Gaignerot - Driessen

From Communal incorporated Shrines to Public independent Sanctuaries in lm iii Crete Florence

). in the very core of this building is a small, 1. i would like to

Fig. 1.

Plan of the building on the Summit at Sissi in lm iiib (drawing by author). rectangular room 3.8 which we excavated in 2009. along the north wall sits a triangular stone with a polished flat surface (figs. 2-3). in the narrow space between this stone and the wall were standing three kalathoi, while two other similar kalathoi were found to the west in front of the triangular stone. one of these most likely fell from the mouth of a snake tube found immediately west of it. Fragments of a sixth kalathos were found one meter further to the south. another tubular stand, without handles but with three pairs of small horns of consecration around its mouth, was recovered slightly further to the south, with next to it a worked deer antler tine. to the east of the triangular stone were lying a handle of what may have been a lamp or a brazier, a large triton shell, and an inverted tall conical cup (fig. 3). more to the south were found other vases and a fragment of another triton shell. against the east wall runs a low bench in an oblique way. in the southwest corner of the room was a rectangular stone with two circular depressions and, in front of it, the bases of two conical cups and four lithic tools. Fragments of bone and shell were found scattered around it and may suggest that the stone tools served to crush certain substances in the depressions. on the basis of the nature, the association and the display of finds, it can be reasonably suggested that room 3.8 represents a space specially designed to accommodate ritual activities connected to a cult and may therefore be called a shrine. i prefer avoiding the label domestic shrine that creates confusion between architectural (domestic/ religious) and social (private/ communal/public) levels 3 . i would rather call room 3.8 an incorporated shrine. and since this specific space forms part of a larger building complex with activities exceeding the frame of the household, in particular in the nearby spacious and monumental hall 3.1, which allowed the gathering of at least three dozen of people, i would call it a communal incorporated shrine (fig. 1). The shrine opens to the south to a large room (4.11) with two square stone pillars and a central hearth, where a snake tube was also recovered. two steps and a platform, giving access to Space 3.10, opened on room 3.1 and its open air area.

another example of such an incorporated communal shrine can be identified in Quartier Nu, a lm iiia-b residential complex at malia (fig. 4) 4 . in the small, square room X2, was found a snake tube next to a slab in the south-east corner, as fallen from it, with nearby a brazier and a stone vase (fig. 5). more to the north were found a conical cup, a miniature vase, a pyxis and a kalathos whereas a second snake tube was standing against the east wall. The main entrance to the complex was from the east where a small porch with pillars gives access to the main hall X22 which has a central hearth between two column bases. in this hall too, at least two dozen people could potentially gather. to reach the shrine X2, one had to pass two spaces with industrial functions. it is not clear if room X2 had a window on the central court in the north part of its west wall. if so, the shrine in X2 may be brought into connection with the pebble mosaic on which a large house model was found. From room X2, it was also possible to reach the open-air space Xii2. i do not have time here to discuss in detail all the other examples but most of them are already known and mentioned in Geraldine Gesell's book 5 . Room 4 in the House with the Snake Tube at Kommos for instance has a cultic context with a snake tube decorated with birds standing on a slab and supporting a conical cup containing pebbles 6 . it is true that no other cultic material was recovered but the fixed installations as well as the variety and the arrangement of the material indicate that these objects were specially deposited, as convincingly shown by John mcenroe, and not only stored in room 4, where no cooking was done. The room opens up on a large open-air area to the south. The material from the adjacent room 6 illustrates that the room was at least occasionally used for dining, while food processing went on in room 12.

at Kommos again, in the small Room X7 in House X a table was recovered with on it two milk jugs, a conical cup containing pebbles and a few shells, while two kalathoi, one of them full of ash, were tucked under the table. in front of it was lying a small slab with traces of ash and evidence of burning and next to it a scatter of pebbles and a triton shell 7 . to the north were found a brazier and an incense burner.

no snake tube was apparently found but one can note the similarities with the context in room 4 in the house with the Snake tube or in a small room in a lm iiib house at Katsamba where two nearly identical red-painted snake tubes made in the same clay were lying in front of a stone platform under which was found an incense burner, a pyxis and two handless cups 8 . ash was found nearby. From the street, the house may have been accessible from somewhere to the south where a storage room was excavated.

at Koumasa too was found a room from which two snake tubes dating to 5. Gesell 1985, 47. 6. mcenroe 1996 and Shaw 1996. 7. Shaw 1993. 8. aλεξίου 1955. lm iiib were recovered and Xanthoudides reports that the room was included into a larger complex 9 .

The presence in a lm iiia2 complex at Kephali Chondrou of various cult objects including a snake tube, a triton shell, cups, offering table legs, figurine head, and rhyta as well as their distribution in three adjacent but not communicating areas makes it very plausible that a shrine was located on an upper floor above Space n1 10 . The building housed activities exceeding the frame of the household and some of its rooms only communicated with the exterior. The alleged shrine above space n1 could have been reached through the paved corridors and from an open-air space to the north.

Room 1 in Building 7 at Palaikastro also contained a cultic context with a tubular stand and a platform 11 . The building was flanked to the southwest by a street but no doorway seems to have existed in this façade. rooms 2, 12 and 5 preserved ample evidence for storage and a public function is suggested for room 14.

all these examples comprise some common characteristics. They all represent shrines incorporated in buildings with communal functions that are dated to lm iiia-b and a number of them were found in coastal sites. They are mostly formed by rather small and more or less square rooms. even if some of these rooms were visible from the outside, this does not mean that the access to the shrine was uncontrolled. on the contrary, it seems that sometimes efforts were made to make access deliberately complicated, forcing the visitor to follow a narrow street or corridor. if the small dimensions of the shrines also imply restricted or controlled access, exterior spaces or larger rooms are always located nearby, allowing more public performances and processions. but the most obvious feature that these shrines have in common is the fact that they all lack figures with upraised arms, as well as plaques. Some may argue that the figures were removed before the sites were abandoned or destroyed. but these figures usually appear in groups, either within lm iii shrines or as fragments scattered or deposited in their immediate vicinity: hence, at least 4 figures were found at Kannia-mitropolis; 5 at Gazi; at least 6 in a deposit east of temple a at Prinias; at least 30 in and around building G at Vronda; at least 5 at Kephala Vasilikis; more than 5 in the temple at Karphi and probably 16 in the cult building at Chalasmeno. it is thus very striking that not a single fragment such as a finger or a piece of a tiara was found within or in the immediate vicinity of the shrines considered in this paper. This remark particularly applies for the recently and intensively excavated sites. Thus it appears that a series of shrines existed on lm iiia-b Crete which contained elements belonging to the equipment of the Gua but which clearly did not comprise any of these figures.

Since the shrines where their presence is attested all date to the mature lm iiib or lm iiiC period, we may suggest that these figures, together with the plaques, were a relative late addition to an existing cult and its equipment. one may object that a figure already formed the focus of the cult in the lm iiia2-b Shrine of the Double axes at Knossos. but the later figures differ from her in many aspects, both contextually and iconographically and may have a completely different meaning and function. We can then propose the following evolution: in lm iiia-b, shrines formed part of communal buildings. They contained especially snake tubes and kalathoi, triton shells, lamps and braziers which were deposited on slabs and platforms. in lm iiib-C, shrines were freestanding bench sanctuaries, containing figures with upraised arms with their respective sets of snake tubes, plaques and kalathoi. What could be the meaning of this development?

Considering the cult material from Vronda, anna-lucia D' agata has proposed to see each ritual set as a dedication from one group in the community or from a regional group of sites 12 . metaxia tsipopoulou has recently suggested a similar hypothesis for the different sets found in the lm iiiC shrine at Chalasmeno 13 : they would constitute votive offerings from different clans in a context of regional competition. These observations make it tempting to see the figures as a symbolic representation of the votaries themselves and not as cult images. The individual statues may be considered as emblematic for a votive group. in this regard, their distinctive attributes may be considered as a symbol of the group rather than a reference to a specific deity, different deities or a separate aspect of a deity, as has usually been suggested. This hypothesis is corroborated by the architectural context and the settlement structure: if the figures were indeed offerings representing votaries, it is not surprising to encounter this type of object solely in public buildings shared by different worshippers who used the figures to signal their identities. in instances where the identity of the votaries was obvious, as in the case of the earlier lm iiia-b incorporated shrines, the socalled Guas would simply not be needed. This hypothesis does not necessarily 12. D ' agata 2001. 13. tsipopoulou 2009. imply that these figures were images of the votaries. indeed even if the figures were probably not cult images, it cannot be conclusively shown that they were not votives representing deities. but the crucial problem is perhaps not to know who precisely they are a representation of but what they are representative of. in a context of interconnection and competition, a clear identification of the votary may have been more important than that of the specific divinity. metaxia tsipopoulou suggests in particular that the pithoi recovered in the shrine of Chalasmeno were offered by different social groups who dedicated the figures and their usual sets 14 . Clearly here the Votives with upraised arms were involved in a strategy of ritualization strongly connected to social, economic, political and cultural interests. at the same time, a link with the wider population was created by the organization of public processions, as clearly illustrated at Chalasmeno where paved roads lead from the different building complexes to the shrine surrounded by large open areas. The same can be observed at Vronda where the shrine, also located at the edge of the settlement, faces a broad terrace allowing large gatherings 15 .

