects of Equity Capital Regulation

Additional explanation about the borrowing composition of firm's problem -The portfolio composition effected by the minimum equity capital regulatoin.

In the model of [START_REF] Kahane | Capital Adequacy And The Regulation Of Financial Intermediaries[END_REF], the minimum capital requirement causes an unintended result: it worsened, rather than improved the intermediary's condition and increases its probability of ruin. He checks this calculation with the ruin constraint and given standard deviation of rate of return at the portfolio composition of liability, stock and bonds.

In this paper, with the portfolio of risky portfolio and stable portfolio, explanation will be easier to be understood why minimum equity regulation induces for banks to operate riskier portfolio. In addition, it intends to reduce procyclicality (to the financial shocks) and promote the countercyclical buffer.

If we assume that the bank manages a risky portfolio with an expected rate of return of 17% and a standard deviation of 27%. The expected rate of return on equity is 7% and even though, there is pressure to raise the required equity every period, liability is same every period. The bank tries to meet the bank capital condition regulated by financial intermediaries so the bank should operate much more riskier portfolio comparing to the previous period as following. To calculate the portfolio composition, we calculate the expected value (Mean).

Mean 0.12(12/100) × 0.07 + 0.88 × 0 = 0.0084 0.1287(13/101) × 0.07 + 0.8712 × 0 = 0.0090 0.1372(14/102) × 0.07 + 0.8672 × 0 = 0.0096

Suppose that the bank decides to invest in the portfolio having a proportion Y of the total investment budget so that the overall portfolio will have an expected rate of return as above.

We know an expected rate of return of a risky portfolio R p is 17% and an expected rate of return of a stable portfolio is 7%. Hence, we get the risky portfolio proportion Y.


Davide Buscaldi
email: davide.buscaldi@lipn.univ-paris13.fr

Mari Carmen Suárez-Figueroa

Effects of Ontology Pitfalls on Ontology-based Information Retrieval Systems

Keywords: Ontology-based Information Retrieval systems, Ontology quality, Ontology pitfalls

Nowadays, a growing number of information retrieval systems make use of ontologies to improve the access to textual information, especially in domain-specific scenarios, where the knowledge provided by ontologies represents a key factor. Such kinds of retrieval systems are often referred to as ontology-based or semantic information retrieval systems. The quality of ontologies plays an important role in such systems in the sense that modelling errors in the ontologies may deteriorate the quality of the results obtained by these systems. In this paper we provide a comprehensive analysis of how ontology pitfalls have an influence on these kinds of systems. This study allows us to have a more complete understanding of the role of ontology quality in the information retrieval field. Our survey shows that pitfalls may act as an indicator not only of possible problems in ontology design, but also of OWL features overseen by system developers.

INTRODUCTION

Since the introduction of the concept of Semantic Web by Tim Berners-Lee in 2001 [START_REF] Berners-Lee | The Semantic Web[END_REF], the Information Retrieval (IR) research community has shown a growing interest in Semantic Web technologies. Ontologies are one of the most important technologies proposed in the context of the Semantic Web. Many IR researchers saw the possibility to use ontologies as an external knowledge source to be integrated into IR systems in order to improve their performance when accessing to textual information. Therefore, in the last decade, a growing number of IR systems based on ontologies have appeared, such as KIM (Kyriakov et al., 2004), MELISA (Abasolo and Gomez, 2000), and TextViz [START_REF] Reymonet | Ontologies et Recherche d'Information : une application au diagnostic automobile[END_REF].

Like any other resources used in software systems, ontologies need to be evaluated before (re)using them in other ontologies and/or applications. Results obtained by ontology-based applications can be affected by the quality of the ontologies used. Ontology quality improvement, by specifying equivalent and disjoint classes, adding instances and properties, can significantly enhance question answering [START_REF] Poveda | A Double Classification of Common Pitfalls in Ontologies[END_REF] or Web search results (Tomassen and Strasunkas, 2009). Independently from the way an IR system exploits an ontology, it is clear that problems, anomalies or pitfalls that occurred in the design of an ontology built for this specific purpose may affect the results obtained by the IR systems. The use of an analysis tool could help to implement better IR systems and/or correct the detected pitfalls. OOPS!1 (OntOlogy Pitfall Scanner) is such a tool, independent of any ontology development environment, originally intended to help ontology developers during the ontology validation [START_REF] Gómez-Pérez | Ontology Evaluation[END_REF]. Currently, OOPS! provides mechanisms to automatically detect 21 pitfalls out of 29 identified in the on-line catalogue2 .

