Introduction

In the context of generalized world-wide exuberance of housing markets, house prices trajectories and market dynamics before and after the great financial crisis have been highly different among European countries. To a certain extent, each country was involved in a specific "story", one could consider as a unique result of former developments and structural characteristics of national housing and finance systems. Common tendencies are nonetheless observable. So that, if a unique model of housing market developments is clearly out of reach, some kind of rationale can and should be identified.

Starting from the view that there are at least 3 distinctive "stories" to be told for in European housing markets, and that France and Sweden belong to the same "story", we shall investigate in more details what are the components of the "Nordic model" which, beyond those two countries, incorporate the whole of North-Western Europe.

Finally, section 3 addresses the analytical stake offered by the Northern resilient price regime. The continuous decline of interest rates, jointly with the increased maturity of loans and the liberalization of residential loans markets, with the consequent rise of mortgage debt, are certainly key factors to explain the upsurge of house prices. But those financial factors hardly give account of the acceleration of house price rises after 2003. The shortage argument is still less convincing.

What is suggested is that the "northern" price regime comes from structural specifics of housing markets, typical of mature urban housing systems, which do not fit with usual stories about housing markets dynamics, and clearly not with standard supply and demand interactions as stated in traditional equilibrium theories. An alternative way is suggested, consisting in exploring a "cumulative disequilibrium" hypothesis, within the framework of stock-flow-consistent (SFC) modelling.

In such a perspective, the path of prices moves is given by self-fulfilling estimates of future valuations of residential spaces, governed by locational behaviors of households. Any displacement in spatial preferences and/or socio-demographic structures can initiate sectorial mismatches between supply and demand, eventually degenerating in a general inflating disequilibrium, and break the link between income and savings flows and wealth accumulation in real estate.

Housing market regimes in Europe

Starting from the observation that the most stable housing markets have been those where the proportion of homeowners is less (namely in Germany, Switzerland and urban Austria, especially Vienna), while the highest volatility was observed where the rental market is strongly residualized (like in Spain, Iceland and some East European countries), some kind of relationship could be assumed between tenures and market volatility or exuberance. But such a relationship doesn't stand for a large range of countries, especially France and all Nordic countries, where housing markets have been highly exuberant despite the relative weight of the rental sector, including a large social rental stock, and no more for some Mediterranean countries (Italy, Malta, Portugal), where the proportion of homeowners is not significantly smaller than in Ireland or Spain, and housing markets much more stable.

1-1 Stable, volatile or just exuberant? Three typical house price dynamics

A short view to the price dynamics strongly suggest to distinguish 3 groups of countries: stable, exuberant, and volatile, according to their respective degrees of housing markets buoyancy:

-the most volatile markets are located in the "peripheries" of the EU: East and Central Europe (Baltic countries, Bulgaria, Hungary, Slovenia), extreme West (Ireland, Iceland) and some (but not all) Mediterranean countries (namely Cyprus, Greece and Spain) ; in those countries, the downturn of the market after 2007 was rapid and violent, most probably signalling a housing bubble ; -the extreme opposite is represented by the 3 "Germanic" countries, where house prices have recorded contained rises (less than 20% in Austria and Switzerland) or even smooth and continuous decline (Germany) from 1997 to 2007 ; those markets are experiencing substantial price increases since 2010 ; -a "Nordic" group of countries, including France and Malta, with Finland plus Scandinavian countries and Benelux, stands in an intermediate position with a mixture of exuberance and relative stability, with no evidence of any critical financial fragility. In those countries, housing markets were very resilient after 2007. The trend of house prices has been very similar (see figure 1-c) in all countries belonging to this group, and very different from what happened in peripheral countries (figure 1-a) and the small group of "Germanic" stable countries (figure 1-b), either before and after the world financial crisis. The overall increase in house prices ranged between 150% and 200%, twice more than I n the US during the same period. As was the case for the "volatile" group, an acceleration process is clearly at work after 2003.

Only 3 European countries do not easily enter in this classification: Italy and Portugal on one side, and the UK on the other side. Portugal (and Italy to a lesser extent) experienced limited rises (50% and 100% respectively) of house prices in the 1996-2007 decade, followed by a non calamitous downturn after 2008. And the UK appears as a "special case" (between volatility and exuberance): the same exuberance as the "volatile", but the same "resilience" as observed in the Nordic group. After a brief and severe credit crunch, housing markets rapidly recovered in 2010, especially in London and South-East England. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Tenures and housing market instability: no simple relationship

In the volatile group, strong fluctuations of house prices (increases over 200% in the 1996-2007 decade, and up to 350-400% in the cases if Iceland and Ireland, followed by sharp -and still going on -drops (more than 30%) after 2007) are associated with an overwhelming weigh of home ownership (80% and more). In the Baltic countries, and other Eastern countries (Bulgaria, Romania, …) the volatility of housing markets is still more impressive, with short periods of hyper buoyancy (1 to 3 years) followed by equally frenetic drops in house prices.

At the other extreme, in the Germanic countries, a majority of households are tenants 1 , and the rental market is highly regulated.

But on the whole the statistical link between homeownership prevalence and house price volatility is rather weak (R 2 =0.38)

In the "central" block of countries belonging to the "Nordic" group, there is no clear relationship between tenures and market buyoancy. Belgium and Luxembourg, where the rental sector is weak, and social housing very limited, have not been more buoyant than Denmark, the Netherlands and France whose rental sector is much more significant. Focus will be put in section 2 on the "Nordic" group and the UK.

Along with tenures and house prices trends, our market-based typology is consistent with a number of characteristics of housing finance and public policies. A strong tradition of public support to housing and a sustainable level of residential debt are common features in the Nordic group.

1 At least in Vienna and other big cities in the case of Austria. Our classification of European countries between peripheral / volatile, Germanic / stable and Nordic / exuberant, unless not totally contradictory to former proposals) in this volume may appear as a kind of oddity. The fact is that it is very difficult to concile policy-based differenciations (such as the liberal / social-democrat / corporatist trilogy developed by Esping-Andersen (….)) with the reality of complex stratified housing systems (marketized / socialized) and/or housing markets (dual/unitary (Kemeny 1995)) or static/dynamic ( (….)).

And some countries are definitely difficult to categorize, especially Italy, Portugal and the UK. The fact is that every national situation presents so many distinctive peculiar characteristics, that it is hardly possible to find a classification which fits for all analytical purposes. Our categorization is clearly market-oriented, but nonetheless different from those made by [START_REF] Van Der | Western European housing systems and the impact of the international financial crisis[END_REF] or by Dewilde and de Decker ( 2015).

Maybe the most surprising is to

Nordic mysteries

1) Resilience and persisting disconnection: After 2007, only 2 countries from the "Nordic" group have experienced serious drops in real house prices: Denmark and the Netherlands. But in the other countries (see figure 3), real prices have recovered their pre-crisis levels, and were still near their peak level of 2007 (or even above in the cases of Luxembourg, Norway and Sweden) at the end of 2014. Even in the Netherlands, where the decrease in prices has been most intense, the real price of houses did not fall beyond its 2000 level, a historical high.

Accordingly, in 2014 house prices were still disconnected from incomes and rents. Price to rent and price to income ratios remained at exceptionally high levels, without any sign of a return to normality. Price to rent ratios stay at their peak level of 2007-2008, about 40% above their long run mean value, and even a little higher in Norway and Luxembourg. In all countries, they are widely above their summits of the late 80s. In the same way, and except for Denmark and Finland, price to income ratios (see figure 5) were still in 2014 at their peak levels of 2007-2008, and in most cases above the preceding records of the late 80s. Such a persisting disconnection between prices and their "fundamentals" dismisses conventional economic wisdom which tells us that market (demand) prices are nothing more than the actual amount of future rents, to which the (cost) supply-price should equalize in the long run, so that if rents are not (unduely) controlled, the housing market would be selfregulated and efficient, through continuous adjustments of prices (in the short run) and the housing stock (in the long run).