The lm iiiC and remote site of Karphi illustrates, however, an interesting case in the here proposed evolutionary process 16 .Small room 58 in the large and complex Priest's house contains material and architectural elements which allow it to be identified as a communal incorporated shrine without figures. The number of sanctuaries and the variety of contemporaneous cults recorded at Karphi seem moreover to indicate that there was no single cult place and hence no centralized cult and therefore perhaps no central ruling organization. Still, all the Karphi shrines were reachable from streets and had thus an access to the temple, where Figures with upraised arms were recovered. The temple thus appears as a public place, where the offering of votives with upraised arms standardized or perhaps institutionalized an internal competition. to conclude, it seems that after the collapse of the palatial system, a cult hosted in incorporated shrines was used to centralize members of local groups that constituted the new communal organization. When these groups were progressively integrated into larger collectives, in newly established sites, the shrine became externalized and the cult was modified to include female figures, 14. tsipopoulou 2009. 15. Preston Day, Klein and turner 2009. 16. Pendlebury, Pendlebury and money-Coutts 1937-1938. representative of the different coalescent groups. The next decisive step would happen with the appearance of the Greek city-state and the introduction of the hearth temple, in the eighth century bC, as attested for instance at Dreros. it remains to determine whether a continuous evolution can be demonstrated linking the lm iiiC bench sanctuary to the hearth temple of the Cretan polis. in this regard, most can be hoped from the work recently resumed at Dreros and its territory.

Fig

  Fig. 2. lm iiib floor deposit in room 3.8 in the building on the Summit at Sissi (photo by author).

Fig

  Fig. 3. lm iiib floor deposit in room 3.8 in the building on the Summit at Sissi (drawing by author).

  3. For a discussion on domestic shrines, see hallager 2009. 4.Driessen and Farnoux 1994.

Fig. 4 .

 4 Fig. 4. Plan of Quartier nu at malia in lm iiib (drawing by author).

Fig. 5 .

 5 Fig. 5. lm iiib floor deposit in room X2 in Quartier nu at malia (drawing by author).

thank the Gerda henkel Stiftung, the university


D Renard

F Ors

Intaros -IMR Dataset

Introduction

This paper is meant to demonstrate how to use a simple Kriging interpolation from the RGeostats package applied to the Annual CTD datasets from R/V Håkon Mosby (Norwegian research vessel).

Download of NetCDF files

Use the following bash script for downloading the data files in the current directory: • date2time: Convert a readable date (YYYY-MM-DD) into a time value used by the dataset.

• som: Return the first day of the month from a given date

Quick example

• Here is a quick R script using these functions which performs:

-Some simple display of available data -Some data selection and dates manipulation -An analysis of the correlation between Temperature and Conductivity for a given year -The display of data points density in each grid cell for a given month -Trimester interpolations of the Temperature data at the surface for a given year Note that subsequent interpolation is performed in 2D (considering the 3D information would require a different script).

Loading Data

First of all, we load the data from the current directory into a new RGeostats database Db. We check its contents and the number of active samples. The following paragraph shows the contents of the newly created Db

db ## ## Data Base Characteristics ## ========================= ## ## Data Base Summary ## ----------------- ## File is organized as a set of isolated points ## Space dimension = 3 ## Number of fields = 8 ## Maximum Number of attributes = 8 ## Total number of samples = 5080116 ## ## Variables ## --------- ## Field = 1 -Name = rank -Locator = rank ## Field = 2 -

Exploratory data Analysis

We apply a selection to mask all samples deeper than 10m depth: We display the number of data points (<50m depth) that fall in each grid cell (with a mesh equal to 1 degree) for a given month:

Estimation

We estimate the 2007 Temperature season maps at sea surface (1m depth +-1m) on a refined grid (mesh = 0.5°) which covers all data available. The geostatistical model is automatically fitted using default parmaters: It could be improved using more expertise and providing additional parameters to function interpol_var.

For comparison purpose, the maps are displayed using the same colorscale, latitude and longitude limits.

  lftp -c open -e "mget 58AA_CTD_2002.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2003.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2004.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2005.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2006.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2007.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2008.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2009.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2010.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2011.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2012.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2013.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2014.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2015.nc" ftp.nmdc.no/nmdc/IMR/CTD lftp -c open -e "mget 58AA_CTD_2016.nc" ftp.nmdc.no/nmdc/IMR/CTD Definition of R functions Definition of some functions: • load_data: Load the data set (all NetCDF files (*.nc) from the given directory) into a Db RGeostats database. The data files must contain the following variables: -LATITUDE: Latitude (degrees) of the vessel position. 1D size = {Nb Positions} -LONGITUDE: Longitude (degrees) of the vessel position. 1D size = {Nb Positions} -TIME: Time of the position (number of days since January 1st, 1950). 1D size = {Nb Positions} -DEPH: Depth of each measure (m). 2D size = {Nb Positions, Nb Measures} -TEMP: Temperature (°C). 2D size = {Nb Positions, Nb Measures} -CNDC: Electrical conductivity (S m-1). 2D size = {Nb Positions, Nb Measures} -PSAL: Sea water practical salinity (0.001). 2D size = {Nb Positions, Nb Measures} • time2date: Convert a time value from the dataset into a readable date (YYYY-MM-DD).

  58AA_CTD_2002.nc : Number of positions = 54 , Maximum depth = 3433 m) ## Reading 58AA_CTD_2003.nc : Number of positions = 693 , Maximum depth = 2810 m) ## Reading 58AA_CTD_2004.nc : Number of positions = 1213 , Maximum depth = 2182 m) ## Reading 58AA_CTD_2005.nc : Number of positions = 1191 , Maximum depth = 2703 m) ## Reading 58AA_CTD_2006.nc : Number of positions = 1164 , Maximum depth = 3298 m) ## Reading 58AA_CTD_2007.nc : Number of positions = 1410 , Maximum depth = 3211 m) ## Reading 58AA_CTD_2008.nc : Number of positions = 1205 , Maximum depth = 2935 m) ## Reading 58AA_CTD_2009.nc : Number of positions = 960 , Maximum depth = m) ## Reading 58AA_CTD_2010.nc : Number of positions = 1413 , Maximum depth = 3390 m) ## Reading 58AA_CTD_2011.nc : Number of positions = 985 , Maximum depth = m) ## Reading 58AA_CTD_2012.nc : Number of positions = 1114 , Maximum depth = 3058 m) ## Reading 58AA_CTD_2013.nc : Number of positions = 1102 , Maximum depth = 2990 m) ## Reading 58AA_CTD_2014.nc : Number of positions = 966 , Maximum depth = m) ## Reading 58AA_CTD_2015.nc : Number of positions = 764 , Maximum depth = m) ## Reading 58AA_CTD_2016.nc : Number of positions = 559 , Maximum depth = m)

  day of each month of the dataset smon = seq(from = som(dlim[1]), to = eom(dlim[2]), by="1 month") # Calculate last day of each month of the dataset emon = do.call("c", lapply(smon,eom)) cat(get_title("The dataset period is:",dlim)) ## The dataset period is:(2002-03-07 => 2016-10-16)

  db = remove_sel(db) db = apply_sel(db, depth_lim = c(0., 10)) ## New number of active samples = 117756 ( out of 5080116 ) In a 2D aerial view, we display the previously selected Temperature values and the countries borders: inquire the database for the range of Salimity values: Salinity = db.extract(db, "Salinity", flag.compress = TRUE) hist(Salinity, breaks = 100, main = "Histogram of Salinity") of values = ", range(Salinity, na.rm = TRUE), "\n") ## Range of values = 0.1694 38.7109 We combine the previous selection with a new selection for removing Salinity outliers: db = apply_sel(db, z_lim = c(22, 39), z_var = "Salinity") ## New number of active samples = 116836 ( out of 5080116 ) Conductivity for 2010's measures at a depth around 100m (previous selection is cancelled first): correlation between Conductivity and Temperature. As a result, we also obtain the correlation coefficient. title = "Correlation between Temperature and Conductivity\n(Year 2010 at 100m depth)" corval = correlation(db, "Temperature", "Conductivity", flag.regr = TRUE, title = title)

  db, depth_lim = c(0., 50), dates_lim = datelim) ## New number of active samples = 1082 ( out of 5080116 ) dbstat = stats_grid(db, "Temperature", fun = "count") title = get_title("# data points by cell (<50m)", datelim) display_stat(dbstat, title = title)