The objective of this paper is to provide an overview of the potential effects of these pitfalls on ontology-based IR systems. In order to accomplish this objective, we selected 12 out of the state-of-theart systems and studied how they work, identifying common features and understanding which pitfalls may affect them. Unfortunately, it is very difficult to evaluate directly these systems since in most cases they are not publicly available, they have been built to work under very specific conditions, and they do not comply with W3C standards. Therefore, our analysis is based exclusively on the study of the systems as described by the authors in their papers.

The remainder of this paper is structured as follows: Section 2 presents related work in ontology evaluation and the pitfall catalogue used in our study. Section 3 describes general characteristics of the state-of-the-art systems analyzed. In Section 4 the analysis of the possible effects of every pitfall on each system is included. Finally, Section 5 outlines some conclusions and future steps.

ONTOLOGY EVALUATION AND PITFALLS

In the last decade a huge amount of research on ontology evaluation has been performed. Some of these attempts have defined a generic quality evaluation framework [START_REF] Ciorascu | knOWLer -Ontological Support for Information Retrieval Systems[END_REF][START_REF] Ciorascu | knOWLer -Ontological Support for Information Retrieval Systems[END_REF], [START_REF] Gangemi | Modelling Ontology Evaluation and Validation[END_REF], [START_REF] Gómez-Pérez | Ontology Evaluation[END_REF], other authors proposed to evaluate ontologies depending on the final (re)use of them (Suárez-Figueroa, 2010), others have proposed quality models based on features, criteria and metrics [START_REF] Burton-Jones | A Semiotic Metrics Suite for Assessing the Quality of Ontologies[END_REF], [START_REF] Djedidi | Onto-Evoal an Ontology Evolution Approach Guided by Pattern Modelling and Quality Evaluation[END_REF], and in recent times methods for pattern-based evaluation have also emerged [START_REF] Presutti | NeOn D2.5.1: A Library of Ontology Design Patterns: reusable solutions for collaborative design of networked ontologies[END_REF]. A summary of guidelines and specific techniques for ontology evaluation can be found on [START_REF] Sabou | Ontology (Network) Evaluation. Ontology Engineering in a Networked World[END_REF]. Despite vast amounts of frameworks, criteria, and methods, ontology evaluation is still largely neglected by developers and practitioners. The result is many applications using ontologies following only minimal evaluation with an ontology editor, involving, at most, a syntax checking or reasoning test. Also, ontology practitioners could feel overwhelmed looking for the information required by ontology evaluation methods, and then, to give up the activity. That problem could stem from the time-consuming and tedious nature of evaluating the quality of an ontology. To alleviate such a dull task technological support that automate as many steps involved in ontology evaluation as possible have emerged (ODEClean and ODEval [START_REF] Corcho | ODEval: a Tool for Evaluating RDF(S), DAML+OIL, and OWL Concept Taxonomies[END_REF], XDTools plug-in for NeOn Toolkit and OntoCheck plug-in for Protégé, and MoKi [START_REF] Pammer | PhD Thesis: Automatic Support for Ontology Evaluation Review of Entailed Statements and Assertional Effects for OWL Ontologies[END_REF] ).

One of the crucial issues in ontology evaluation is the identification of anomalies or bad practices in the ontologies. Different research works have been focused on establishing sets of common errors [START_REF] Rector | Owl pizzas: Practical experience of teaching owl-dl: Common errors and common patterns[END_REF], [START_REF] Poveda | A Double Classification of Common Pitfalls in Ontologies[END_REF]. The ontology pitfalls catalogue presented in [START_REF] Poveda | A Double Classification of Common Pitfalls in Ontologies[END_REF]is being maintained and improved and it is available on-line. Such a version consists on the 29 pitfalls. In addition, such pitfalls can be checked using OOPS! [START_REF] Poveda | Validating Ontologies with OOPS![END_REF], is a web-based tool, independent of any ontology development environment, for detecting potential pitfalls that could lead to modelling errors. This tool is intended to help ontology developers during the ontology validation activity , which can be divided into diagnosis and repair. Currently, OOPS! provides mechanisms to automatically detect as many pitfalls as possible, thus helps developers in the diagnosis activity. In the near future OOPS! will include also methodological guidelines for repairing the detected pitfalls. We refer the reader to the OOPS! Site for a complete explanation of each pitfall.