2) The credit channel, and what else?

Of course, the first and massive explanation of the disconnection between prices and rent as between prices and incomes is the continuous decline in mortgage interest rates since the mid 90's, leading to unprecedented lows. Along with longer maturities of loans, and good macroeconomic conditions, it explains the recovery after the real estate crises of the 90's, but not the acceleration of the late 2000's. As can be seen on figures 6-a to 6-f, house prices and the volume of residential loans evolved in line with another with a correlation of 0.80 to 0.95, but during the years 2003 to 2008 the rise in prices is accelerating relatively to the rise of debt. The relationship between tenures and house price dynamics is indirect. It goes from home ownership to residential debt (R2=0.42), and from debt to prices (R2=0.63): the declining interest rates (and other favorable credit conditions) allow higher borrowing, which in turn increases biding capacity, thus effective demand, and finally drives prices up. But this "financial accelerator" of prices is not specific of the "northern" countries, the cost of credit has been lowered in the same proportions in Germany, Austria and Switzerland.

198… 198… 198… 198… 198… 198… 198… 198… 198… 198… 199… 199… 199… 199… 199… 199… 199… 199… 199… 199… 200… 200… 200… 200… 200… 200… 200… 200… 200… 200… 201… 201… 201… 201… 201…
The degree of housing finance deregulation is not either a key to explain high volatilities of house prices. According to IMF and ECB, the development of mortgage markets was rather limited in Ireland and Spain (see MCD indicator in table 2 below), in comparison with Denmark, Sweden and The Netherlands. Typical maturities and typical LTVs are not either discriminant. Finally, securitization was also very limited in Southern Europe. Thus, the frontier between "sensible" exuberance (in North-Western Europe) and "irrational" volatility in Eastern and Southern countries cannot be drawn through housing finance criterias. The only cases where there is a clear relationship between mortgage loans deregulation and post crisis developments on housing markets are Denmark and the Netherlands. Both exhibit high levels of housing debt and a strong development of mortgage credit.

Generally speaking, the debt level and its relative growth (see table 2) are the best criterias for identifying virtual financial fragility. Belgium, Finland and France appear as the most robust from this viewpoint. And of course Austria and Germany, but also Italy. On the whole, as the "financial accelerator" was at work in all European countries, it cannot explain why and how the buoyancy of housing markets was not followed by stagnation or depression after the Great Contraction following the world financial crisis. What are the factors explaining such a stabilization at so high levels of house prices?

The relaxation of monetary policies, giving the way to unprecedented low interest rates, is only part of the story. And the role of low interest rates is somewhat paradoxical; on one side, they raise the purchasing power of households; on the other side, as they contribute to drive up prices, they reduce the bidding capacity of future buyers. If interest rates were to decline -and they will do it one day or another ! -the effect on house prices would be immediate, but as regards households, the decline in prices would compensate for the rising cost of credit, so that the net effect on effective demand might remain uncertain.

As regards "volatile" countries, it is not so hard to find a story, which has a lot of antecedents in economic history; it is a story of "overtrading", a term used by Adam Smith to qualify the Ayr Bank collapse of 1772, and the crisis which followed; and it is a text-book classical real estate crisis: developers and the building industry overreact to a market recovery, subsequent to some "fundamental" shocks on demand ; this story is in line with what happened in Spain, Iceland, the Baltic countries and in a general way in the peripheries of Europe. But it doesn't fit with what succeeded in our "Nordic" group. Even in the US, the story was somewhat different: the so-called subprime crisis was the produce of specific financial developments, and uncontrolled growth of risky bank loans, not so much a downfall of overproduction.

The Nordic housing market regime: in search of a theory

There is little doubt that the initial impulse to house prices was given by the general economic recovery of the late 90s, during the net.economy boom. Economic growth, cheaper credit and greater incomes give a reasonably convincing account of what happened on housing markets until 2002. But why was the housing boom so long and so strong? As explained in section 1, housing finance conditions are a good part of the story, but not the whole. Usual explanations broadly consist in 2 lines of argument: one is the "supply shortage argument", the other is the "bubble hypothesis".

Our purpose in this section is first to insist on some characteristics of mature housing system, as those of old European countries, which can enlighten the recent working of their housing markets, and second to suggest an unexplored way for theorizing such "mature housing markets".

The quantity puzzle

1)

The shortage argument dubious Nor exuberance nor resilience can be explained in "Nordic" countries as the product of a persistant shortage of new constructions. As shown in figure X below, the shortage argument must deserve attention in Denmark, Sweden and the UK, where construction rates effectively remained very low, but doesn't stand for Norway, France, Finland and Belgium, where they were much higher. And there is clearly no definite relationship between price movements and the levels of new construction. In Germany, the rate of construction felt dramatically after 1996, and prices were stable, while construction activity was very dynamic in Ireland and Spain, but prices exploded. Demographic trends in European countries do not justify construction rates over 1%.

2)

Transactions and constructions In mature housing systems, housing markets are first of all markets for existing homes. In UK and the "Nordic" group, the market for new houses represents no more than 15% to 30% of total exchanges. The number of transactions on existing homes for one completion of new built home is over 5 in Sweden and UK. Transactions represent between 2 and 3 times Considering that a very small part of new built dwellings are offered to buy-to-let investors, this offers a rationale for the disconnection between prices and rents. The traditional line of reasoning doesn't stand in a world where, as is the case in all European countries, except the Germanic, the rental sector represents such a small part of new supply that it is no more price-maker, and is eventually totally residualized.

3)

Prices and quantities As was already the case in the 80s, it has been observed during the pre-crisis decade that increasing prices did not dry the market. As shown on figures 10-a to 10-f, the volume of transactions remained stable or even increased during the boom. There is either no significant relationship (France, Finland, UK), or a positive correlation (Belgium, Sweden) between prices and volumes. This co-cyclicity of prices and quantities is another puzzling characteristic of markets for existing homes.

The reason why is that on property markets which are dominated by existing homes, a majority of transactions are made between home owners. Outsiders are a dominated minority of transactors. The majority of transactors are sellers-buyers, so that increasing prices do not substantially affect their purchasing power. They only have to bridge the gap between the two values. Their increased housing wealth allows them both to offer higher bid prices and to get greater loans from the bank. The market is then driven by assets values, not by incomes trends. Growing financial wealth can eventually reinforce this dependency of effective money demand to existing stocks. Supply and demand are not independent forces, as in the usual representation of markets. Such an autonomy of demand could explain how and why price to rent and price to income ratios have reached such unusual levels, without any clear tendency to return to "normal" long run values after 2007. And it doesn't imply that markets are involved in a speculative bubble, at least in the usual meaning of the word. The reality of contemporary housing markets is that there is no "fundamental value" which could act as an attractor for the market price. The general shift of housing policies towards more favorable to home property and more demand-oriented, thus less committed to supply stimulus, especially for social housing has reinforced preferences for home ownership.

Beyond those common features, national housing systems have a number of distinctive characteristics. For instance, France (like Germany) has a more robust financial system than many other countries, but probably (this is a matter for further inquiry) a greater degree of spatial segregation, and a more rigid separation between rental and property segments than Scandinavian countries. As we have seen price-elasticities are very different from one country to another: very low in Sweden and the UK, notably higher in Belgium and France.

Thus the same exuberance of housing markets may be the result of different national cocktails.

A new price regime

On markets dominated by the property department (and not the rental department) and transactions on existing homes, in a context of permanently low levels of construction, rents are no more price drivers and production costs are no more long run « attractors ».