  = c(paste(year, "-01-01", sep = ""), paste(year, "-12-31", sep = ""of Temperatures for", year, "=", zlim, "\n") ## Range of Temperatures for 2007 = -1.8927 13.7984 First three months of the year datelim = c(paste(year, "-01-01", sep = ""), paste(year, "-03-31", sep = "")) db = remove_sel(db) db = apply_sel(db, dates_lim = datelim, verbose=FALSE) cat("Temperature in First trimester of", year, "\n") ## Temperature in First trimester of 2007 res = interpol_var(db, depth = depth, depth_tol = depthtol, var = var, mesh = mesh) ## New number of active samples = 142 ( out of 5080116 ) ## Range of Information = -1= res, flag.estim = TRUE, xlim = xlim, ylim = ylim, zlim = zlim) datelim = c(paste(year, "-04-01", sep = ""), paste(year, "-06-30", sep = "")) db = remove_sel(db) db = apply_sel(db, dates_lim = datelim, verbose=FALSE) cat("Temperature in Second trimester of", year, "\n") ## Temperature in Second trimester of 2007 res = interpol_var(db, depth = depth, depth_tol = depthtol, var = var, mesh = mesh) ## New number of active samples = 53 ( out of 5080116 ) ## Range of Information = -1= res, flag.estim = TRUE, xlim = xlim, ylim = ylim, zlim = zlim) datelim = c(paste(year, "-07-01", sep = ""), paste(year, "-09-30", sep = "")) db = remove_sel(db) db = apply_sel(db, dates_lim = datelim, verbose=FALSE) cat("Temperature in Third trimester of", year, "\n") ## Temperature in Third trimester of 2007 res = interpol_var(db, depth = depth, depth_tol = depthtol, var = var, mesh = mesh) ## New number of active samples = 186 ( out of 5080116 ) ## Range of Information = -1= res, flag.estim = TRUE, xlim = xlim, ylim = ylim, zlim = zlim) datelim = c(paste(year, "-10-01", sep = ""), paste(year, "-12-31", sep = "")) db = remove_sel(db) db = apply_sel(db, dates_lim = datelim, verbose=FALSE) cat("Temperature in Fourth trimester of", year, "\n") ## Temperature in Fourth trimester of 2007 res = interpol_var(db, depth = depth, depth_tol = depthtol, var = var, mesh = mesh) ## New number of active samples = 69 ( out of 5080116 ) ## Range of Information = -1= res, flag.estim = TRUE, xlim = xlim, ylim = ylim, zlim = zlim) display the Standard Deviation of the Estimation Error for the previous last trimester interpolation (could be done after each interpolation and would give different results).

  Return the last day of the month from a given date • apply_sel: Apply a selection on the Db by masking samples falling outside from a given interval (Depth, Longitude, Latitude, Dates, Variable values). Accepted variable names are: Temperature, Conductivity and Salinity.

	• remove_sel: Remove all selection variables from the Db.
	• get_xlim: Get integer Longitude limits of the given [filtered] Db
	• get_ylim: Get integer Latitude limits of the given [filtered] Db
	• get_tlim: Get time limits of the given [filtered] Db
	• get_zlim: Get range values for a given variable of the [filtered] Db
	• get_title: Return a suffix for plot titles which indicates the period covered by a [filtered] Db
	• create_grid: Create a grid centered on the data from the given [filtered] Db with a given square mesh
	• display_grunit: Display a grid centered on the data from the [filtered] Db with a given square mesh
	into the current plot
	• display_var: Display the variable of interest from the given [filtered] Db
	• display_stat: Display the statistics map coming from the function stats_grid
	• display_result: Display the estimated results coming from the function interpol_var and overlay
	data in the same plot

• eom:

• interpol_var: Interpolate a variable on a 2D grid at a given depth from a [filtered] Db

• stats_grid: Generate a statistics map for a given variable from a [filtered] Db

Perspectives

• Several perspectives can be imagined for this showcase script:

-Tackling the data in a 3D approach in order to account for any possible vertical trend. Specific display would be welcomed. -Using a secondary exhaustive information (i.e. satellite image) in order to improve the knowledge provided by the measures: external drift concept. -Using simulations (instead of kriging) in order to derive maps of quantities which are not linearly related to the data: Probability that the temperature passes a given limit


Jorge Martinez-Gil

Jose F Aldana-Montes

Smart Combination of Web Measures for Solving Semantic Similarity Problems

Keywords: Similarity measures, Web Intelligence, Web Search Engines, Information Integration

Purpose

Semantic similarity measures are very important in many computer related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text clustering have used some semantic similarity measures in the past. Despite the usefulness of semantic similarity measures in these applications, the problem of measuring similarity between two text expressions remains a key challenge.

Design/methodology/approach

In this article, we propose an optimization environment to improve the existing techniques that use the notion of co-occurrence and the information available on the Web to measure similarity between terms.

Findings

Experimental results on Miller & Charles and Gracia & Mena benchmark datasets show that the proposed approach is able to outperform classic probabilistic web-based algorithms by a wide margin.

Originality/value

We present two main contributions:

We propose a novel technique which beats classic probabilistic techniques for measuring semantic similarity between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines.

We evaluate our approach on the Miller & Charles and Gracia & Mena benchmark datasets and compare it with existing probabilistic web extraction techniques.

Introduction

The study of semantic similarity between terms is an important part of a lot of computer related fields [START_REF] Zhu | Semantic-distance based evaluation of ranking queries over relational databases[END_REF]. Semantic similarity between terms changes over time and across domains. The traditional approach to solve this problem has consisted of using manually compiled taxonomies such as WordNet [START_REF] Budanitsky | Evaluating WordNet-based Measures of Lexical Semantic Relatedness[END_REF]. The problem is that a lot of terms (proper nouns, brands, acronyms, new words, and so on) are not covered by dictionaries; therefore, similarity measures that are based on dictionaries cannot be used directly in these tasks [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. However, we think that the great advances in web research have provided new opportunities for developing more accurate solutions.

In fact, with the increase of larger and larger collections of data resources on the World Wide Web (WWW), the study of web extraction techniques has become one of the most active areas for researchers. We consider that techniques of this kind are very useful for solving problems related to semantic similarity because new expressions are constantly being created and also new senses are assigned to existing expressions [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. Manually maintaining databases to capture these new expressions and meanings is very difficult, but it is, in general, possible to find all of these new expressions in the WWW [START_REF] Yadav | A conceptual model for user-centered quality information retrieval on the World Wide Web[END_REF]. Therefore, our approach considers that the chaotic and exponential growth of the WWW is the problem, but also the solution. In fact, we are interested in three characteristics of the Web:

 It is one of the biggest and most heterogeneous databases in the world. And possibly the most valuable source of general knowledge. Therefore, the Web fulfills the properties of Domain Independence, Universality and Maximum Coverage proposed in [START_REF] Gracia | Web-Based Measure of Semantic Relatedness[END_REF].

 It is close to human language, and therefore can help to address problems related to natural language processing.

 It provides mechanisms to separate relevant from non-relevant information or rather the search engines do. We will use these search engines to our benefit.

One of the most outstanding works in this field is the definition of the Normalized Google Distance (NGD) [START_REF] Cilibrasi | The Google Similarity Distance[END_REF]. This distance is a measure of semantic relatedness derived from the number of hits returned by the Google search engine for a given (set of) keyword(s). The idea behind this measure is that keywords with similar meanings from a natural language point of view tend to be close according to the Google distance, while words with dissimilar meanings tend to be farther apart. In fact, Cilibrasi and Vitanayi (2007) state: "We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we used the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases''. Our work is about those web search engines; more specifically, we are going to use not only Google, but a selected set of the most popular ones.

In this work, we are going to mine the Web, using web search engines to determine the degree of semantic similarity between terms. It should be taken into account that under no circumstances data from experiments presented in this work can be considered as a demonstration that one particular web search engine is better than another or that the information it provides is more accurate. In fact, we show that the best results are obtained when weighting all of them in a smart way. Therefore, the main contributions of this work are:

 We propose a novel technique which beats classic probabilistic techniques for measuring semantic similarity between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines. The smart combination is obtained using an elitist genetic algorithm that is able to adjust the weights of the combination formula in an efficient manner.

 We evaluate our approach on the Miller & Charles [START_REF] Miller | Contextual correlates of semantic similarity[END_REF] and [START_REF] Gracia | Web-Based Measure of Semantic Relatedness[END_REF] benchmark datasets and compare it with existing probabilistic web extraction techniques.

The rest of this work is organized as follows: Section 2 describes several use cases where our work can be helpful. Section 3 describes the preliminary technical definitions that are necessary to understand our proposal. Section 4 presents our contribution which consists of an optimization schema for a weighted combination of popular web search engines. Section 5 shows the data that we have obtained from an empirical evaluation of our approach. Section 6 discusses the related works and finally, Section 7 presents the conclusions and future lines of research.