ONTOLOGY-BASED INFORMATION RETRIEVAL SYSTEMS

The classical Information Retrieval task consists in, given a user request (usually in natural language) q and a collection of text documents D, retrieving a subset R, |R| << |D| of documents that are relevant with respect to the information need expressed by the user request. IR systems are usually composed of the following components: an indexing module, which process the collection of documents to transform each document in a representation stored in a way that allows to search the collection efficiently a search module, which transforms the natural language query in the same way and calculates the score for each document with respect to the query An ontology-based IR system may use the knowledge included in the ontology in the indexing module, to expand the index with information that otherwise could remain implicit in the text (for instance, extending the information that "car is a vehicle"), in the search module, to expand the query in the same way, or in both. In the first two cases we talk, respectively, about index and query expansion. In order to carry out an expansion of this kind, it is necessary to map a concept to the terms that are supposed to denote the concept. In many cases, the concept name is also the term that denotes the concept; in other cases, terms are stored in the ontology or in different structures. The process to map a term in a text to the corresponding concept in the ontology is called annotation.

Since no ontology-based IR systems are currently publicly distributed, to perform our analysis we selected the following 12 ontologybased information retrieval systems from the stateof-the-art. The choice was determined by the level of detail provided for the description of the system. We have studied how they work and identified common features.

A. Castells et al. [START_REF] Castells | An adaptation of the Vector-Space Model for Ontology-Based Information Retrieval[END_REF] use an ontology structure very similar in principle to the one used in TextViz. Document annotations are stored together with concepts, but terms are not modelled as concepts. Concept labels contain the terms that are used in the annotation phase. The query is translated into a RDQL query that is run on the ontology to retrieve the relevant documents. The user is allowed to specify weights on concepts of his choice at the query formulation time. [START_REF] Kiryakov | Semantic Annotation, indexing and Retrieval[END_REF]. This system focuses on Named Entities (NE), that is, people, organizations, places, etc. The ontology contains, for each entity, a link to its most specific class (for instance, "Arabian Sea" is an instance of the "Sea" class). The entities are identified thanks to patternmatching grammars. Lucene3 is used to store the entities IDs together with the document. Entities in the queries are also converted to their respective IDs, therefore allowing to resolve cases in which an entity may have different names. [START_REF] Ciorascu | knOWLer -Ontological Support for Information Retrieval Systems[END_REF]. This system uses three different OWL ontologies: the first one corresponds to the WordNet4 ontology, where synsets have been mapped to concepts and the WordNet relationships to OWL properties. A second OWL ontology contains the terms related to each concept (terms are represented using their stemmed form). The last ontology is used to represent the documents, extracted from the Wall Street Journal corpus. This last ontology actually serves as index since the document is represented using the concepts from the other two ontologies. Queries are transformed in logical forms which are used to filter the relevant documents. [START_REF] Bhagdev | Hybrid Search : Effectively Combining Keywords and Semantic Searches[END_REF]. This system was developed to search technical documentation about jet engines. It uses two indexes: a standard keyword-based index, and a triple store where triples are of the form <subject, relation, object>. The search module extracts concepts to build triples that are translated into SPARQL5 queries. The words appearing in the original query that cannot be mapped into concepts are sent to a traditional information retrieval system. The final result is obtained ranking documents using the traditional approach and filtering the relevant ones by means of the SPARQL query results. E. Liu et al. [START_REF] Liu | Semiautomatic Annotation System for OWL-based Semantic Search[END_REF]. do not use an ontology to carry out query or index expansion; they instead use the ontology as an index, storing terms, documents as concepts and the occurrence relationship as a property connecting terms and documents. They rely on OWL to model the ontology.