The structure of contemporary housing markets has strong impact on the market process of prices formation: the price of new housing now depends upon the price of existing housing, through the price of land, and expectations tend to dominate the process of price formation.

The main determinant of prices for existing homes is the amount of accumulated saving which can be devoted to transactions. But for the majority of buyers, this amount depends … upon the current price of existing homes. Which is highly dependent of the subjective future value attributed by transactors to a given place. Here the localized character of housing plays a decisive role. Owning a home is not only owning a living place, but also obtaining rights on a definite piece of space. The present valuation of a place depends upon the dominant (conventional) opinion of what will be its future value. There is no objective criteria which can guide and discipline the "urban animal spirits" governing such expectations, which are speculative in the proper meaning, but not in the sense that they are "independent" of fundamentals which do not exist. Estimating future rents is of no help, because they will be determined by … future values of something which is not a commodity with a cost of production, but the price of land, in which the value of the place is objectivized, but dependant upon subjective valuations and expectations.

.3 Segmented housing markets and the disequilibrium hypothesis

What generally puts in motion a housing boom, subject to favorable macroeconomic conditions, is a displacement in demand which causes a mismatch with the existing stock. For any reason (demographic, like a migration shock or economic, like a change in the distribution of incomes, or in residential behaviors) the structure of the housing stock is no more (or less than before) adapted to the new structure of demands. Which creates sectorial (and located) disequilibriums which will in turn originate crowding out and lock-in effects and may destabilize the whole market system.

We are thus driven to a vision of housing markets as a set of sub-markets in permanent interaction with one another. In such an unending process, markets never reach a stable equilibrium.

1) Rationale for the disequilibrium hypothesis

As advocated by Rothenberg, Galster, Butler and Pitkin (The Maze of Urban Housing Markets, 1991) the heterogeneity and durability of housing result in a high degree of market segmentation. The housing system is composed of different sub-markets, between which substituability is narrowly limited. Especially if we consider a third dimension of housing: its spatial immobility, which adds a lot to its complexity. Adjusting the market doesn't mean equalizing demand and supply, but entering in an unending matching process between houses and households of different types. Two sets of reasons make the case for disequilibrium:

-In the short run (market period), the heterogeinity of the housing stock and the rigidity of supply imply information and transaction costs which makes the transaction process very complex, and generates mismatches; -In the medium run (investment/production period), time lags in production and the viscosity of the urban structure (not only the housing stock. Because all individuals are not reallocated at each moment of time, urban space is not marginalist.

2) What should be adressed?

The first stake for economic theorists is to offer a realistic vision of market processes in a world where a full equilibrium is not instantaneously obtained. If individuals are not pure machines, demand and supply functions cannot be « walrasian » ones. Effective demands (as opposed to notional demand) cannot be deduced from simple utility maximization under budget constraint. Additional constraints must be introduced on liquidity and on disposable supply. On housing markets, "supply" doesn't mean only quantities offered, but also spatial disposability. Housing units of definite types must be affordable at the right place in the good proportion.

Then, if transactions are going on in disequilibrium, the final result is uncertain. The long run doesn't exist as an independent analytical level. Because the target will move during the adjustment process, there is no "natural" level of housing values. Path dependency effects are thus unavoidable. Rather than reaching a pre-definite equilibrium point, stability consists in keeping on a sustainable trajectory.

The next stake is to identify what have been the initial "displacements" which have put in motion the disequilibrium process. They may have two origins: in the structure of employment and/or households, or in spatial preferences and behaviors. Probably the two types of "displacements" have played a role in the 2000s. The most probable is the scarcity of free spaces for upper-middle class households (a "spatial-quantity" constraint) (deciles 6 to 8?).

European cities are almost universally involved in two processes: gentrification and urban sprawl. The former corresponds to what we called (see [START_REF] Tutin | From exuberance to resilience: French housing markets after the crisis[END_REF]) a « peripheric » market regime ruling cheap home ownership development for low income households on the urban fringe; this regime is credit led and sustained by public policies. The latter, which is at work in inner cities and first belts, is led by middle and upper-middle class households. It is an asset-led market regime self-sustained by capital gains. Gentrification comes from a lack of intermediate residential spaces, in societies where those middle class households represent a growing proportion of the employed population. It consists in refurbishing the existing housing stock in former popular neighborhoods, and thus in a restructuration of the whole hierarchy of residential spaces. This is probably the main initial displacement explaining the strength and duration of the housing boom.

The dominant regime is the "central" regime, run by gentrifiers, which is also the less fragile, being widely self-sustained by the very inflation of housing wealth. The upsurge of house prices comes from the top of the market, not from the bottom. In such an "asset-led" market regime, as opposed to an "income-led" regime, the dynamics of wealth accumulation is "driven by capital gains/losses on real and financial assets rather than the accumulation of active saving by households. Consequently, the relationship between the historical (aggregate) saving rates of net wealth across countries is often quite noisy" (HFCN 2013, p. 15), and house prices are governed by arbitrages on stocks, not by income and savings flows. Which urges for an analysis of market processes based upon stock-flows interactions, rather than instantaneous supply and demand confrontation.

3) The case for SFC modelling SFC (stock flow coherent) models have been developed in the last 2 decades as a postkeynesian alternative to DGSE (Dynamic Stochastic General Equilibrium) models; initially derived from Tobin (1969Tobin ( , 1982) ) and Minsky (1990), they have been implemented by Godley (1999), Lavoie (

) and Taylor (2004). The main differences with neoclassical general equilibrium relate to the representation of individuals, and the role of stocks: individual agents are represented by their balance sheets, and current flows of income and savings governed by valuations of stocks. The potential supply of assets consists in the whole accumulated stock. Of course, this is a pure virtuality, but the possibility that sellers are numerous and buyers very few, so that real estate becomes illiquid, is not a fantasy. Thus current flows are governed by stocks. This approach holds a nominalist view of the economy: liabilities and contacts (including labor contracts) are expressed in money terms (standard theory is outrageously realistic), and demand is nothing else than a monetary purchasing power present on the market. Economic theory is too much accustomed to think about demand as a "quantity" (in the case of housing, a number of dwelling units) when it should be considered, in a monetary economy, as a certain amount of money offered to the market. This old view of money demand was held by pre-classical economics: its best expression can be found in Cantillon's Essay SFC models are of special interest for contemporary housing markets, in « mature urban housing systems », where valuation of the housing stock and the outstanding stock of housing debt are the main triggers of house price dynamics.

Under the condition that it is sustained by the credit system, a wealth to price circle can be initialized by any displacement in the composition of population, housing preferences, income distribution or any factor affecting the matching between continuously evolving demands and a rigid stock of dwellings and city structure. Once engaged in a boom, home property markets operate as a self sustained circular row.

Conclusion

In this paper, we first identified three different housing market regimes (labelled as Germanic, Nordic and Peripheral), based on tenures, house prices dynamics, and different levels of debt burden and financial fragility. The so-called "Nordic" group of countries is typical of a new "price regime", characterized by a strong pre-crisis exuberance, in contrast with Germanic countries, and a remarkable post-crisis resilience, which contrasts with the violent burst of prices in the volatile group.

Once admitted that the "supply shortage argument", and the "bubble hypothesis" are both non convincing, it is necessary to identify what are the common characteristics between those "northern" countries to which this specific house price regime could be related. The clear dominance of transactions on existing homes, the positive correlation between prices and quantities, and the role of credit have been Finally, what is suggested is to consider north-european house prices as a case-study for disequilibrium analysis.

