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2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

Using Ontology for Measuring Semantic Similarity for Question Answering

System IMuthukrishnan Ramprasath, 2Shanmugasundaram Hariharan I Assistant Professor, Department ofInformation Technology

2 Associate Professor, Department of Computer Science and Engineering IJ.1. Engineering and Technology, 2T,RP en¥in�ering

.college, T�ichy,Tamilnadu, India

E-mail: Imramprasath @gmall.com. [email protected]

Abstract-Semantic similarity is an essential concept that widen across various fields such as artificial intelligence, natural

language processing, information retrieval, relation extraction, document clustering and automatic data extraction. The study

proposed in this paper focus on semantic similarity and counter measure on question answering task. Despite the usefulness of

semantic similarity in these applications, finding the exact meaning between the words from question and answer pair has become a major challenge in question answering system. To recognize various relationships those exist between question and answer pair, measuring semantically is essential. We present

study using a match maker algorithm for similarity measurement between question-answer pair. The study

compares similarity measurement using match maker algorithm as against semantic similarity measure on Miller-Charles bench marked data set. This paper present study about different types

of algorithm used to measure the semantic similarity between the words and those result compare with match maker

algorithm. The experiments on real datasets shows matchmaker algorithm works better than web-based semantic similarity measure

Keywords: Semantic Similarity, Question answering system, Match Maker Algorithm

I. INTRODUCTION

Semantic similarity can be define as a concept whereby a set of terms within term lists are assigned a metric based on the similarity of their meaning. Semantic Similarity between the words can be achieved by defining topological similarity using ontologies to define a distance between words. Similarity acts as fundamental concept in theories of knowledge and behavior. Measuring semantic similarity between words play important role in a wide range of application such as natural language processing, information retrieval, web mining application and question answering system. Emergence of internet has created a way for creation of new word and existing words assigned with new senses. It is important to know the semantic relation between words to decide whether these words are semantically similar or not. For example 'lorry' and 'trucker' can be considered semantically similar because both are grouped under similar term 'vehicles'. Similarly 'donkey' and 'lorry' can be considered semantically similar because 'Lorries' and historically 'donkey' are used for transportation. Semantic relation such as A and B are used in automobile or A and B are used in transportation, exit between two words A and B in these example.

It is difficult to analyze each document separately and directly because of high growth rate and numerous documents on the web. We use search engine to measure the semantic similarity between the words or entities. Finding the

ISBN No. 97S-1-4673-204S-1112/$31.00©2012 IEEE

similarity sense between words is a significant concept that has been widely used in many research areas. The opposite of semantic distance is a semantic similarity. Several algorithms have been proposed for finding the similarity between the words. The discovery of semantic meaning for particular word is usually done by using semantic match maker algorithm which defines four degree of matching such as Exact, Plugin, Subsume and Fail. [16 On the other hand the method followed in these match making are only based on logic based reasoning is restricted to the determination of the subsumption relation among the concept of ontology. Match maker adopts Word Net as the concept of ontology. For instance two different nodes, their corresponding words in the ontology tree structure probably not the same even if they have similar semantic meaning.

Thomas Deselaers et al [17] has proposed visual and semantic similarity in image net. There is no standard frame work available for measuring the similarity between the words. OWL-S [16] is a de facto standard can be used to describe the semantic relation between two words, which is based on the OWL ontology language. Ontology is the important component in the process of semantic matchmaking. It can be used to infer additional information which has not been explicitly stated in on ontology. (M.D Boni and Manandhar) has treats each word pair separately, and thus cannot capture the semantic of the word as a whole. [2] (Gildea&juruafsky, 2002) has stated that the contribution of the semantic role in the question answering system is more significant. [2] Hahn et al. (2003) has proposed similarity between two representations is based on the Representational Distortion theory, which aims to offer a theoretical structure of similarity judgments. [3]

The computation of the semantic similarity among words can be classified in to three categories namely network distance approach, information content based approach, feature based approach. In this paper, we analyze different algorithms to find out semantic similarity between words. H. Chen, M. Lin, and Y. Wei [4], has proposed a doublechecking model using text snippets returned by a web search engine to calculate semantic similarity between words. We investigate semantic match maker algorithm to measure the similarity between the words or entities.