B. KIM

C. knOWLer

D. K-Search

F. MELISA (Abasolo and Gomez, 2000). This system uses a medical ontology where concepts correspond to MeSH6 terms. They expand queries using the medical ontology and the results are presented to the user to receive an additional feedback. Finally, the expanded query is re-sent to the search engine to present the final search results. [START_REF] Shah | Information Retrieval On the Semantic Web[END_REF]. This system is tailored to work on Web documents, especially news documents. The ontology contains an event taxonomy (sport event, movie show event, etc.) with spatio/temporal concepts that are connected to event concepts in order to establish the relationships between an event and where and when it took place. The extraction of events from free text is carried out using an annotation tool named AeroText. The document index is expanded with the annotated concepts and relationships (triples subject-relationobject). At search phase, the queries are converted in triples which are searched into the index. [START_REF] Pruski | Adaptive Ontology-Based Web Information Retrieval: The TARGET Framework[END_REF]. This system is a web search engine that is based on OWL primitives, enriched with the meronymy and antonymy relations. An ontology is used to store concepts about a specific domain. The concepts contained in queries are expanded using the concepts that are directly connected to them in the ontology.

G. OWLIR

H. TARGET

The query and the results of the Web search are transformed in graphs and a score is assigned to the top 100 retrieved pages, as a result of a graph similarity calculation. [START_REF] Bannour | Une plateforme opensource de recherche d'information sémantique[END_REF]. This is a Terrier7 extension that allows, given an ontology and a terminology associated to this ontology, to index and retrieve documents using concepts as index terms. Documents weights are calculated using a concept-based version of the wellknown tf.idf weighting scheme. The ontology is used to compute similarity values between concepts, by taking into account the hierarchical relationships between concepts.

I Terrier-SIR

J. Textpresso [START_REF] Müller | Textpresso: An Ontology-based Information Retrieval and Extraction System for biological Literature[END_REF]. This system uses an ontology of biological concepts (e.g., gene, allele, cell, etc.) and relations connecting them (association, regulation, etc.) to expand the index and the query. In order to identify concepts in text, regular expressions are used to find the terms associated to each concept. The concepts in the ontology are structured in "categories" and "subcategories", thus a retaining a (shallow) hierarchical structure. Queries can be expanded with more generic or specific concepts, according to the user needs. [START_REF] Reymonet | Ontologies et Recherche d'Information : une application au diagnostic automobile[END_REF]. In TextViz, terms denoting concepts are stored in the same OWL ontology containing the concept themselves (terms are modelled as concepts). The ontology is also used for indexing, to store the concept instances identified in documents. Document and queries are annotated using term labels, then a similarity is calculated between document and query instances, for each document, exploiting hierarchies, using a concept similarity formula named Proxigénéa. In a test scenario, the score was also modified depending on the presence or not in the document of a relation expressed in the query, but in general the weighting scheme proposed takes into account only concepts. An important factor seems to be how terms (keywords representing the ontology concepts) are processed. Some systems consider concept names as terms, others separate terms from concepts and in this second case, terms may be also stored in the ontology as concepts of a different class. The ontology itself may or may not be used as an index. In the affirmative case, queries may be transformed in a language such as SPARQL. Some systems may use or not the taxonomic information (is-a relationship) to enrich queries (query expansion), documents (index expansion) or both, or to calculate concept similarity. Other relations (not OWL primitives) may also be used by the system.

K. TextViz

Here we present first our analysis of the potential effect of each pitfall on the results of an ontologybased IR system, on the basis of the description provided by authors. We remember that IR systems are usually evaluated using precision (number of relevant document retrieved divided by the number of retrieved documents) and recall (number of relevant documents retrieved divided by the number of relevant documents in the collection). Secondly, we show the qualitative analysis of the impact the pitfalls in the OOPS! catalogue could have in the 12 ontology-based IR systems described in Section 3.

P1. Creating polysemous elements:

if the concept name is used to annotate the text, this pitfall would imply having ambiguous annotations, with a possible decrease in the precision.

P2. Creating synonyms as classes: if the system exploits hierarchical information, or calculates distances between concepts to determine a similarity value, this pitfall may affect precision.

P3. Creating the relationship "is" instead of using rdfs:subClassOf, rdf:type or owl:sameAs: if a system exploits hierarchical information, the concepts that are connected using this re-implementation of an OWL primitive may actually never be taken into account, affecting both precision and recall.

P4. Creating unconnected ontology elements: the appearance of this pitfall in the ontology would affect both precision and recall, meaning that some ontology elements could not be reached.

P5. Defining wrong inverse relationships: this pitfall would affect precision if the system exploits property features, such as inverse.

P6. Including cycles in the hierarchy: having a cycle between classes in one of the ontology hierarchies would imply that a system that exploits hierarchies in a recursive way could not finish its process.