Figure

  Figure 1-a House prices trajectories in Europe "Volatile" group 1995-2013

  Figure 1-b House prices trajectories in Europe Germanic group 1995-2013

Figure 2

 2 Figure 1-c House prices trajectories in Europe "Nordic" group 1995-2013

  Figure 2 House price rise (1996-2007) and owner occupation rate

Figure

  Figure 3 Real house prices -1970-2014 -European resilient markets

FigureFigure 5

 5 Figure 4 Price to rent ratio for resilient countries 1980-2014 FRA GBR BEL DNK FIN LUX NLD NOR SWE Source: OECD Ratio 2010 = 100

FIGURES 6 -

 6 FIGURES 6-a to 6-f HOUSE PRICES AND RESIDENTIAL DEBT -RESILIENT COUNTRIES

  Figure 7-a Home ownership and housing debt per capita (% growth 1996-2007) IR

  Figure 8 Construction rates 1996-2013Representative countries

  Figure 9 Existing/new housing markets (Ratio = transactions on existing dwellings/new built homes)

TABLE 1 CHARACTERISTICS OF THE HOUSING SYSTEMS AND RESIDENTIAL LOAN MARKETS Country Home ownership Private rental Social rental Private rents Public support to ownership Mortgage markets Level of residential debt Rise of indebtness 1996-2007 House price dynamics

 1

	Germany	Minority	Important	Residual	Strong	Weak	Strongly	Low	No	Counterflow
					regulation		regulated
	Austria	Minority	Important	Important	Strong	Weak	Regulated	Very low	Slow	Very low
					regulation
	Switzerland	Minority	Majority	Small	Regulated	Weak		High	Strong	Low
	Belgium	Majority		Small				Moderate	Very strong	Exuberant,
										Strong
										resilience
	Denmark	Majority	Important	Important	Strong	Moderate	Deregulated	Very high	Very strong	Exuberant,
					regulation					Weak resilience
	Finland	Majority	Important	Important	De regulated Moderate	Re-regulated	Low	No	Exuberant,
										Strong
										resilience
	Norway	Invasive	Significant	Important		Strong		High	Limited	Exuberant
										Strong
										resilience
	Netherlands	Majority	Weak	Very	Regulated	Moderate	Deregulated	Very high	Very strong	Exuberant,
				important						Weak resilience
	Sweden	Majority	Important	Important	Strong	Growing	Re-regulated	High	Very slow	Exuberant,
					regulation					Strong
										resilience
	France	Majority	Important	Important	Low	Strong	Strongly	Low	Very strong	Exuberant,
					regulation		regulated			Strong
					(rises only)					resilience
	UK	Dominant	Weak	Important	Free	Strong	Deregulated	Very high	Strong	Exuberant
	Italy	Dominant	Weak	Residual				Very low	Limited	Moderate
	Portugal	Dominant	Significant	Residual			Regulated	High	Very strong	Low volatility
	Spain	Invasive	Weak	Residual		Strong	Deregulated	High	Very strong	High volatility
	Greece	Dominant	Significant	Inexistant				Low	Strong	High volatility
	Ireland	Dominant	Weak	Residual	Free		Deregulated	Very high	Disproportionate	High volatility
	Iceland	Invasive	Weak	Residual			Deregulated	Very high	Disproportionate	High volatility
	Hungary	Dominant	Weak	Residual	Free			Very low	Limited	High volatility

Table 2 Mortgage debt and mortgage market characteristics

 2

		MD/Y	%	NMD/inh	%	MD/Y	%	NMD/inh	%	Typ.	Typ.	TIR g EQU h MCD i Bank
		2007 a	growth b	2007 c	growth d	2013	growth b	2013 c	growth d	LTV e	Mat f			reg j
			96-		96-		2007-		2007-
			2007		2007		13		13
	Austria	24	+12	9,850	+175	28	+4	12,600	+28	60	25 25% No	0.31 2.49
	Germany	48	+1	16,980	+28	44	-4	17,555	+3.4	70	25 0%	No	0.28 1.97
	Italy	17,5	+10	5,500	+270	23	+5.5	7,277	+32.3	50	-36%* No	0.26 2.74
	Finland	34	+5	14,884	+95	45,7	+11.7	20,311	+36.5	75	17 97% Yes	0.49 1.95
	France	35	+15	13,200	+143	43,8	+8.8	17,700	+34	75	15 14% No	0.23 1.52
	Belgium	38	+12	15,050	+139	49,5	+11.5	21,320	+42	83	20 6%	No	0.34 2.18
	Sweden	65,5	+14,5	30,348	+96	81	+15.5	44,624	+47	80	25 38% Yes	0.66 2.31
	Denmark	93	+31	46,130	+125	94	+1	52,920	+14.5	80	30 25% Yes	0.82 2.06
	Netherlands	98	+56	43,013	+258	105	+7	47,478	+10.4	90	30 30% Yes	0.71 1.66
	UK	85	+27	33,856	+204	81	-4	30,421	-10	75	25 72% Ext	0.58 1.45
	Spain	62	+44	17,519	+561	60	-2	15,977	-9	70	20 76% Lim. 0.40 1.78
	Ireland	74	+50	42,763	+758	58	-16	27,787	-35	70	20 70% Lim. 0.39 0.74
	Iceland	119	+54	24,600	+314			11,660	-52
	EU27	48		14,609		51	+1,5	16,220	+11

a. Residential mortgage debt to GDP ratio. Source: EMF b. Points of GDP. c. Nominal residential mortgage debt per adult. Source: EMF d. Variation of nominal debt per capita. e. Typical Loan to Value ratio. f. Typical maturity of mortgage loans. g. Type of interest rate (% of variable). h. Equity withdrawal. i. Index of mortgage credit development (IMF). j. Bank regulation (increasing in strictness (OECD)). *72% mixed Sources: ECB (2003), EMF (2012), IMF (2012), OECD (2011).


Yusik Ri
email: riyusik@163.com

Hyonchol Kim
email: kimhyonchol@163.com

Songil Cha
email: chasongil@163.com

Bootstrapping Method for Automatically Acquiring Instances and Properties in the Web Resources

Keywords: Bootstrapping, Ontology, Instance, Property, Proper noun extraction, Seed, Web tables

With the rapid increase of Web resources, the Ontology has become an important research topic in the fields of Web information management and knowledge engineering. Nonetheless, ontology creation remains a daunting task. Bootstrapping method, which aims at automatically generating instances and their relations in a given domain, is a promising technique for ontology creation. In this paper we focus on an ontology instance and property generating method by using bootstrapping from the Web table resources. Most Web tables are designed for intuitive perception of human, thus, it has a certain limit to automatically interpret the table using only structural information. So, this work observes on the method for interpreting the table structure based on proper noun that is semantic core element of the table. This work propose algorithms to automatically acquire all instances or properties belonging to a given class, taking few terms belonging to class composed of proper noun as the seed. Empirical study of our novel approach has proved the effectiveness of ontology creation. Experimental results show that our approach can effectively acquire more accurate instances and properties from Web resources.