Use Cases

Identifying semantic similarities between terms is not only an indicator of mastery of a language, but a key aspect in a lot of computer-related fields too. It should be taken into account that semantic similarity measures can help computers to distinguish one object from another, group them based on the similarity, classify a new object into the group, predict the behavior of the new object or simplify all the data into reasonable relationships. There are a lot of disciplines where we can get benefit from these capabilities. For example, data integration, query expansion, tag refactoring or text clustering. Now, we are going to explain why.

Data integration

Nowadays data from a large number of web pages are collected in order to provide new services. In such cases, extraction is only part of the process. The other part is the integration of the extracted data to produce a coherent database because different sites typically use different data formats [START_REF] Halevy | Data integration: The teenage years[END_REF]. Integration means to match columns in different data tables that contain the same kind of information (e.g., product names) and to match values that are semantically equivalent but represented differently in other sites (e.g., "cars" and "automobiles"). Unfortunately, only limited integration research has been done so far in this field.

Query Expansion

Query expansion is the process of reformulating queries in order to improve retrieval performance in information retrieval tasks [START_REF] Vechtomova | Query expansion with terms selected using lexical cohesion analysis of documents[END_REF]. In the context of web search engines, query expansion involves evaluating what terms were typed into the search query area and expanding the search query to match additional web pages. Query expansion involves techniques such as finding synonyms of words (and searching for the synonyms as well) or fixing spelling errors and automatically searching for the corrected form or suggesting it in the results, for example.

Web search engines invoke query expansion to increase the quality of user search results. It is assumed that users do not always formulate search queries using the most appropriate terms. Appropriateness in this case may be because the system does not contain the terms typed by the user.

Tag refactoring

Nowadays the popularity of tags in websites is increased notably, but its generation is criticized because its lack of control causes it to be more likely to produce inconsistent and redundant results. It is well known that if tags are freely chosen (instead of taken from a given set of terms), synonyms (multiple tags for the same meaning), normalization of words and even, heterogeneity of users are likely to arise, lowering the efficiency of content indexing and searching contents [START_REF] Urdiales-Nieto | MaSiMe: A Customized Similarity Measure and Its Application for Tag Cloud Refactoring[END_REF] . Tag refactoring (also known as tag cleaning or tag gardening) is very important in order to avoid redundancies when labeling resources in the WWW.

Text clustering

Text clustering is closely related to the concept of data clustering. Text clustering is a more specific technique for unsupervised document organization, automatic topic extraction and fast information retrieval and filtering [START_REF] Song | Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures[END_REF].

A web search engine often returns many web pages in response to a broad query, making it difficult for users to browse or to identify relevant information. Clustering methods can be used to automatically group the retrieved web pages into a list of logical categories.

Text clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Examples of text clustering include web document clustering for search users.

Technical Preliminaries

Given two terms a and b, the problem which we are addressing consists of trying to measure the semantic similarity between them. Semantic similarity is a concept that extends beyond synonymy and is often called semantic relatedness in literature. According to [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]; a certain degree of semantic similarity can be observed not only between synonyms (e.g. lift and elevator), but also between meronyms (e.g. car and wheel), hyponyms (leopard and cat), related words (e.g. blood and hospital) as well as between antonyms (e.g. day and night) [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. In this work, we focus on optimizing web extraction techniques that try to measure the degree of synonymy between two given terms using the information available on the WWW.

These are the definitions for the concepts that we are going to use:

Definition 1 (Similarity measure). A similarity measure sm is a function sm : µ 1 x µ 2  R that associates the similarity of two input terms µ 1 and µ 2 to a similarity score sc є R in the range [0, 1] which states the confidence for the relation µ 1 and µ 2 to be true .

In this way, a similarity score of 0 stands for complete inequality of the input terms and 1 for the semantic equivalence of µ 1 and µ 2.

Definition 2 (Hit). Hit (also known as page count) is an item found by a search engine to match specified search conditions. More formally, we can define a hit as the function hit: expr  N which associates an expression to a natural number which ascertains the popularity of expr in the subset of the WWW indexed by the search engine.

A value of 0 stands for no popularity and the bigger the value, the bigger its associated popularity. Moreover, we want to remark that the function hit has many possible implementations. In fact, every web search engine implements it a different way.

Example 1. (Similarity measures based on web hits).

If we look at the literature, we can find a lot of similarity measures. For example, measures based on hits which are a kind of similarity measure which are calculated based on the co-occurrence (Dagan et al., 2009) of the terms in the WWW, thus, the number of hits for each individual term to be compared and their conjunction. Some of the most popular measures of this kind are: Pointwise Mutual Information (PMI) [START_REF] Turney | Mining the web for synonyms: Pmi-ir versus lsa on toefl[END_REF], Dice, Overlap Coefficient or Jaccard [START_REF] Manning | Foundations of Statistical Natural Language Processing[END_REF], to cite some of them. When these measures are used on the Web, it is necessary to add the prefix Web-; WebPMI, WebDice, and so on. All of them are considered probabilistic because given a web page containing one of the terms a or b, these measures try to compute the probability of that web page also containing the other term. These are their corresponding formulas:

𝑊𝑒𝑏𝑃𝑀𝐼 (𝑎, 𝑏) = 𝑝(𝑎, 𝑏) 𝑝𝑎 • 𝑝(𝑏) 𝑊𝑒𝑏𝐷𝑖𝑐𝑒𝑎, 𝑏 = 2 • 𝑝𝑎, 𝑏 𝑝𝑎 + 𝑝𝑏 𝑊𝑒𝑏𝑂𝑣𝑒𝑟𝑙𝑎𝑝𝑎, 𝑏 = 𝑝𝑎, 𝑏 min 𝑝𝑎 , 𝑝𝑏 𝑊𝑒𝑏𝐽𝑎𝑐𝑐𝑎𝑟𝑑𝑎, 𝑏 = 𝑝𝑎, 𝑏 𝑝𝑎 + 𝑝𝑏 -𝑝a, 𝑏
On the WWW, probabilities of term co-occurrence can be expressed by hits. In fact, these formulas are measures for the probability of co-occurrence of the terms a and b [START_REF] Cilibrasi | The Google Similarity Distance[END_REF]. The probability of a specific term is given by the number of hits returned when a given search engine is presented with this search term divided by the overall number of web pages possibly returned. The joint probability p(a, b) is the number of hits returned by a web search engine, containing both the search term a and the search term b divided by the overall number of web pages possibly returned. Estimation about the number of web pages possibly returned by a search engine has been studied in the past [START_REF] Bar-Yossef | Random sampling from a search engine's index[END_REF]. In this work, we set the overall number of web pages as 10 10 according to the number of indexed pages reported in [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF].

Despite its simplicity, using hits as a measure of co-occurrence of two terms presents several advantages. It is surprisingly good at recognizing synonyms because it seems to be empirically supported that synonyms often appear together in web pages [START_REF] Cilibrasi | The Google Similarity Distance[END_REF]. Moreover, if the search terms never occur (or almost never) together on the same web pages, but do occur separately, this kind of measure will present very low similarity values.

Contribution

Traditionally web extraction techniques which are based on the notion of web hits have used a given web search engine (frequently Google) in order to collect the necessary information for their purposes. We thought that there was no compelling reason for considering Google the most appropriate web search engine in order to collect information and we began to use other search engines.

When we collected and processed the data from a wide variety of search engines, we saw that Google was the best source. However, we discovered that the average mean of the results was better than the results from Google. So we understood that maybe an optimized combination of those search engines could be even better than the average means. Based on this, we designed an experiment in order to test our hypothesis.

Figure 1 shows the solution proposed for the computation of the optimized web extraction approaches. The key of the architecture is an elitist genetic algorithm (i.e. an algorithm which selects the better individuals in each iteration), with a small mutation rate which generates a vector of numerical weights and associates them to their corresponding web search engine. The final solution can be expressed in the form of a numeric weight vector. It should be taken into account that the output of this architecture is a function that tries to simulate the behavior of the (group of) human(s) who solve the input benchmark dataset.

For the implementation of the function hit, we have chosen the following search engines from among the most popular in the Alexa ranking (Alexa, 2010): Google, Yahoo!, Altavista, Bing, and Ask. It should be taken into account that our approach does not include the automatic selection of appropriate web search engines because it assumes that all of them are offered initially, and those which may be associated with a weight of 0 will be automatically deselected.

The vector of numerical weights is encoded in binary format in each of the genes belonging to the domain of the genetic algorithm. For example, a vector of 20 bits can represent 1 weight of 20 bits, 2 weights of 10 bits, 4 weights of 5 bits, or 5 weights of 4 bits. It is necessary to implement a function to convert each binary weight into decimal format before to calculate the fitness for the weight vector. The number of decimal possibilities for each weight is 2 bits , for example, in case of choosing a vector of 5 weights of 4 bits, it is possible to represent 5 weights with 2 4 = 16 different values. These different values can range from 0 to 15, from 8 to -7, from 0 to 1 (where ticks have double precision) and so on. This depends of the implementation for the conversion from binary format into decimal format.