P. Moreda et al. [IS] has discussed open domain question answering system based on semantic information, semantic role and WordNet.Semantic match maker algorithm which consist of two major challenges namely how to propose a principled approach for measuring the semantic distance between words using ontology and to estimate the similarity

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between words by use of the semantic distance. Initially keyword based match is proposed to quantify the degree of match between words. Semantic distance will be used measure the distance between the words in the ontology. We performed experiments for evaluating the proposed match maker algorithm. The result shows that the proposed semantic match maker algorithm has better performance than the other algorithms likes co-occurrence double clicking model [4], sahami and heilman [2] (SH), and Normalized Google distance [7].

The rest of the paper is ordered as follows. In section 2 we discuss preceding works related to semantic similarity measures. The study of match maker algorithm describe in Section 3 we investigate a match maker algorithm to find similarity between words. Section 4 compares the proposed method in opposition to previous Web-based semantic similarity measures and numerous baselines on a standard data set. To evaluate the ability of proposed match maker algorithm in capturing the similarity between two words, we apply and test with real time data set. Section 5 present conclusion and future works.

II. RELATED WORK

The similarity measurement is derived from a set of assumption about similarity between words; it cannot define directly by formula. In recent times, many researches have been made on discovering semantic similarity between words in web correlated application. Existing work on semantic similarity mostly focus on the similarity between two articles, while semantic similarity between two words has attracted less attention. It's very hard to find semantic meaning for a particular word in many web related application because the incredible increase in information on the web.

In order to incorporate these semantics in the web ontology's concepts are used. ce zhang [11] has discussed the compact concept ontology based method to find the similarity between words also he presented new method of calculating similarity between article based on word net. Devis bianchini [12] has presented ontology based approach to support flexible and efficient match making between service description. Next he presented ontological framework for service modelling and discussed deductive similarity-based match making.

Ontologies play a key responsibility in the semantic web by providing vocabularies to corresponding word that can be used by applications to understand similarity meaning. [6] (Danushka Bollegala, Yutaka Matsuo) has proposed relational model to compute the similarity between the words by using snippets retrieved from the web search engine.(Cilibrasi and Vitanyi) [7] has discussed about Normalized Google Distance (NGD) which is used to find the distance metric between words using page counts retrieved from web search engine.

(Rada et al) [S] has proposed straight forward method to calculate the similarity between words is to find the length of the shortest path connecting two words. [S](Sahami and T. Heilman) calculate the similarity between words using snippets retrieved from the web search engine. [10] Danushka

Bollegala, has proposed an automatic method to estimate the semantic similarity between words or entities using web search engines. Stefan Dietzel, Alessio Gugliotta2 [20] has addressed semantic level mediation to support the Web Service selection problem, and said it will be used on identifying semantic similarities between entities across different Semantic web Services ontologies.

Yyuan-peng IiI and bao-Iiang lu2 [21] has discussed about semantic similarity problem in Gene Ontologies (GO) domain, and shows new way for mine more information from the gene ontology graph. Hector Llorens and P. Moredaet al. [IS] has presented evaluation for question answering system which describes how semantic role information and WordNet classes influences general open-domain question answering system. Albertoni R. and De Martino M [22] has proposed frame work to access the semantic similarity among instance within ontology. The goal of frame work used to define a susceptible measurement of semantic similarity.

Evgeniy Gabrilovich and Shaul Markovitch [23] has discussed about semantic relatedness computation of natural language text, and presented explicit semantic analysis novel method which is used to represent the meaning of text in high dimensional space of concepts derived from Wikipedia. P. Resnik [25] has presented a similarity measure using information content. He used WordNet as the taxonomy for measuring similarity.

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III. MATCH MAKER ALGORITHM

The concept of match maker algorithm used to quantify the matching degree between the words. The quantitatively ranked valued returned by the algorithm is used to represent the matching degree, and compare the ranked value with existing algorithm to find the better similarity between the words. This makes user easier to recognize the semantic meaning. The advantage of matchmaker is giving a ranked list of meaning for a particular word instead of rough categories.

A. Semantic Similarity

Explanation: A semantic similarity Sim: � XxX' [0, 1] is a function from pair of words to a real number between zero and one used to expressing their similarity between the words.

B. Semantic Distance

Semantic distance is defined as the minimum length of the relation path between the words in an ontology structure. Relation path is composed of relation defined in ontology. when constructing a relation path in ontology, we should consider the one way relation and two way relation between the words. Object property is a one way relation, superclassOf is as two way relations, because super class also has relation between two classes. To represent the semantic

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distance between two words in ontology we should think about the basic categories of exact, subsume, fail and plugin. These categories only used to compute the distance of two words. If there is no hierarchal relation between words, they will have an infinite distance, and will be classified in to fail categories. Simply define Semantic Distance is an extension to the relation of f our categories. Initially match maker used a key word-based matching to find the semantic meaning for a particular word which does not utilize the semantic information in the web. Hence we first define ontology model which is used to find the relationship between the words. Then we define semantic distance to measure the distance between the words in the ontology.