Introduction

Today, the Semantic Web comprises techniques that promise to dramatically improve the current WWW and its use. With the emergence of the Semantic Web and the growing number of heterogeneous data sources, the benefits of ontologies are becoming widely accepted [START_REF] Antoniou | A semantic web primer (Second Edition)[END_REF]. Accordingly, researches for using ontologies on the Web have become active [START_REF] Serrano | Applied Ontology Engineering in Cloud Services, Networks and Management Systems[END_REF]. Currently, in many cases, most Web ontologies are simpler than previous ontologies used in the design and diagnosis. The Web ontologies define terms used as data (metadata) for explaining things of a special domain. Manually setting ontology up would entail a lot of time, not to mention that there are only a handful of experts available. For this reason, researchers are paying attention to automatic transformation of the Web resources in the areas into ontologies [START_REF] Zhang | A survey on fuzzy ontologies for the Semantic Web[END_REF]. In order to provide the necessary means to widely apply ontologies to various fields there are today many proposals for using ontology learning and machine learning, and until now the study on domain ontology learning has been flourishing. Rupasingha et al. [4] proposed a Web service clustering method through calculating the semantic similarity of Web services using ontology learning method. El Asikri et al. [START_REF] Asikri | Contribution To Ontologies Building Using the Semantic Web and Web Mining[END_REF] described the commonalities of the areas, such as the semantic web and data mining, in order to resolve problem of extracting useful and shared knowledge, as well as solve the problem of the interoperability between Web systems by using the ontology learning from Web content. Rupasingha et al. [START_REF] Rupasingha | Calculating Web Service Similarity using Ontology Learning with Machine Learning[END_REF] presented a method for calculating Web service similarity using both ontology learning and machine learning that uses a support vector machine for similarity calculation in generated ontology instead of edge count base method. Kumara et al. [START_REF] Kumara | Ontology Learning with Complex Data Type for Web Service Clustering[END_REF] proposed clustering approach that considers the complex data type as well as the simple type in measuring the service similarity. This approach used hybrid term similarity method which proposed in their previous work to measure the similarity. Song et al. [START_REF] Song | A Novel Automatic Ontology Construction Method Based on Web Data[END_REF] reviewed the related concepts and methods of ontology construction and extension, proposed an automatic ontology extension method based on supervised learning and text clustering. This method used the K-means clustering algorithm to separate the domain knowledge, and to guide the creation of training set for Naive Bayes classifier. Jupp et al. [START_REF] Jupp | Webulous and the Webulous Google Add-On -a web service and application for ontology building from templates[END_REF] presented Webulous that is an application suite for supporting ontology creation by design patterns, and provided simple mechanisms for the addition of new content in order to reduce the overall cost and effort required to develop ontologies. Peng et al. [START_REF] Peng | Medical ontology learning based on Web resources[END_REF] proposed a method which can learn a heavy-weighted medical ontology based on medical glossaries and Web resources, in order to deal with heterogeneous knowledge in the medical field. Wei et al. [START_REF] Wei | From Web Resources to Agricultural Ontology: a Method for Semi-Automatic Construction[END_REF] presented a semi-automatic construction method for agricultural professional ontology from web resources. For semi-structured web pages, this method automatically extracted and stored structured data through a program, built pattern mapping between relational database and ontology through human-computer interaction, and automatically generated a preliminary ontology, finally completed checking and refining by domain experts. The Web is an enormous resource of information contained in billions of individual pages. Most information resource on the Web is presented in the form of semistructured or unstructured documents, encoded as a mixture of loosely structured natural language text and template units. Yu et al. [START_REF] Yu | Use web resources to construct ontology concept hierarchy[END_REF] proposed a modified hierarchical concepts tree building method by applying pruning algorithm on the graph. They used the clue words to product queries containing hierarchical relation to get corpus rich in concepts hierarchical relation through the search engine from Web. Vasilateanu et al. [START_REF] Vasilateanu | Enterprise Domain Ontology Learning from Web-Based Corpus[END_REF] proposed a semantic search engine for relevant documents in an enterprise, based on automatic generated domain ontologies, with observing on the component for ontology learning and population. Manvi et al. [START_REF] Manvi | Generating Domain Specific Ontology for Retrieving Hidden Web Contents[END_REF] focused on generating domain specific ontology for retrieving hidden web contents. In this paper a knowledge base used in automatically filling up search interfaces for retrieving hidden web data. The Web tables are used mainly for structuring information, and they are the strongest means of presenting structured information. The The comparatively comprehensive and complete model for the analysis and transformation of the tables is Hurst's [START_REF] Hurst | Layout and language: Beyond simple text for information interaction -modelling the table[END_REF]. This model analyzes the tables along graphical, physical, structural, functional, and semantic dimensions. Jung et al. [START_REF] Jung | Hybrid Approach to Extracting Information from Web-Tables[END_REF] suggested a method for extracting table-schemata based on table structure and heuristics. Using this method, a table is converted into a table-schema and a triple. Chen et al. [START_REF] Chen | Mining Tables from Large Scale HTML Texts[END_REF] employ heuristic rules to filter out non-genuine tables from their test set and make assumptions about cell content similarity for the table recognition and interpretation. Wang et al. [START_REF] Wang | A Machine Learning Based Approach for Table Detection on the Web[END_REF] proposed a machine learning based approach to classify given table entity as either genuine or non genuine. Pivk et al. [START_REF] Pivk | From Tables to Frames[END_REF] focused on understanding table-like structures only due to their structural dimension and transforming the most relevant table types into F-logic frames. Tijerino et al. [START_REF] Tijerino | Ontology Generation From Tables[END_REF] described the automatic generation of ontologies from the normalized tables, which is a structure they got after normalizing table-equivalent data. Tanaka et al. [START_REF] Tanaka | Ontology Extraction from Table on the Web[END_REF] proposed a method for extracting relations based on interpretations given by humans, in order to interpret structures of each tables correctly. This method is easy to apply to tables in various domains because it uses interpretations given by humans and generalized table structures instead of a domain-specific knowledge base. Jung et al. [START_REF] Jung | Constructing Domain Ontology Using Structural and Semantic Characteristics of Web-Table Head[END_REF] detected that, generally, a table provides a semantic core element in a HEAD, and proposed a method for automatically extracting domain ontology using heuristics for extracting table schemata based on semantic core element. As specified above, most research endeavors to interpret the table by using structural characteristics of the table. But, most Web tables are designed by humans, thus, it has a certain limit to automatically interpret table using only structural information of the table. Though Jung et al. [START_REF] Jung | Constructing Domain Ontology Using Structural and Semantic Characteristics of Web-Table Head[END_REF] proposed heuristics for detecting semantic characteristics based on the location of the table cells, they did not mention which becomes semantic core element. Through the observation about semantic features of the table, we found that if there are proper nouns on the table, then they can become a semantic core element. So this paper focuses on the proper noun extraction method, which is a prerequirement for interpretation of table structure based on proper nouns. That is, in this paper, we propose an automatic extraction method of the instance composed of proper nouns. In order to rapidly and accurately obtain brief domain ontology from the table cells consisting of proper nouns, we employ unsupervised learning method based on bootstrapping [START_REF] Abney | Bootstrapping[END_REF]. Bootstrapping method, which aims at automatically generating instances and their relations in a given domain, is a promising technique for ontology creation. H. Davulcu et al. [START_REF] Davulcu | OntoMiner: Bootstrapping and populating ontologies from domain-specific Web sites[END_REF] proposed the OntoMiner system which offers automated techniques for creating ontologies based on a small collection of relevant Web sites. The work presented an approach for bootstrapping and populating large, rich, and up-to-date domain ontologies that organize the most relevant concepts, their relationships, and instances (which correspond to members of concepts). W. S. Wu et al. [START_REF] Wu | Bootstrapping domain ontology for semantic web services from source web sites, Technologies for E-Services[END_REF] presented the DeepMiner system which learns domain ontologies from the source Web sites. Given a set of sources in a domain of interest, DeepMiner first learns a base ontology from their query interfaces. It then grows the current ontology by probing the sources and discovering additional concepts and instances from the data pages retrieved from the sources. A. Segev et al. [START_REF] Segev | Bootstrapping Ontologies for Web Services[END_REF] proposed an ontology bootstrapping process for web services. The proposed ontology bootstrapping process integrates the results of two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation, and applies a method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. F. Keshtkar et al. [START_REF] Zhang | Construction of semantic bootstrapping models for relation extraction[END_REF] presented a novel semantic bootstrapping framework that uses semantic information of patterns and flexible match method. The work considerably enhance based on iterative bootstrapping model which generally implies semantic drift or low recall problem. Through the experimental observation about semantic features of the Web tables, we found that if there are proper nouns on the table, then they can become a semantic core element. The previous research (Cha et al. [START_REF] Cha | A Method to Extract Automatically Ontology Instances Using Bootstrapping[END_REF]) proposed algorithms to automatically generate all instances belonging to an ontology class from a Web table, taking few instances belonging to the class composed of proper noun as the seed. To this end, this paper focuses on the extracting instances and properties based on interpreting the table contents by using structural and semantic characteristics of the table. That is, in this paper, we propose a bootstrapping method to construct ontologies with the instances and properties. The paper is structured as follows: In the Section 2 we present an automatic generation method of the instance belonging to given class from Web tables; In Section 3, we describe an automatic property generation method based on proper noun extraction; In Section 4, we evaluate our method according to the experimental result; Finally, in Section 5, we provide conclusions to our work.