P7. Merging different concepts in the same class:

if the merged concepts should have different parents, the appearance of this pitfall would affect the precision of the system. P8. Missing annotations: if the system uses labels and/or comments to carry out some tasks, the pitfall may affect the precision and recall of the system.

P9. Missing basic information: this pitfall may indicate that the information included in the ontology is not complete, affecting recall and/or precision. However, the ontologies used by the analysed systems do not seem to use ORSD.

P10. Missing disjointness: the analysed systems do not use disjoint axioms. The pitfall could affect precision if a system can take into account this information.

P11. Missing domain or range in properties: if a system exploits relationships other than "is-a", the appearance of this pitfall in the ontology would affect its precision.

P12. Missing equivalent properties: this pitfall may cause same concepts to have different parents. Therefore, if a system exploits hierarchical information, it may affect its precision and recall.

P13. Missing inverse relationships: this pitfall would affect precision if the system is able to exploit property features, such as inverse.

P14.

Misusing owl:allValuesFrom: currently, the appearance of this pitfall in the ontology does not affect in any sense. This pitfall may affect if the system exploits more language primitives. P15. Misusing "not some" and "some not": currently, the appearance of this pitfall in the ontology does not affect in any sense. This pitfall may affect if the system exploits more language primitives.

P16. Misusing primitive and defined classes:

currently, the appearance of this pitfall in the ontology does not affect in any sense. This pitfall may affect if the system exploits more language primitives. P17. Specializing too much a hierarchy: in most analysed systems, this is not perceived as a pitfall. Many systems model instances directly into the ontology. However, in some cases, when the individual is not really an instance of a concept but it is connected to the concept by means of a relation, this pitfall may indicate an error in the instance creation.

P18. Specifying too much the domain or the range: if relationships other than "is-a" are used, some relations may be missed due to this pitfall. Therefore, precision could be affected.

P19. Swapping intersection and union: if relationships other than "is-a" are used, some relations may be missed due to this pitfall. Therefore, precision could be affected. P20. Misusing ontology annotations: systems that exploits annotation properties to operate (for instance, TextViz) may be affected by this pitfall.

P21. Using a miscellaneous class: if a concept is not used, it should not appear. This pitfall may affect systems if the miscellaneous concept can be actually instantiated, leading to a decrease in precision.

P22. Using different naming criteria in the ontology: this pitfall may affect systems that use concept names in the annotation process. Using concepts with names that do not usually occur in the text may compromise their correct annotation, causing a deterioration in both precision and recall.

P23. Using incorrectly ontology elements: the appearance of this pitfall would affect depending on the modelling decisions (classes or properties). In ISCO, for instance, relations are modelled as concepts.

P24. Using recursive definition: definitions should not affect the IR process in any way.

P25. Defining a relationship inverse to itself: currently, the appearance of this pitfall does not affect any of the analysed systems. This pitfall would affect if the system exploits property features, such as inverse and symmetric.

P26. Defining inverse relationships for a symmetric one: currently, the appearance of this pitfall does not affect any of the analysed systems. These pitfalls would affect if the system exploits property features, such as inverse and symmetric.

P27. Defining wrong equivalent relationships: if a system uses relationships and OWL primitives, the appearance of this pitfall in the ontology would affect to the precision.

P28. Defining wrong symmetric relationships:

currently, the appearance of this pitfall does not affect any of the analysed systems. This pitfall would affect if the system exploits property features, such as inverse and symmetric.

P29. Defining wrong transitive relationships: if a system exploits the transitive property in relationships, the pitfall may affect its precision.

P30. Missing equivalent classes: this pitfall may cause same concepts to have different parents. Therefore, if a system exploits hierarchical information, it may affect its precision and recall.

Table 1 provides an overview of how every pitfall may or not affect each of the analysed systems.