The comparison between the benchmark datasets and our results is made using the Pearson's Correlation Coefficient, which is a statistical measure which allows comparing two matrices of numeric values. Therefore the results can be in the interval [-1, 1], where -1 represents the worst case (totally different values) and 1 represents the best case (totally equivalent values). Our approach is efficient and scalable because it only processes the snippets (no downloading of web pages is necessary) for the result pages from the web search engines. On the other hand, scalability is given by our genetic algorithm which allows us to expand the set of web search engines without causing any kind of technical problem. This is would not be possible if we would use a brute force strategy, for example.

It should be taken into account that problems related to the speed of the process, and more importantly, to the restrictions on the maximum number of allowed queries per engine, are solved by means of a preliminary phase which loads the results retrieved from the search engines into appropriate data structures. We consider that this information is valid for a period of 7 days, because the content indexed by the search engines growths dynamically.

Evaluation

In this section, we are going to show an evaluation of our approach. The section is divided in a) a preliminary study to choose the appropriate parameters for configuring the optimization environment, b) the empirical data that we have collected from the experiments, c) a study on the quality of the optimized function, d) the effect of the operators on the final result and finally, e) the information necessary to repeat the experiments.

Preliminary study

We are going to do a preliminary study of the configuration parameters for the environment.

-For the number of genes per chromosome we have selected such values as 5, 10 and 20. A study using a t-Test distribution has shown us that that the differences between samples obtained by means of these parameters are not statistically significant. Therefore, we have selected 20 genes per chromosome. -For the number of individuals in the population, we have selected such values as 20, 50 and 100. Again, a t-Test statistical distribution has shown that the differences between these samples are not statistically significant. We have selected a population of 100 individuals. -Related to crossover and mutation fraction, we have chosen a high value for the crossover between genes and, a small percentage for mutations, because we wish a classical configuration for the genetic algorithm. -Related to the combination formula, our preliminary study has shown us that the weighted sum is better that the weighted product and the weighted square.

After ten independent executions, we noticed that the genetic algorithm did not improve the results beyond the 200th generation, so we have set a limit of 200 generations for the algorithm. The final results are computed by means of the average mean from these 10 generations. We are going to evaluate our approach using the Miller & Charles benchmark dataset which is a dataset of term pairs rated by a group of 38 human beings [START_REF] Miller | Contextual correlates of semantic similarity[END_REF]. Term pairs are rated on a scale from 0 (no similarity) to 4 (complete similarity). Miller & Charles dataset benchmark is a subset of Rubenstein & Goodenough original benchmark dataset of 65 term pairs [START_REF] Rubenstein | Contextual correlates of synonymy[END_REF]. Although Miller & Charles experiment was carried out many years later than Rubenstein & Goodenough, Bollegala et al. (1965) state that two sets of ratings are highly correlated [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF]. Therefore, Miller & Charles ratings can be considered as a good benchmark dataset to evaluate solutions that involve semantic similarity measures.

Results

In this subsection we are going to present the empirical results that we have obtained from our experiments. It should be taken into account that all figures, except those for the Miller & Charles ratings, are normalized into values in [0, 1] range for ease of comparison. Pearson's correlation coefficient is invariant against a linear transformation [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF].

Table 1 shows the collected data using several search engines for solving the Miller & Charles benchmark dataset. The measure that we have used is NGD (Cilibrasi & Vitany, 2007). As we commented previously, Google is the best search engine for our purposes; however, the average mean and the median present even better results. Other kinds of statistical measures such as mode, maximum and minimum have been considered because their associated results have not been better. Figure 2 shows us the behavior of the average mean in relation to the Miller & Charles benchmark dataset. As it can be seen the behavior is quite similar, however, it can be improved by using more elaborated formulas than the average mean, as we show in our second experiment. Meanwhile, Table 2 shows us the results from several probabilistic web extraction algorithms described previously. The data represent the numerical values obtained before and after the optimization process. The classic score is obtained using only the Google search engine, while the optimized score is obtained by means of the Pearson's Correlation Coefficient obtained after 200 generations from an elitist genetic algorithm that tries to maximize the weighted sum of some of the most popular web search engines. Lastly, Figure 3 represents a histogram with the results that clearly show us that our approach significantly outperforms classic probabilistic web extraction techniques. In some cases, there is an improvement of more than two times the original score. Although our hypothesis has been tested using measures based on web hits, we see no problem in using other kind of web extraction techniques, and thus, the optimization environment can be used to optimize other kinds of web extraction techniques if these techniques are susceptible to be parameterized.

Quality of the obtained function

Maybe the most important fact of this work is to determine if the trained optimal weights for the previous dataset can maintain the same level of improvement for other term pairs. In order to clarify this, we are going to use the function optimized using the Miller & Charles benchmark dataset for solving a different benchmark. We have chosen the Gracia & Mena benchmark dataset [START_REF] Gracia | Web-Based Measure of Semantic Relatedness[END_REF]. This benchmark dataset is similar to our previous dataset. It contains 30 pairs of English nouns, where some kind of relationship are present in most of them: similarity (person, soul), meronymy (hour, minute), frequent association (penguin, Antarctica), and others.

As it can be seen in Table 3, quality of the optimized function when solving the Gracia-Mena benchmark dataset is very good, or at least, it is better to use the optimized function that the classical approach.

Effect of the operators

In our experiments we have chosen the weighted sum because the results are, in general better, than the results for the weighted product or the weighted square. The effects of the operators in the optimization environment can be seen in the Table 4.

The weighted sum is the most appropriate operator in all cases, the reason is that values are always better that those obtained using a weighted product or a weighted square. This is the reason why we have chosen the weighted sum as the operator for our optimization environment. As future work, we can try to test the effect of other kinds of statistical measures such as: median, mode, maximum or minimum for the values retrieved from the different search engines, and so on. These measures have not been considered in this work, because they have not an associated numeric value which can be optimized using our approach.

Data to repeat the experiments

Related to the conditions of the experiment, we have used:

As web search engines for implementing the function Hit: {Google, Ask, Altavista, Bing, Yahoo}

The elitist genetic algorithm has been configured taking the following parameters into account 1 :

-20 genes per chromosome -A population of 100 individuals -0.98 for crossover fraction -0.05 for mutation fraction -We allow 200 generations -The goal is to optimize the weighted sum

We have chosen the weighted sum because the results are, in general better, that the results for the weighted product.

It should be taken into account that in case of repeating the experiments, results from the experiments can vary slightly along the time because the content indexed by the web search engines is not static.

Related Work

In addition to its multiple applications in the Natural Language Processing field, it is widely accepted that similarity measures are essential to solve many problems such as classification, clustering, and retrieval problems. For this reason, several works have been developed over the last few years proposing different ways to measure semantic similarity [START_REF] Budanitsky | Evaluating WordNet-based Measures of Lexical Semantic Relatedness[END_REF]. According to the sources exploited and the way in which they are used, different families of methods can be identified. These families are: Taxonomies of concepts, Featurebased approaches, and the Web as corpus paradigm.

Related to taxonomies of concepts, the most popular method for calculating similarity between two words consists of finding the length of the shortest path connecting the two words in a taxonomy [START_REF] Budanitsky | Evaluating WordNet-based Measures of Lexical Semantic Relatedness[END_REF]. If a word has two o more meanings, then multiple paths may exist between the two words. In such cases, it is possible to choose the shortest path between any two meanings of the words (optimistic approach) or the largest path between them (pessimistic approach). A problem frequently acknowledged with this approach is that it relies on the notion that all links in the taxonomy represent uniform distances.

An advantage of our method compared to the taxonomy based semantic similarity measures is that our method requires no taxonomies for computing the semantic similarity. Therefore, the proposed method can be applied in many tasks where such taxonomies do not exist or are not up-to-date.

Related to feature-based approaches, it is possible to estimate semantic similarity according to the amount of common and non common features [START_REF] Petrakis | X-Similarity:Computing Semantic Similarity between Concepts from Different Ontologies[END_REF]; by features authors typically consider taxonomical information found in an ontology and concept descriptions retrieved from dictionaries.

The problem of feature-based approaches is that the feature detection stage requires features to be located accurately and reliably. This is a non-trivial task. On the other hand, it is not easy to determine if two words share a common feature, this is due to the problem of feature matching. Our approach does not require feature detection or matching.

Regarding the Web as a knowledge corpus [START_REF] Lapata | Web-based models of natural language processing[END_REF], unsupervised models demonstrably perform better when n-gram counts are obtained from the Web rather than from other corpus [START_REF] Keller | Using the web to obtain frequencies for unseen bigrams[END_REF]. In fact, Resnik and Smith extracted sentences from the Web to create parallel corpora for machine translation [START_REF] Resnik | The web as a parallel corpus[END_REF]. We have identified two research lines related to the use of web measures: 1) measures based on web hits, and 2) measures based on text snippets.