2. Automatic instance acquisition

The knowledge on the domain terminology is required in order to manually construct ontology about products that are increasing daily such as CD / DVD, software etc., are not known already. Thus, it is highly regarded to automatically build an ontology based on Internet resources of a given area. But, in order to automatically generate semantically correct ontology, certainly, must to be based on some clues. If to extract instances from the Web table does not depend on any clue and uses only structural information of the table, it is difficult to determine quality of the extracted instance. Through experimental interpreting of Web table structure, we found that if there are proper nouns on the table, then they can be as the semantic core element, that is, as the instance. The semantic core element is a head element that plays the role of the 'pivot' in understanding table structure [START_REF] Jung | Constructing Domain Ontology Using Structural and Semantic Characteristics of Web-Table Head[END_REF]. Once proper nouns are extracted from a table, we can more accurately interpret table structures focusing on proper nouns that are semantic element of the table. Therefore, this section describes method generating automatically other instances in the table, taking some proper nouns such as already familiar product name as a clue. That is, this method is an approach which extracts the rest instances in the same class by using the proper noun extraction method, having some instances given by the user as a seed.

2.1. Proper noun extraction model based on bootstrapping

For automatic extraction of the instance, we employ the proper noun extraction method. This method is an approach, that if there is the row or the column composed by the proper noun in the Web table, then we consider it as instance and extract the proper nouns. That is, the proper noun extraction means to extract the other terms guessed belong to the class which the entered word (proper noun) belongs to. In this section we present the proper noun extraction method using bootstrapping. Bootstrapping is shown as follows: firstly, generate a pattern from the document in accordance with a small amount of seed terms, then using this pattern again extract other words from the document, and lastly using the extracted terms create another word. We can extract a large amount of terms from a small amount of seed terms by a repeat of this process. To begin with we define the fundamental notions.

A proper noun p is a noun that is the name of a specific individual, place, or object. For example, personal name, country name, denomination name, organization name, and so on. A proper noun set P is a set of proper nouns, i.e. p ∈P.

A seed term s is a proper noun specified artificially before proceeding with automatic learning. A seed term set S is a set of the seed terms, i.e. s ∈S.

From the above definition, we can know that s∈P and S⊂P. For example, Table 1 shows the seed term set which belongs to each class. Herein, first column is class name and second column is the proper nouns which belong to the class. For example, desktop is a name of desktop class, and Acer, Asus and Compaq are the instance (manufacturer brand) which belongs to the desktop class.

A domain table set T is a set of genuine tables which is chosen in the Web tables of a given domain, collected using search engine such as Google or Yahoo. In this paper, in order to obtain genuine tables of given domain we use the algorithm proposed in the previous study [START_REF] Jung | Hybrid Approach to Extracting Information from Web-Tables[END_REF]. Withal, search keys for obtaining of the domain table set are a domain name (that is, a class name) and the seed term set selected by user in the beginning.

2.2. Instance acquisition algorithm

In this subsection, we propose the detailed algorithm for acquiring the instances based on IB-Model. The input of this algorithm is the domain

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In algorithm 1, X is an element pair which consisted by arbitrary combination of elements in the seed term set S, for example {s1, s2, s3},{s1, s2},{s1, s3},{s2, s3},{ s1},{ s2},{ s3}, and so on. C0 is a candidate term set acquired when the seed term is {s1, s2, s3}. C is a candidate term set which is consisted at the rest every loop period. X0 is a seed term set that used as input of every loop period. In the beginning, the algorithm enters the bootstrapping process, taking {s1, s2, s3} which consist of three terms. Then add the new acquired terms to the candidate term set C. In the second loop, takes two old (before used) terms and a newly acquired term as a seed term. Because we can't consider that the newly acquired terms are certainly right instance. This will prevent that the accuracy is lower, and take another new elements as a seed. Fundamental steps of algorithm are shown as follows:

Pattern enactment. In order to produce pattern, we found TABLE tags which wraps each of the seeds from arbitrary table of the domain table set. For example, when {Hunan, Hubei, Shanxi} is a seed term, we can take Hunan	, Hubei	, Shanxi	 which is wrapped by TABLE tag as a pattern. That is, we can constitute the term	 used for defining the table cells as a pattern. Every table's creator is using TABLE tag of different description form, but this algorithm employs only …	 tag which denotes the table cell for the purpose of pattern enactment. Withal, for a brief descriptive purpose, we consider only the case of nonuse of  tag attribute such as ALIGN, VLIGN, EIDTH, HIGHT, BACKGROUND, BTCOLOR, and so on. Fig. 2 shows the example of the Web table that contains above three seeds.

Fig. 2. An example of a table contained the proper nouns

Acquiring the candidate term. In order to construct the candidate term set, we assume as follows: If certain term appears with the proper noun in same row or same column of a table, then this term also is a proper noun. Therefore, we take the row or column of term extraction. For example, the second column of Fig. 3 contains the proper noun "Wuhan" that we know already. Withal, another terms on this column are proper nouns belonging to "Chinese cities" class. Before extracting the candidate term, we must determine table reading orientation, i.e. row wise or column wise [START_REF] Chen | Mining Tables from Large Scale HTML Texts[END_REF]. For example, if the first row of the table consists of the attribute cells, and the others are value cells, then this table is the column wise. If the table is row wise, then take each cell of the row in which exists proper noun as candidate term, and if the table is column wise, then take each cell of the column in which exists proper noun as candidate term. Evaluation. In order to extract more accurate candidate term, we must resolve table HEAD. Withal, we must determine candidate term extraction range in the row (or column) in which exists proper noun, that is, we must resolve value region corresponding to the table HEAD. Fig. 4 is an example in which exists two different HEADs (1 st row and 13 rd row). For the purpose of extracting candidate term taking above proper noun as a seed, we must restrict range of value region, i.e. there is candidate extraction object between 2 nd row and 12 nd row. But if not, it has possibility to extract irrelevant terms. In this example, "Mexico", "Germany", and so on are irrelevant term. Therefore, in this step we evaluate whether each term c of the candidate term set C is a term belonging to a given class, and choose only fit terms. Withal, in the evaluation step, evaluate whether newly extracted proper noun exists in the proper noun set P, and only add proper noun which does not overlap to P.

Automatic property acquisition

Observation on the location of the properties

It is difficult to extract inclusively, if property also as well as instance does not depend on any clue. Therefore, in this section we propose an automatic acquisition method of property belonging to the class using proper noun extraction approach based on bootstrapping.

In order to extract the property from the Web table, firstly, based on the proper noun extraction method mentioned above, extract the instance belonging to the class, and take a set of these instances as P. Then, determine three initial seed property belonging to the class, and denote them as a 1 , a 2 , a 3 . Denote a set of the properties as A, i.e. a 1 , a 2 , a 3 ∈A. For example, in the first row of the table on Fig. 5, attributes such as Type, Standard, Resolution, and so on, are property belonging to a camera class, and elements (but the first element is excepted) of the first column are instances of a camera Model class.