CONCLUSIONS

We carried out a survey of existing state-of-the-art ontology-based information retrieval systems with respect to the pitfalls listed in the OOPS! catalogue. Our analysis shows that indeed OOPS! may prove useful to the developers of ontology-based IR systems in order to verify the quality of the ontology they use in their systems and prevent errors. Our analysis highlights also the fact that most of current available systems do not use some advanced features (especially with respect to relationships) that are provided by the OWL language. It is difficult to say whether this issue derives from the fact that developers ignore the existence of these features, or whether it is consequence of the state of the art of the available Natural Language Processing tools.We hope that this work will be viewed as an incentive for people working on ontology-based IR systems to: make their systems available for comparative testings; get used to adopt existing standards; evaluate their ontologies with an existing tool like OOPS!, in order to benefit of having some degree of quality in such ontologies. As a further work, we plan to carry out an evaluation of the speculated effects on a new version of the TextViz system which takes into account relations in a more advanced way than TextViz. This new version of TextViz is being completed and should be available soon. In order to carry out such evaluation, we will have to produce a test environment with different ontology benchmarks that include different combinations of pitfalls. Thanks to the results of this study, we are also planning to sketch some advices to help developers of ontology-based information retrieval systems to avoid pitfalls that may prevent their systems from working properly or deteriorate their performance.

http://www.oeg-upm.net/oops

http://www.oeg-upm.net/oops/catalogue.jsp

http://lucene.apache.org

http://wordnet.princeton.edu

http://www.w3.org/TR/rdf-sparql-query/

http://www.nlm.nih.gov/mesh/

http://terrier.org/

ACKNOWLEDGEMENTS

This work has been partially supported by (a) the Spanish projects BabelData (TIN2010-17550) and BuscaMedia (CENIT 2009-1026), and (b) the Post-Doctoral Exchange Programme of the French-Spanish Laboratory for Advanced Studies in Information, Representation and Processing (LIRP Associated European Laboratory (LEA)), and (c) the EFL Labex project.

Table 1. How pitfalls may affect each of the analysed systems 8 . Black: pitfall may have a negative effect on the system. Gray: pitfall could affect the system if it was designed to take into account a specific feature. White: pitfall has no impact in the system.

Sys

(1) P19

(1) P20 (2) P21

(1) P22 P23

(1)

8 Notes: (1) may affect if OWL is used; (2) only TextViz and Castells use labels;

(3) may affect if ORSD is used; (4) may affect if system exploits some language primitives that are not currently exploited; (5) may affect if the system exploits property features; and (6) the paper did not provide enough insights to determine whether the pitfall may affect or not.


YaSemIR: Yet Another Semantic Information Retrieval System

Davide Buscaldi1 and Haïfa Zargayouna 1 LIPN, Université Paris XIII, F-93430 Villetaneuse {davide.buscaldi,haifa.zargayouna}@lipn.univ-paris13.fr Abstract. In this paper we present YaSemIR, a free open-source Semantic Information Retrieval system based on Lucene. It takes one or more ontologies in OWL format and a terminology associated to each ontology in SKOS format to index semantically a text collection. The terminology is used to annotate concepts in documents, while the ontology is used to exploit the taxonomic information in order to expand these with their subsumers. YaSemIR is a flexible system that may be configured to work with different ontologies, on various types of documents.

Context

The number of Ontology-based IR systems has been continuously growing in the last years, boosted both by the Semantic Web (SW) and Information Retrieval (IR) research communities. Unfortunately, the proposed solutions are heterogeneous in methods, test collection, scope and standards adopted [START_REF] Scheir | Information retrieval on the semantic web -does it exist? In Lernen, Wissensentdeckung und Adaptivitt[END_REF]. Various criteria have been proposed to classify these systems [START_REF] Fernndez | Semantically enhanced information retrieval: An ontology-based approach[END_REF]; among these, one is particularly discriminant with respect to the SW or IR nature of a system: whether a system is oriented to data or document retrieval (that is the case of our system).

Independently from the choice of a SW or IR perspective, these systems made some hypothesis: (i) domain ontologies are available, (ii) these ontologies are usable both to annotate documents and to retrieve them.

As the semantic web is continuously expanding and thanks to the effort of standardization, there are more and more ontologies available online. They can be selected using web interfaces such as Watson 1 . The second hypothesis means that the available ontologies are large and explicit enough to cover the vocabulary used in documents. Although we can affirm that the first hypothesis is realistic, the second hypothesis is more difficult to realize in practice. The usability of ontologies is related to their lexical information and to the performance of the annotation process (which links documents to ontologies). Therefore, it is difficult to assess the performance of an Ontology-based IR Systems independently from the coverage allowed by the ontologies it uses.