-Regarding web hits, we have seen that one of the best measures was introduced by Turney and it consists of a point-wise mutual information measure using the number of hits returned by a web search engine to recognize synonyms [START_REF] Sanchez | Ontology-driven web-based semantic similarity[END_REF]. On the other hand, Bollegala et al. have proposed several works which use web hits for measuring semantic similarity [START_REF] Bollegala | Measuring semantic similarity between words using web search engines[END_REF], mining personal names aliases [START_REF] Bollegala | Mining for personal name aliases on the web[END_REF], or measuring relational similarity [START_REF] Bollegala | Measuring the similarity between implicit semantic relations from the web[END_REF].

-Related to text snippets, it should be taken into account that snippets are brief windows of text extracted by a search engine around the query term in a document, provide information regarding the query term. Works addressing this issue were proposed by Sahami and Heiman (2006) who measured the semantic similarity between two queries using snippets returned for those queries. For each query, they collect snippets from a search engine and represent each snippet as a TF-IDF-weighted vector. Or Chen et al. who proposed a double-checking model using text snippets returned by a web search engine to compute semantic similarity between words [START_REF] Chen | Novel association measures using web search with double checking[END_REF].

Conclusions

In this work, we have presented a novel approach that generalizes and extends previous proposals for determining the semantic similarity between terms using web search engines.

The optimization environment consists of several web extraction techniques which use a selected set of simple web search engines (instead of a single web search engine) which are combined in order to achieve satisfactory results.

We have provided an analysis of the most popular algorithms for extracting web information by using web hits, and characterized their relative applicability as black boxes in our optimization environment. It is necessary to bear in mind that the success of our approach depends largely on the kind of the underlying simple web search engines included and the heterogeneity and soundness of the benchmark datasets for determining their associated weights.

We have implemented a prototype for our optimization schema following an approach based on Genetic Algorithms that is highly scalable, thus, it can be expanded with a lot of simple web search engines. Our proposal has been optimized using a widely used benchmark dataset [START_REF] Miller | Contextual correlates of semantic similarity[END_REF] and applied to solve another widely used but different benchmark dataset [START_REF] Gracia | Web-Based Measure of Semantic Relatedness[END_REF]. We have shown us that our approach significantly outperforms the classical probabilistic techniques by a wide margin when solving the two datasets.

As future work, we propose a comparison of the knowledge provided by online encyclopedias and that provided by the web search engines. The idea behind this proposal is to use not only simple measures liked those based on web hits, but to benefit from the structured nature of these encyclopedias in order to use and optimize more complex web extraction techniques.

The goal is to further improve semantic similarity detection techniques. In this way, the semantic interoperability between people, computers or simply agents in the WWW might become true.

Figure 1 .

 1 Figure 1. Solution proposed for the computation of the semantic similarity measure. An elitist genetic algorithm generates a vector of numerical weights. The final solution can be expressed in the form of a numeric weight vector.

Figure 2 .

 2 Figure 2. Comparison between Miller & Charles benchmark dataset and average mean from several web search engines

Figure 3 .

 3 Figure 3. Existing techniques can be significantly improved by using our approach

Table 1 . Summary results for Miller & Charles benchmark using several search engines

 1

		Mil.Char. Google Ask	Altavista	Bing Yahoo Average Median
	cord-smile	0.13	0.05	0.25	1.00	0.00	0.00	0.26	0.05
	rooster-voyage	0.08	0.24	1.00	0.00	0.00	0.00	0.25	0.00
	noon-string	0.08	0.50	1.00	0.00	0.00	0.00	0.30	0.00
	glass-magician	0.11	1.00	1.00	1.00	0.00	0.01	0.60	1.00
	monk-slave	0.55	1.00	1.00	1.00	0.00	0.00	0.60	1.00
	coast-forest	0.42	1.00	1.00	1.00	0.02	0.01	0.61	1.00
	monk-oracle	1.10	1.00	1.00	1.00	0.00	0.00	0.60	1.00
	lad-wizard	0.42	0.04	1.00	1.00	0.00	0.00	0.41	0.04
	forest-graveyard	0.84	1.00	1.00	1.00	0.00	0.01	0.60	1.00
	food-rooster	0.89	1.00	1.00	1.00	0.00	0.00	0.60	1.00
	coast-hill	0.87	1.00	1.00	1.00	0.06	0.02	0.62	1.00
	car-journey	1.16	0.17	1.00	1.00	0.01	0.00	0.44	0.17
	crane-implement	1.68	0.14	1.00	0.00	0.00	0.00	0.23	0.00
	brother-lad	1.66	0.18	1.00	1.00	0.00	0.00	0.44	0.18
	bird-crane	2.97	1.00	1.00	1.00	0.13	0.09	0.64	1.00
	bird-cock	3.05	1.00	1.00	1.00	0.07	0.07	0.63	1.00
	food-fruit	3.08	1.00	1.00	1.00	0.03	0.01	0.61	1.00
	brother-monk	2.82	1.00	1.00	1.00	0.11	0.04	0.63	1.00
	asylum-madhouse	3.61	1.00	1.00	1.00	0.00	0.00	0.60	1.00
	furnace-stove	3.11	0.46	1.00	1.00	0.00	1.00	0.69	1.00
	magician-wizard	3.50	1.00	1.00	1.00	0.04	0.98	0.80	1.00
	journey-voyage	3.84	1.00	1.00	1.00	0.00	0.00	0.60	1.00
	coast-shore	3.70	1.00	1.00	1.00	0.02	0.08	0.62	1.00
	implement-tool	2.95	1.00	1.00	1.00	0.00	0.02	0.60	1.00
	boy-lad	3.76	1.00	1.00	1.00	0.18	0.02	0.64	1.00
	automobile-car	3.92	1.00	1.00	1.00	0.01	0.34	0.67	1.00
	midday-noon	3.42	1.00	1.00	1.00	0.07	0.00	0.61	1.00
	gem-jewel	3.84	1.00	1.00	1.00	0.39	0.05	0.69	1.00
	Correlation	1.00	0.47	0.26	0.35	0.43	0.34	0.61	0.54

Table 2 . Results from probabilistic web extraction algorithms before and after the optimization process for Miller & Charles Dataset Measure Classic Score Optimized Score Improvement

 2

	WebPMI	0.548	0.678	23.72 %
	WebDice	0.267	0.622	132.95 %
	WebOverlap	0.382	0.554	45.02 %
	WebJaccard	0.259	0.565	118.14 %

Table 3 . Quality of the optimized function when solving the Gracia-Mena benchmark. Gracia-Mena column shows the results from Google, Gracia-Mena' shows the results from the smart function obtained in the previous experiment Measure Gracia-Mena Gracia-Mena' Improvement

 3

	WebPMI	0.548	0.571	5.35 %
	WebDice	0.520	0.534	2.69 %
	WebOverlap	0.521	0.538	3.26 %
	WebJaccard	0.528	0.596	12.88 %

Table 4 . Effect of the operators in the final results from the optimization environment

 4

	Measure	Weighted Sum Weighted Product Weighted Square
	WebPMI	0.678	0.242	0.348
	WebDice	0.622	0.240	0.413
	WebOverlap	0.554	0.245	0.278
	WebJaccard	0.565	0.372	0.337


Peihong Huang
email: huangpeihong6488@sohu.com

Exploration on Causal Law of Understanding and Fusion Linking of Natural Language

Keywords: Natural language understanding, Perception, Interconnection, Prospection

To research the causation law of natural language understanding from its cognitive model is a kind of creative method which has been being developed recently. In this paper, the research situation of natural language understanding and interconnection are introduced, and furthermore we prospect its potential future affect to other areas.