Fig. 5. An example of the table that contains the several attributes

As shown in the figure, when the table is row wise, generally, the properties are constructed by the cells which lie at the first row of the table. When extracting the property using the proper noun extraction method, the seed term contains three properties and one instance which is selected from instances collected already. In each product there are special and common attributes. For example, not only digital camera but also computer has "resolution" attribute, that is, this is the common attribute. When we take only three properties as the seed property, it has possibility which can extract even properties belonging to other class. Thus, we add one instance to the seed term set S, in order to extract rightly the property belonging to given class.

3.2. Property acquisition algorithm based on the IB-Model

The property acquisition algorithm based on the IB-Model similar to the instance acquisition algorithm (algorithm 1). The properties discussed in this subsection are Owl:DatatypeProperty [START_REF]OWL Web Ontology Language Reference[END_REF][START_REF]OWL 2 Web Ontology Language Document Overview (Second Edition)[END_REF] In algorithm 2, X is a element pair which consisted by arbitrary combination of elements in the seed property set A. C is a candidate property set which consisted at the every loop period. R is a property set. X0 is a seed term set that it used as input of every loop period. In the beginning, the algorithm enters the bootstrapping process, taking {s1, s2, s3} which consist of three properties and an instance of P. In the second loop, the algorithm takes two old (before used) properties, a newly extracted property, and an instance of P as a seed term. If all elements of property set were processed, then replace the instance by a new element and repeat process. In the subsection below, stepwise instantiate major steps of algorithm. Pattern enactment. In order to produce pattern, we look for TABLE tags which wrap each of the properties from arbitrary table of the domain table set. The HEAD tag of the table is often denoted by term	, term	, term	, term	, term	, and so on. At the HEAD of the table, tag attribute is used for the purpose of standing out. In order to denote HEAD cell, every table's creator uses table tag of different description form, but for a brief descriptive purpose, we employ only term	 pattern (see Fig. 2). Acquiring the candidate property. In order to construct the candidate property set, we assume as follows: If certain term appears with the seed property in HEAD row or HEAD column of a table, then this term also is a property. Therefore, we take the HEAD row or HEAD column of the table as the object of candidate property acquisition. Evaluation. In this step, firstly, remove properties that are not DatatypeProperty from the candidate property set. That is, if "total" or "other" appears at the HEAD of the table, then remove it from the property candidate. Denote the term set which isn't DatatypeProperty by "NonwordList", i.e. we need c∉NonwordList. Then, from HEAD row cell remove the element that  tag's attribute is COLSPAN. That HEAD element generally has hierarchical structure, thus this is not DatatypeProperty that we wish to obtain. Withal, in this step, evaluate whether newly acquired property exists in the property set R, and only add property which does not overlap to R. In the case of property acquisition, also institute threshold value N of seed property set A as greater one than two.

4. Empirical evaluation

4.1. Empirical evaluation of the algorithm 1

Automatic instance acquisition method (algorithm 1) mentioned above is suited to obtain instances, which are severely changed, such as new products. In our experiment, in order to correctly construct the seed term set, we selected twenty publishing companies based on revenue rank of the world's largest publishing companies in 2008 from the Web pages. Fig. 6 shows a Web table in which second column contains the instances belonging to a "publishing company" class, i.e. the seed term set is consisted of cells in the second column of this table. Firstly, in order to obtain an experimental data, we choose initial seed belonging to "publishing company" class. If number of the initial seed is many, then accuracy is enhanced. Through experimental observations, we confirmed that when take two initial seed terms, a large amount of irrelevant terms are extracted and the accuracy was decreased. Hence, we selected already known three initial seeds, i.e. {McGraw-Hill Education, Wiley, Reed Elsevier}. Next, in order to obtain the domain table set, we collected arbitrary two hundred Web tables among around twenty thousand Web pages of the "publishing" domain acquired using search key "publishing company", "McGraw-Hill Education", "Wiley" and "Reed Elsevier" of Google search engine. Then, according to the algorithm 1 proposed in the section 2, we acquire the instances belonging to the "publishing company" class. In this case, we take  McGraw-Hill Education 	,  Wiley 	,  Reed Elsevier 	 which is wrapped by  tag and 	 tag used for defining the table cells as a pattern. Previous researches endeavored to interpret the tables using structural characteristic, hence, it is difficult to experimentally compare with our method. Withal, it is troublesome to rightly measure recall and precision of the experimental results obtained by our method. Therefore, in this experiment, we evaluated that the proposed instance acquisition algorithm how to effectively increase the instances about every time operation. For the above purpose, we compared with twenty instances extracted from Fig. 6, i.e. watch obtaining process of correct twenty instances. In order to evaluate the experimental results, we used the recall and precision metrics as follows: We can interpret the recall and precision metrics as follows: Firstly, we discuss the recall. In the case of the simple input, the system registers singly the correct instance depending on the input sequence. Therefore, the recall is proportional to input time, in the case of seventeenth input only arrive at 100 percent. In the case of our proper noun extraction, is extracted a large amount of candidate instances at first time, at second time the recall arrives at 64 percent. However, at next time the candidate term is overlapped, and rising speed is slow, at sixth time it arrives at 100 percent. Through this, we confirmed that the proper noun extraction method can increase effectively the instance from a small amount of user input. Next, we discuss the precision. In the case of the simple input, the precision is 100 percent. But, in the case of our instance extraction based on IB-model, the precision decreases and at seventeenth time almost arrives at 94 percent. A cause that the precision does not arrive at 100 percent is that was included the irrelevant terms.

4.2. Empirical evaluation of the algorithm2

The property acquisition method (algorithm 2) employs the instances which extracted by the instance acquisition algorithm (algorithm 1). For an experiment, we selected products which have comparatively many attributes such as mobile phone, digital camera, LCD TV, LCD monitor and CRT monitor. Next, in order to obtain the domain table set, we collected around ten thousand Web pages acquired using individually search key "mobile phone", "digital camera", "LCD TV", "LCD monitor" and "CRT monitor" of Google search engine. Table 2 shows the representative attributes belonging to each product class. Next, in order to obtain more correct table set, we choose again the Web pages which contain "Nokia", "dimensions", "weight" and "display size" from around two thousand Web pages acquired by search key "Mobile phone".

When {Nokia, dimensions, weight, display size} is a seed term, we can take Nokia	 and dimensions	, weight	, display size	 which is wrapped by HEAD tag as a pattern. Then, according to the algorithm 2 proposed in the section 3, we acquire the properties belonging to the "Mobile phone" class.

Likewise, we can extract properties belonging to other class. Table 3 shows total experimental result. As shown in the table 3, the average recall of our method arrives at 93 percent. Through this, we confirmed that our method can obtain almost the properties belonging to a given class. Withal, the average precision arrives at 90 percent. To extract the properties from the Web pages which describe only actual attributes of the product is the most ideal. But, in this experiment, did not choose from the Web pages applied like that.

Conclusions

This paper has presented an automatic acquisition method of instances and properties belonging to a given class from the Web table resources based on bootstrapping. Our approach was able to acquire fast a great amount of instances and properties using bootstrapping from a small amount of seed terms entered by human. When interpreting and straightening out table structure according to ontology schemata, to determine accurately class, instance and property gives a decisive impact on the use of ontologies in the future. Though this paper briefly extracted only instances composed of proper nouns and DatatypeProperty, we consider that this approach offer a good basis not for construction of arbitrary domain ontology fit with our intent, but for right semantic interpretation of table structure. In our future work, a more precise method for extracting ObjectProperty and other relationships will be developed based on extracted proper nouns.

1 )

 1 Recall = Number of acquired correct instance/ Total number of correct instances (Precision = Number of acquired correct instance/ Total number of acquired instances (2)

Fig. 6 .