C. Ontology Concept

The performance of match maker algorithm is greatly dependent on the essential concept ontology from which we generate a weighted tree for each word. Although our match maker algorithm adopts word net as the concept ontology. Word Net does not fit very well with our algorithm. For instance, consider three words automobile, car, and computer, automobile and car are semantically very similar with each other while computer not relevant to them. In Word net the three words will be matched with three different nodes and thus the semantic relationship among them is will be ignored in match maker algorithm. The following section we discuss some algorithm which is used to measure the semantic distance between the words in ontology.

D. Algorithm used to compute semantic distance

The semantic distance can be computed by using relationships between the words defined in ontology. We discuss here some of algorithm used to define semantic distance. The below figure shows different words are linked in ontology. Assume that the relation between two words in ontology have same weights, no matter what kind of relation they have between the words.

Fig I.Sample ontology illustrating hierarchical relationship among words

Fig. 2 .The relations between words in ontology

The URD algorithm has the object property is a one way relation. The extension of Unweighted Relation Distance is called Weighted Relation Distance (WRD). WRD is used to give different weight to relation path exist between two words in ontology. The concept is stimulated by [13], which gives a computation of semantic distance between words. The work can be extended by taking object property relation in to account. It is difficult to compute the semantic distance between the words in WRD, because it does not take the depth of the hierarchy in ontology. Depth First Distance algorithm used to solve the problem in WRD by using [14] generalized Cosine-similarity measure. The formula is as follows:

depth(a) + depth(b) D FD (a, b) = --"-----'---'----"--"'---'-

2 x depth(LCA(a, b)) (1)

LCA (Lowest Common Ancestor) is the node of greatest that

is an ancestor for both a and b. The depth is calculated from

the beginning. For example in fig.2 depth (cup) = 1, depth

(hard disk) = 3. So DFD (S-ram,D-ram) = (3+3)/( 2x3) = 6/4

= 1.5, DFD (Ram, Mother board) = (2+2)/(2xI) = 4/2 = 2.

This implies distance between Ram and mother board is

greater than the distance between S-ram and D-ram.The

semantic similarity can also measured by using page counts

values. Web jaccard, web dice, overlap (simson) to compute

the semantic similarity using page counts. We use notation

'SeX)' to indicate the page counts for the query 'Q' in a

search engine. The Webjaccard coefficient between words

'WI ','W2', webjaccard (WI, W2) is defined as

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W ebJaccard( wI, w2)=

to s(wlnw2)

,ifs(wlnw2) �c

,otherwise (2)

s( wI) + s( w2) -s( wI n w2)

Wlfl W2 denote the conjunction query WI and W2. There

is a possible that two words may appear on same page even though they are not related. If the page count for the query

WI n W2 is less than the threshold c, the web jaccard

coefficient set to be zero. Similarly we define web overlap, web overlap (WI, W2), as

Weboverlap(wl, w2)=

,ifs(wln w2) � c

to s(wln w2) ,otherwise

(3)

min(s(wl),s(w2) )

The natural modification of the simpson coefficient is called as web overlap. We define the webdice coefficient as a variant of the dice coefficient. WebDice (WI, W2) is defined as

WebDice(wl, w2)= fO s(wlnw2)

lS(Wl) + S(W2)

,ifs(wlnw2):S:c

,otherwise (4)

Simple approximation of actual word co- occurrences in the web is said to be page counts. It shown empirically exist a high correlation between word counts.

E. improvement of match making level

Suppose we are interested in measuring the similarity between the words that are defined in same ontology. Ontology can be used to represent the knowledge about particular domain. This domain knowledge includes entities, their property and relationship with each other. Entities in the ontology are termed words. To overcome the disadvantage in existing algorithm we define five match making level is Exact, Subsume, Plugin, brother, Fail.

Stepl: Exact: In this level sim (X; Xj = 1 . The exact matching level in ontology includes the current node and its

parent. (3) Step 2: Plugin: This matching level in ontology

includes all of ancestor nodes of current node. Step 3: Subsume: This matching level includes all of

descendent nodes of current node.