Introduction

As we know humankind has a phenomenon which is interconnection. For example, if A is B's brother, and B is C's brother, we can know immediately A is C's brother. However, if apparent knowledge is not sufficient, machine wouldn't know it. The capability of interconnection makes people to comprehend from inference. This is because of capability of understanding. It is just because of the use of understanding that humankind can solve mathematic problems from simple to complex, can speak from babbling to creation of masterpiece which is rich of literary grace. However, in the area of NLP (Natural Language Processing), the main way of NLP is based on statistics. Nevertheless, when facing even further language translation and problem solving, the method based on statistics is not a magic bullet. On the contrary, method based on understanding when doing natural language processing can do it at ease. Different from psychology concepts, the theory of natural language understanding [START_REF] Huang | Formalization of natural language understanding[END_REF] [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF] defines the perception as the minimum element of feeling, and uses perception as the basic concept of the physical theory of understanding. As for the qualitative aspect of perception, since it is abstract, it needn't to be fully analogized at current stage. Instead, just extract the qualitative invariant part of perception and identify this part with symbols. This method can achieve the same effect as that can be gained from fully analogized implementation. We define perception element as: p Perception: so called perception, is the unit of consciousness, which is the minimum element which can be felt and which is also the minimum element of meaning, the qualitative invariant part of which is identified as: p 2

Understanding of logic and physical model of interconnection

Paper [START_REF] Huang | Formalization of natural language understanding[END_REF][2] presented that types of understanding include not only understanding of meaning but also understanding of logic. Understanding is the sense of certainty when the stimulus matches the set of perceptual patterns within the cognitive system, and the formula is:

) , ( ) ( x x g t m P w x u b   (1)
Among them, the external stimulation is x , let an understanding of stimulation x to be ) (x u ,the corresponding perceptual pattern is x m and the perceptual subset is to be x g P , the assembly perception set is to be g P , x g P  g P ,the matching between

x and m and its confidence function are to be w ,the certainty feeling is t b . The stimulation material consists of various parts, the understanding of it includes all parts of the understanding.Paper [START_REF] Huang | Formalization of natural language understanding[END_REF] gives a formula of the comprehensive understanding. To stimulation x , let the perception produced by it to be p , the comprehensive understanding of x is to be ) (x u c

; Set a stimulus x containing n sets of assembly perceptions and n sets of perceptual patterns , the corresponding assembly perception set and perceptual patterns are respectively

i x g P ) ( and ) ,..., 2 , 1 ( ) ( n i M i x 
,then comprehensive understanding of the x is:

      n i i x i x g n i i x g c M P w P u x u 1 1 ) ) ( , ) (( ) ) (( ) ( (2)
In order to comprehensively and systematically reveal the physical law of natural language understanding, paper [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF] presented the reliability and integrity theorems of the axiom system which is essential to pragmatic implication inference.

The important theorem about pragmatic implication presented and proved in Paper [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF] is also based on the foundation of the integrity theorem, and the inference of pragmatic implication is very important to the semantic computing, it provides the formalization operating procedures to semantic computing. The pragmatic implication of a sentence in the corresponding context is:

j s t j s s n j i i i s r j S h p p p M p ) ( ) ) ( ) ( ( ) ( , 1        (3)
Among them, an understood context contains a collection of sentences ,its sentence meaning is

j s P ) ( , its literal meaning is j s s P ) (
, and the deduced semantic is as

j s t P ) ( .
From the expression, the pragmatic implication is a deduction value in the sentence group context G. By taking a certain value of G, it is possible to determine a pragmatic extrapolation value in a context.

Understanding of logic is the prerequisite of interconnection

There is a close relationship between understanding of logic and interconnection. In paper [START_REF] Huang | A method of machinery inference learning based on text explaining[END_REF], we reveal how to implement reasoning understanding (understanding of logic) according to perception semantics dictionary within semantics sequence correspondent to language, and furthermore acquire new understandable belief. The results of these steps also further verify the theory of natural language understanding we found. Table 1 is one of the experimental examples from paper [START_REF] Huang | A method of machinery inference learning based on text explaining[END_REF], which is a sentence of a famous Chinese ancient poem of Tang Dynasty, by poet Li Bai. This example assume that the machine already has words: in front of the bed, bright moon, light, and has the perception semantic value, and machine can understand the semantics of these words according to the definition of understanding. Where, Mr is pattern set generated after context being understood, new pattern set filter function new(Mr) generates new pattern set M. Table 1. Computing example of Learning based on understanding.

Table 1 shows the formalization procedure of machine understanding learning of "bright moon light in front of the bed". It means that, is it feasible that "bright moon" and "light" are put together? The implication of "bright moon" shows that it can give out "light", then the meaning of "bright moon light" is the "light" shined by "bright moon", thus "bright moon light" is understandable. What is the comprehensive meaning of "in front of the bed" and "bright moon light"? Is it feasible that there is "bright moon light" "in front of the bed"? When "bright moon" shines in the sky, the "light" of it can illuminate to everywhere, thus it can illuminate to "in front of the bed". Therefore "bright moon light in front of the bed" is understandable. Storing after understanding is learning. Understanding is the basis and one of purposes of learning. So called reasoning of logic understanding includes deduction and induction reasoning based on perception semantics, new knowledge can only be acquired when logic understanding is implemented, thus interconnection effects can be gained.

Compared to statistics based machine learning, one of the advantages of understanding based learning approach is that its learning results have characteristics of robustness,because their learning results are fully logical.Secondly, its learning outcomes is grounded, because the result is in line with reality; thirdly,the outcomes of learning is understandable for human beings; fourthly, it is a small sample learning method. The prerequisite of using the method is that it requires careful description of common sense, and requires a comprehensive understanding. The process of comprehensive understanding is more complicated and more time-consuming than statistical method. So far, the study is still in its infancy,and many further researches are needed to do persistently.

The physical model of interconnection and its meaning

Interconnection is to fuse certain perception material and then generate new correct beliefs, thus interconnection is also known as fusion. A model of interconnection is presented in paper [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF], shown in Fig. 1. In paper [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF][4], interconnection is built on the basis of understanding, machine cannot implement interconnection if there is no understanding as the basis, and the knowledge in it is only the isolated hard knowledge. Since knowledge base system is built on the foundation of concepts, therefore, these knowledge can ' t be interconnected well. Differentiating, understanding and fusing concepts is fundamental roadmap to overcome these problems. To understand these concepts is useful to automatic learning and automatic usage.

In Fig. 1,When cognitive system observes Real World, it forms the perceiving Images of Real World, following-on through Instinct Mechanism of machinery understanding , judges whether it forms any function, which means to judge if it has values. If the function rule is true(i.e.

) (x e =1) , then cognitive system differentiates and segments out the Function Rules including Concepts(C01 , C02,... , Cmn ) /Knowledge(K01 , K02,... , Kmn ) .

In cognitive system,all Concepts(C01 , C02,... , Cmn ) /Knowledge(K01 , K02,.. ). These concepts and knowledge are in fusion linking state when cognitive system is thinking or using these concepts。

Prospection of understanding theory

During the course of language translation, supposing that the machine has already got some basic facts and knowledge, if the machine has the ability of interconnection, it will have the ability of analogy. It can interconnects the old material (content) again with perception elements, and can generate new beliefs according to new requirements, and thus can extract new experiences from old material constantly. This means if the machine grasps a typical interconnection, it will comprehend the whole category by analog. This process is just like paper making: Pound paper fiber and mixing fiber with water, and finally fuse them into fine pulp, then manufacture paper products meeting various kinds of requirements. Lack of ready-made and suitable knowledge is often a problem, thus machine-oriented interconnection is extremely significant.

On the other hand, since there is no limitation (restrains) of the magnitude of the concept particles of variable or status in Turing Machine Model, formalization system based on Turing Machine Model is normally local, and thus the fusion of systems is difficult. This limitation is manifested when programming. Generally a program is a formalization system. When programming, firstly the variables of various kinds of data structure are defined, and then the logical flow are defined or structured, and then variable mathematical operation or formal variation are implemented, and thus the final results are got and stored into the result variables. We can see that a program is consists of variables and limited operation instruction sequence. Program is just a formalization system which can stop within limited steps.

The particle size of variable (or status) can be changed. Size and unit can be element, or even can be big enough to the magnitude of a combined concept which is composed of multiple concepts. These concepts can be huge, and concepts of different systems are independent, thus the definition of variables also can be of huge quantity, this means human need to program huge quantity of program, and build huge quantity of formalization system, only in this way we can adapt to the change of the reality world.

After all, the limitation of Turing Machine Model is that it cannot fuse the knowledge it uses. The interconnection built on understanding will enhance the usage rate and usability of knowledge, and thus we outlook that our natural language understanding theory will impact software engineering, especially software production automation significantly.

The theory of natural language understanding can also be applied to network content security, spoken dialogue system, information retrieval, verification code recognition and voice content retrieval, etc [START_REF] Xiao | Research progress of network content security[END_REF][START_REF] Wu | Spoken dialogue system of tourism information[END_REF][START_REF] Zhang | Telecom business software design based on Natural Language Processing and speech recognition[END_REF][START_REF] Jiang | Design and implementation of universal demand retrieval data processing subsystem for Baidu video[END_REF][START_REF] Zhou | Overview of network content security[END_REF][START_REF] He | Research and development of natural language[END_REF][START_REF] Yin | Research on verification code recognition algorithm based on SVM[END_REF][START_REF] Cheng | Tourism website content search based on ontology[END_REF], and thus can incrementally fulfill the dream of The Imitation Game of Alan Turing.

Conclusion

Natural language understanding is a conclusion of one of the important rules about human language phenomenon. It is the same as physics that theory of natural language understanding can be summarized and extracted from the psychological and physical phenomenon. In the field of computer science, Turing Machine is its base, and the initial intention of Turing Machine is to analog thinking computationally. This is the nature of Turing Machine. Just as what is analyzed in paper [START_REF] Huang | NLU-A Logic theory on machine perceiving language of humankind[END_REF], the interconnection ability of original Turing Machine Model is limited. Natural language understanding theory [START_REF] Huang | Formalization of natural language understanding[END_REF][2] is an extension to Turing Machine Model. Just like that Turing machine liberate human being from cumbersome computation, we believe that natural language understanding theory will liberate human being from cumbersome analogy of thinking which results in building of infinite formalization systems for human using Turing Machine. This is just the motivation of the natural language understanding theory research.