In literature, the coverage problem is addressed by enriching the knowledge base during the annotation process [START_REF] Kiryakov | Semantic annotation, indexing, and retrieval[END_REF][START_REF] Reymonet | Ontologies et recherche d'information : une application au diagnostic automobile[END_REF], or proposing to combine both keyword and ontology-based search. Works that propose such a combination can be divided in two categories depending on the combination method: composition or merge. [START_REF] Rocha | A hybrid approach for searching in the semantic web[END_REF] propose to apply a spread activation algorithm on a graph build from the documents retrieved by a traditional search engine. [START_REF] Finin | Information retrieval and the semantic web[END_REF] propose three strategies that perform a semantic search and reasoning and then rank results with a web search engine. More recent works propose to merge the results of the two searches, [START_REF] Bhagdev | Hybrid Search: Effectively Combining Keywords and Semantic Searches[END_REF] propose to return the intersection of sets of documents retrieved by keyword matching and semantic matching (retrieved from an RDF store). Our work is situated closer to the IR perspective and belongs to the last category of hybrid search.

The integration of multiple ontologies is also promising to address the coverage problem, to our knowledge, only PowerAqua system [START_REF] Lopez | Poweraqua: Supporting users in querying and exploring the semantic web[END_REF] enables to take into account several ontologies. However, PowerAqua is designed with a question answering perspective.

The quantity of models and system proposed in literature is not proportional to the quantity of tools that have been produced and, notably, distributed as free and open source software. Many studies were also tailored for a specific, domainclosed task [START_REF] Muller | Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature[END_REF][START_REF] Bhagdev | Hybrid Search: Effectively Combining Keywords and Semantic Searches[END_REF] and it is not clear if the proposed approach would scale. We would like to propose extensions to well-known free, open source IR systems to cope with semantic search. The advantage of such an open source systems is to isolate different semantic components and thus enable to set up comparative evaluation of systems (relying on the same annotation component for example).

In this paper, we introduce an open-source Ontology-based IR system, based on Lucene2 , which takes advantage from standard SW formalisms: OWL and SKOS 3 . YaSemIR is a flexible system that may be configured to work with different ontologies, on various types of documents. A development version is available at the address 4 . A stable, complete and easily configurable version is currently being tested.

System Architecture

The key components of YaSemIR are:

-An annotation module, which identifies the occurrence of an ontology concept in the documents of the collection or the input query;

-A standard indexing module, based on Lucene. This module creates a standard index, based on Lucene's vector space model implementation;

-A semantic indexing module, which takes the annotations produced by the annotation module, expands them using the ontology and stores the expanded annotations in a separate semantic index (one separate index for each ontology used); -A ranking module, which takes the scores calculated using keywords and combines them with the scores calculated using the concepts;

-A knowledge battery (KB), provided by the user, composed by one or more ontologies (in OWL) and a terminology file (in SKOS format) for each ontology, which contains the concept labels, that is, the lexical denotation of concepts: they trigger the detection of a concept in a text. In the case of using multiple ontologies, they could be different conceptualization of the same domain, to improve concept coverage, or refer to different domains, to allow to better represent documents that may pertain to more than one domain.

Let us examine how these components are used in the indexing and the search phase [START_REF] Bernstein | Simpack: A generic java library for similarity measures in ontologies[END_REF].

Indexing After the knowledge battery has been loaded, the first step carried out by YaSemIR is to create a label index which maps the stemmed labels into the corresponding concepts. If no terminology were provided, the system would attempt to extract the labels from the concepts URIs of the respective ontology.

After the creation of the label index, the actual indexing is carried out. During this phase, the semantic indexing module parses the documents. Each documents D is passed to the annotation module which fetches the index labels and returns a set of concepts C D = {c 1 , . . . , c n }. This set of document concepts is expanded with their ancestors in the ontology A D = {c n+1 , . . . c m }. Therefore, each document is annotated with the set of concepts C D ∪ A D which enable to map with general queries' concepts even if they do no occur in the document. The document annotation is stored in the semantic index.

The complete text of the document is indexed with the standard vector model, using the Lucene standard vector model. We chose to maintain a standard, key-word based index in order to overcome the KB coverage problems.