 6 Fig.6. An example of the table of the publishing domain extracted from the Web page Fig.7shows the comparison of the experimental results.

Fig. 7 .

 7 Fig. 7. The comparison of the experimental results

  LCD TVDisplay type, Resolution, Screen size, Aspect ratio, Dynamic contrast ratio, Response time, Viewing angle, Audio output power, USB, HDMI ports, TV size, etc.

  type, Viewable size, Resolution, Backlight, Color depth, Contrast ratio, Viewing angle, Response time, Luminance, Video input, Horizontal/vertical frequency, Power consumption(On), Power consumption(Off), On screen display, Controls, Power, Dimensions, Weight, Safety and EMI, VESA compliant, Kensington lock ready, Integrated audio, Miscellaneous, etc. CRT monitor Screen type, Viewable size, Resolution, Dot pitch, Face, Video input, Horizontal/vertical frequency, Controls, Dimensions, Safety and EMI, Miscellaneous, etc.

  Table structures represent relations between data in the table. Therefore, we can easily extract ontologies from a table, based on features of the table structures [15]. However, understanding of table contents requires table structure comprehension and semantic interpretation, which exceed the complexity of corresponding linguistic tasks. Previous studies for constructing domain ontologies from the Web table are centralized to interpret table structure.

Table 1 .

 1 An example of the seed term belonging to a given class Model is the domain table set T and the initial seed term set S, the output of this model is the proper noun set. A quality of the seed term set greatly affects the accuracy of the extracted proper nouns. Therefore, users must select more obvious and important terms for the initial seed term set. In addition, through experimental study, we confirmed that can increase the accuracy of the proper noun extraction in the case of that N s (the number of the seed terms) is three or more. The number of the seed list used in this model is always N s . A threshold value N of the seed list is the number of an old seed term among the seed terms entered in the each loop of bootstrapping. In the beginning, N=N s . Constitute the threshold value N of the seed list as greater one than two. This means that it must contain at least two initial seed term in the seed terms used at the every loop. That is, can effectively extract terms when N≥2. But if not, can't the extracted terms. The seed list is possible combinations of initial seed terms composed of greater one than two. At the first loop period of bootstrapping, this model extracts terms taking N s initial seed terms. But, at the second loop period, it extracts possible candidate terms taking N s -1 initial seed terms and a new seed candidate. Likewise, at the third loop period, it extracts possible candidate terms taking N s -2 initial seed terms and two new seed candidates. The loop period finishes when N<2. In the pattern production process, we institute the TABLE tags that wrap each of the seed term from the table. In the table selection and extraction process of the candidate term, model selects tables that contain the seed term pattern from the domain table set and extract all rest cells which appears with the seed terms in same row or same column of the table. In the selection and evaluation process, model firstly, only adds the obtained candidate terms which do not overlap to the proper noun set. Secondly, selects the seed candidates for next loop among the proper noun set and add to a seed candidate set. Finally, evaluates the threshold value N of the seed list, and finishes when N<2, if not repeats the loop period.

		Acer, Asus, Compaq, Dell, eMachines, Everex,
	Desktop	Gateway, HASEE, HP, Lenovo, Panasonic, Samsung,
		Sony, TCL, Toshiba, etc.
		Agfa, Canon, Casio, Contax, Epson, FujiFilm, HP,
	Digital Camera	Kodak, Konica Minolta, Kyocera, Leica, Nikon, Olympus, Panasonic, Pentax, Ricoh, Samsung, Sanyo,
		Sigma, Sony, Toshiba, etc.
		Akai, AOC, Axion, Benq, Casio, Dell, Diamond, Epson,
		Gateway, GPX, Haier, Hewlett Packard, Hitachi,
	LCD TV	Honeywell, Hyundai, JVC, Konka, LG, Mitsubishi,
		NEC, Nikon, Panasonic, Philips, Samsung, Sanyo-
		Fisher, Sharp, Skayworth, Sony, Toshiba, etc.

The proper noun extraction model based on bootstrapping is shown below. This model extracts automatically new proper nouns based on the few seed terms from the domain table set. In this work, the model is named as IB-Model (Instance Bootstrapping-Model). Fig.

1

shows IB-Model. In the model, the dotted line arrow denotes a process for extracting a pattern that contain the initial seed term. The extraction of the pattern from the domain table set only needs to be determined once.

Fig. 1. IB-Model

The input of the IB-

  X←{s 1 ,s 2 ,s 3 }, X 0 ←{s 1 ,s 2 ,s 3 },P←s 1 ∪s 2 ∪s 3 , C Select new seed candidate set X₀ from X and C 0

	10: P←P∪C
	11: if isn't │X│≥ 2 then
	12: output P, and finish
	13: end if
	14: // Selection
	15: if processed all elements in C 0 then
	16: from S select new element pair and replace X by it;
	17: If C 0 =Ø then
	18:	C 0 ←C
	19: end if
	20: end if
	21:

table set T and three initial seeds s 1 , s 2 , s 3. The output of this algorithm is the acquired instance set P. Our goal is to rapidly and accurately generate the domain ontology. Therefore, we used tree seed terms, i.e. N s =3. Algorithm 1 shows the instance acquisition algorithm. Algorithm 1. Instance acquisition algorithm 1: Input S,T 2: Choose s 1 ,s 2 ,s 3 ∈S 3: 0 ←Ø 4: Create pattern which wraps S from T 5: Construct candidate term set C from the table in T by using pattern that wraps X₀ 6: //Evaluation and selection 7: // Evaluation 8: From C remove terms that don't correspond to evaluation condition 9: From C remove all c that c∈C ∧ c∈P , it's result also remains to C

  R←s 1 ∪s 2 ∪s 3 3: Create pattern which wraps A and P from T 4: X←{s 1 ,s 2 ,s 3 }; Take arbitrary p ∈P, X 0 ←{p,s 1 ,s 2 , s 3 }, C 0 ←Ø 5: Construct candidate property set C from the table in T by using pattern that wraps X₀ 6: // Evaluation and selection 7: // Evaluation 8: From C remove properties that isn't DatatypeProperty 9: From C remove all c that c∈C ∧ c∈R , it's result also remains to C 10: R←R ∪ C 11: if isn't │X│≥ 2 then go to 21 12: // Selection 13: if processed all elements in C 0 then 14: from A select new element pair and replace X by it 15: if C 0 =Ø then

		.
	Algorithm 2 shows the property acquisition algorithm.
	Algorithm 2. Property acquisition algorithm
	1: Input A,P,T
	2: 16:	C 0 ←C
	17: end if
	18: end if
	19: Select new seed property set X 0 from P, X, C 0
	20: go to 5
	21: if processed all elements in P then
	22: output R, and finish
	23: else go to 4
	24: end if

Table 2 .

 2 An example of the correct attribute set Bit depth, Lens system, Picture mode, Image control functions, Memory card compatibility, Shutter speed, Zoom capability, Flash, Focus, Shooting mode, LCD display, Sound capture, Connectivity, Power supply, System requirements, Dimensions, Weight, Model, etc.

		2G Network, 3G Network, Dimensions, Weight, Display
		type, display size, Alert types, Internal memory, Card
	Mobile phone	slot, GPRS, EDGE, WLAN, Bluetooth, USB, Camera,
		OS, CPU, Messaging, Browser, Games, Colors, GPS,
		Battery, etc.
		Resolution,
	Digital
	camera

Table 3 .

 3 Recall and precision for acquired properties

	Product class	No. of web tables	Experimental result Recall (%) Precision (%)
	Mobile phone	156	95.4	91.8
	Digital camera	139	93.0	89.6
	LCD TV	112	89.2	88.7
	LCD monitor	134	94.7	91.2
	CRT monitor	97	90.8	90.3

Model