Fig. 1 .

 1 Fig. 1. Extended(Pulped) Turing Machine Model processed information based on perception semantics


Hu Meng
email: menghu@citycloud.org.cn

Yongsheng Pan

Lang Sun

Application and Implementation of Batch File Transfer in Redis Storage

Keywords: Redis, HDFS, Batch Processing, Distributed File System

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduce

File system is an important way to transmit and store information. The use of the scale continues to expand, such as the office system, mail, message system through which information can be shared and distributed quickly. Users in such applications, not only requires high-speed processing speed, but also requires the reliability of storage. Therefore, massive files in cloud storage research has important practical value. Massive file storage is generally based on HDFS. HDFS is a distributed file system, through the cheap multi-machine support large-scale data sets of large file storage, with strong scalability, and solve the storage problem of space constraints. Meantime, HDFS can provide high-throughput data access. It is ideal solution for largescale data set applications, and even in the case of error can guarantee the reliability of data storage. It assumes that the calculation elements and storage would fail, so it maintains multiple copies of the work data to ensure that they can be redistributed against the failed nodes. It works in parallel to ensure efficient processing. But the storage efficiency of small files in HDFS is not high. It uses NameNode to maintain the mapping of file path to the data block and the mapping of the data block to User DataNode, and also monitor DataNode heartbeat and maintain the number of data block copies. When a large number of small files stored in HDFS then the NameNode will run out of most of the memory, resulting in low storage efficiency, limiting the file access speed.

Taking into account the above-mentioned problems, we use a separate server with large memory to cache the data to be merged. It would improve the performance of the management node, and avoid the main server bottlenecks. The cache server uses Redis to store data. Redis is a memory-based high-performance Key / Value database. It writes updated data to disk or writes modified operation to additional log files periodically to ensure data persistence. And the Master-Slave synchronization provides a high availability and reliable platform to users. The first upload files cache in Redis, writing to disk operation only need to execute one time after merging the files which would reduce the times of disk I / O. And file uploading processes in memory which can provide a significant reduction of response time of file uploading.

Algorithm Summary of batch processing

The file storage scheme designed in this paper is to build an intermediate platform between users and HDFS system to handle the upload, query and delete operations of received files. As large files can be stored directly and efficiently in HDFS, the platform only process small files. Processing of files that larger than 32M would return a processing-received tag directly. Consolidated storage scheme as shown in Figure 1.

Users interact with the platform through socket. Redis is used to cache user files.

Caching files merge and store in the Sequence File of HDFS through the HDFS interface. The metadata records cache in Redis.

Fig. 1. Files consolidated storage solution

Storage structure

Redis as a file cache database, save the cache file content and metadata records. The cache structure design is as follows: The length of the file in the cache, that is, the total length of the file data stored in the RCF MH:DID Hash File information that update to metadata record after serialization. For example, MH: 12 store all the metadata structure of the folder identified as 12. Key for the file name, Value for the metadata SDIR:DID Hash Folder structure, Key for the folder, Value for the folder name DID Long Automatic growth of the folder identification The storage structure of HDFS platform is the Sequence File stored and combined under basic directory, named after timestamp. The Sequence File uses the sequential storage structure so that we can quickly locate the contents of the small files through the file location Store Position recorded in metadata corresponding to File Name. And, Sequence File uses Block compression to reduce disk usage and increase transmission speed. Block compression is a series of records, that is, the small files here, organized together, unified compressed into a Block. Block information mainly store: the number of records contained in the block, the set of the length of each record Value, and the set of the value of each record Value.

Storage platform implementation

According to the above design proposal, based on the load cost model, the file platform is divided into two parts: basic processing and background processing. Users process basic file operation such as upload, modify and delete through the platform interacted with Redis and Sequence File; The timer combined with the basic operation triggers the event to invoke background processing to ensure the reliability and speed of the system. When uploading a file, the received file is stored in the file cache RCF in Redis and the RCF Length of the file (RCFL) stored in the RCF is updated. Then, to determine the RCFL, if the length achieved the size for merge, a "merge file" message MF is send to the background processing module. When reading a file, the file will be returned directly if it exists in the upload buffer of Redis. Otherwise, the contents of the file will be read by the cache processing module and returned to the client. When deleting a file, first determine whether it exists in RCF. If true then delete it. Otherwise, the metadata of delete flag will be set to 0 and mark the file would be deleted.

Load Cost Model

As a complete system, not only to improve the efficiency of file storage, but also take into account the system load conditions. The load cost of existing server resources generally evaluated by the usage of independent CPU or memory. This statistics is not comprehensive. For example, high CPU usage will not affect the operation which only occupy high memory and disk I / O usage, and in actual use, the various types of resources requirements of the thread operation cannot be comprehensively evaluated either.

In order to make up for the shortage of resource statistics, this paper puts forward a load cost model which use the user's response time as an estimate criteria, based on the experimental analysis of statistical data to determine the cost of the formula, so to evaluate the effects of variety factors more reasonable. The process is: while the system is running normally, gather statistics and analysis the various types of resources, such as CPU, memory, disk I / O and other in real-time, record the response time of task processing threads and main customer service thread, to determine various factors. In this way, to avoid the lack of timeliness of traditional statistical estimates, the use of real-time computing can ensure reliable and comprehensive analysis with all kinds of environmental resources.

This paper combines the features of Sequence access and based on the GD-SIZE algorithm, calculate the cost H with the formula (1), and archive the small file cache replacement strategy.

Hi = N / S

(1)

The general GD-SIZE algorithm is: Each document in the buffer has a corresponding cost. When the document is brought into the buffer, the H value of the document is the reciprocal of the document size. When the replacement occurs, document which has the smallest H value Hmin is swapped out, and the H value of the remaining document becomes the H value before the replacement minus Hmin. According to the characteristics of Sequence File, reading the file in a single block may need to traverse many times. The value of H that GZ-SIZE algorithm used cannot actually reflect the cost of the document. The cost of the document has positive correlation with the traversed files number N for visiting the file. We can multiply the reciprocal of the file size S by N as the initial value of the GZ-SIZE algorithm, to achieve cache replacement.

Experiment

To establish the load cost model and determine the load cost formula, the influence of various factors on the response time of the main thread needs to be quantified. The coefficient of influence of the factor is obtained by the method of multiple linear regression analysis. In this system, CPU usage (C), memory usage (M), and disk I/O (D) have a major impact on performance, so they are used as dependent variables and uploading response time (T) as response variable, the multiple regression equation is expressed as:

T = k1C + k2M + k3D (2)
The specific operation is as follows: In the running nodes of the platform, execute multiple processes that have great impact to C, M and D to get different resource occupancy results, and gather the file upload time statistics. The results are as follows: .00 Regression analysis of the results can calculate that k1 = 0.257, k2 = 0.332, k3 = 0.103. In order to enable the user get responding within 100ms, the response time T calculated with C, M, and D should be less than 100ms as the expected threshold of the load. In the experimental environment, the value of k1, k2, k3 is input to the running configuration. When the background message MF is received, the load threshold is calculated by the formula.

In order to eliminate the impact of unstable factors (such as speed), randomly selected 10 small files in the standard HDFS and the use of optimization modules in the file system to upload, and ultimately get the cost time shown below:

Fig. 2. File upload time

It can be seen that the time for file uploading is significantly reduced by batch merging of files, which is reduced from an average of 453.1 ms with traditional way to an average of 52.3 ms by 88.45%. In the file uploading in a batch file, the implementation changes from receiving files through original HDFS memory and writing the disk later to receiving files by Redis memory directly, no longer need to wait for the slow disk I / O operation of writing. The upload speed is significantly improved.

Conclusion

In this paper, we consider the storage method of small files in HDFS, design and implement the small files storage optimization based on reliable HDFS file system.

Combining the Redis cache mechanism effectively reduces the memory usage of the NameNode node, the disk I / O is reduced compared to the traditional HDFS files merge method, speeding up the file uploading and acquiring speed in a large number of frequent file reads. It can be seen from the results of the experiment that the Redisbased HDFS file batch merge storage optimization method can improve memory utilization and speed up the file retrieval speed, and not affect the speed of file updating and querying, ensure the fast and reliable file operation and preservation.

Table 1 .

 1 Redis cache data storage structure

			System	HDFS
			Redis
	Name	Type	Description
	RCF	Hash	Cache the data of the file, including the contents of

Table 2 .

 2 Response time of the user request

	T/ms	C/%	M/%	D/(Blk_wrtn/s)
	49	13	33	17.03
	59	15	31	184.00
	92	63	21	188.00
	55	23	22	0.00
	57	12	21	0.00
	73	36	32	116.00
	51	45	43	8.00
	68	89	21	32