Search The search phase is triggered by the user with a natural language query. This query is analyzed by the annotation module to extract a set of concepts Q = {c 1 , . . . , c k }, following the same procedure for the document annotation. This representation is searched in the semantic index, while the base query is searched in the standard index. The keyword-based search relies on Lucene mapping and scoring 5 . The semantic search first returns the list of documents that contain at least one query concept (or a descendant of a query concept). The aim of the semantic score is to return first documents that contain concepts that are the most similar to the query concepts. In our initial evaluation of the system, we used a simple semantic score based on the conceptual similarity score defined by [START_REF] Wu | Verb semantics and lexical selection[END_REF]: Other conceptual similarity measures exist in the literature [START_REF] Bernstein | Simpack: A generic java library for similarity measures in ontologies[END_REF] and they can be easily integrated to YaSemIR. The semantic score is calculated as follows:

s(c i , c j ) = 2 • depth(lca ij ) depth(c i ) + depth(c j ) . (1)
SS O B (Q, D) = ci∈Q w(r(c i )) max cj ∈C D s(c i , c j ) ci∈Q w(r(c i )) ; ∀c i , c j ∈ O B (2)
Where r(c i ) is the top-level concept (a direct child of an ontology root) for c i , and w(r(c i )) a weight assigned to model the relative importance of a sub-hierarchy over another. This is useful in order to give more importance to concepts appearing in one ontology than another. It can also be useful if the ontology contains different sub-domains. In YaSemIR, w can be specified in one of the following ways: Moreover, a weight can be assigned by the user to each ontology (or to subcategories). Once a semantic score is calculated for each of the ontologies in the KB SS O1 (Q, D), . . . SS O B (Q, D), and the standard score score Luc (Q, D) is obtained from the standard index. These scores are combined into a single score using the CombANZ strategy [START_REF] Shaw | Combination of Multiple Searches[END_REF]. CombANZ is defined as the average of all non-zero scores, while CombMNZ is defined as the sum of all scores, multiplied by the number of non-zero scores. The CombANZ-calculated score is used to rank the documents which are returned to the user. The final score is as follows:

-w(c i ) = 1, ∀c i ∈ O 1 ∪ O 2 ∪ . . . ∪ O B (
score(Q, D) = i∈1..B SS Oi (Q, D) + score Luc (Q, D) m ( 3
)
Where B is the number of available ontologies and m the number of non-zero scores.

First Results and Perspectives

We presented a free, open-source semantic IR system based on Lucene, which exploits concept labels to annotate documents and queries. We carried out a preliminary evaluation on the OHSUMED test collection6 , using the BIKE ontology7 as KB. We carried out the experiments comparing a keyword-based Lucene baseline, the results obtained with the manual concept annotation included in the collection, and the results obtained with the automatic annotation based only on concept names. The MAP obtained with the manual annotation was 0.297, compared to 0.254 obtained with Lucene (baseline keywords-only) and 0.249 obtained with the automatic annotation. The results showed significant differences in precision depending on errors in the annotation of queries and documents, highlighting the importance of the annotation process in semantic IR. As further work, we plan to integrate state of the art automatic annotation tools when available such as those reported in [START_REF] Uren | Semantic annotation for knowledge management: Requirements and a survey of the state of the art[END_REF] and to experiment system scalablity by testing it with large knowledge bases such as dbpedia. We also plan to look for additional ontologies to test the system with multiple ontologies. [START_REF] Fernandez | Using trec for cross-comparison between classic ir and ontology-based search models at a web scale[END_REF] proposed 40 public ontologies covering a subset of TREC domains (WT10G documents) enriched by available knowledge bases associated with these ontologies. The integration of multiple ontologies proposed by YaSemIR can also be extended in order to work in a multilingual environment, taking advantage from the multilingual features of SKOS.

Fig. 1 .

 1 Fig. 1. Overview of the search process.

  All concepts in any of the ontologies of the KB have the same weight); -w(c i ) = df (c i )/N : frequency of concept c i in collection divided by the number of documents in the collection (frequent concepts are more important); -w(c i ) = -log(df (c i )/N ): the inverse document frequency of c i (rare concepts are more important).

http://watson.kmi.open.ac.uk/WatsonWUI/

http://lucene.apache.org/

http://www.w3.org/2004/02/skos/

https://github.com/dbuscaldi/YaSemIR

a cosine measure

http://ir.ohsu.edu/ohsumed/ohsumed.html

It is the MeSH ontology in OWL format developed by the Biomedical Knowledge Engineering Laboratory (BIKE

) at Seoul National University.

Acknowledgments

This work has been partially financed by the Labex EFL (ANR/CGI).