Step 4: Brother: The brother matching level includes all of brother nodes of current node.

Step 5: Fail: There is no match between the pair of words. Sim (X; X) = O.

TABLE I COMPARISION OF SEMANTIC SrMlLARITY WITH DIFFERENT

ALGORITHM

Word-pair Similarity

WebJaccard Web dice Subflead Measured Result

UsillgMatcfl Maker

Automobile- 0.65 0.66 0.15 0.89 car

Journey- 0.41 0.58 0.39 0.75 voyage

Gem-jewel 0.29 0.30 0.42 0.51

Food-fruit 0.75 0.76 0.55 0.84

Food-rooster 0.21 0.00 0.42 0.43

Monk-slave 0.17 0.18 0.77 0.34

Coast-hill 0.96 0.97 0.84 1.00

Forest- 0.06 0.06 0.54 0.57 graveyard Magician- 0.29 0.30 0.68 0.84

wizard Cord-smile 0.09 0.11 0.13 0.21

Noon-string 0.12 0.12 0.21 0.33

Brother-lad 0.18 0.19 0.26 0.44

Rooster- 0.00 0.00 0.21 0.15 voyage Stock- 0.68 0.75 0.53 0.88 market

IV. EXPERrMENTS

To evaluate the performance of algorithm and usefulness of the semantic similarity measure we used Miller- Charles data set. The webjaccard, dice, web overlap simson similarity can be computed using the formula define in section 3.4. Tablel compares the proposed match maker algorithm against web based semantic similarity measure: web Jaccard, dice, [15]

Normalized Google distance (NGD). Each algorithm uses Pearson correlation coefficient to measure the similarity score. Similarity score for each algorithm are normalised to [0, 1] range for the ease comparison with other semantic web based algorithm. The similarity scores shown in the table 1 expect the proposed similarity measure result are taken from the original published papers. Comparing to the existing method score shown in table 1 the proposed method reported highest correlation. We have seen that existing method use the co-occurrence statistics such as page count based method to measure the semantic similarity between words. Unlike the proposed method does not consider the co-occurrence statistics instead it uses the five different matching level to measure the semantic similarity between words.

V. WORD PAIR SIMILARITY CALCULATION IN

EACH MATCHMAKING LEVEL

Given word pair X, X', their semantic similarity is computed in each match making level as follows. Level l: In Exact match making level,

sim(X, X')= l. (5)

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TABLE fl. WORD PAIR SIMILARllY ILLUSRATTING IN HIERARCHAL

Word-pair Subflead

Automobile -four wheeler-cost-5Iacks 0.9 2

Automobile -four wheeler-speed-2000CC 0.93

Automobile -four wheeler-company-ford 0.9 7

Automobile -four wheeler-cost 0.8 5

Automobile -four-wheeler-speed 0.81

Automobi Ie -four-wheeler-company 0.75

Automobi le-four-wheel er-computer 0.53

Automobile-computer-cost 0.33

Automobi Ie-fruit -speed 0.28

Automobile-student-speed 0.18

Automobile-fruit-cost 0.11

Level 2& 3: Word pair has inheritance relationship in plugin and subsume level. The fewer number of hopes between words is that more similar they are. Distance measure is one which is used to calculate the similarity between words. The concept of similarity is defined as sim (X, X') = 1 - dis (X, X'), where dis (X, X') denotes the weighted distance of two words in an ontology.A weight value w(x) is assigned to each word in ontology taxonomy. Since the distance between two given words represent the path over their closest common parent ccp (X, X'), it is calculated as the sum of their distance to their closest common parent. dis (X, X') = [w (ccp (X, X') - (w(x) ] + [w(ccp (X; X') - w(x)] (6)

The weight value between the words in the ontology is calculated with the help of following formula.

1 W(x) =

KI(x)+' (7)

W (X) The length of the longest path in the ontology. K is the predefined factor used to indicate rate of the weight value nodes in ontology. The values is larger than l. We assigned k = 2 .

Level 4: In fail level there is no match between the words in ontology. sim (X, X') = o.

Level 5: In brother level, both word pair have same parent node. The similarity between words in ontology will be larger, if the depth of the parent node is relatively big.

VI. CONCLUSION AND FUTURE WORK.

We have presented a matchmaker algorithm to measure the similarity between two words. The ontology concept has used to represent the several relationships that exist between the words. The proposed matchmaking algorithm correctly computes the semantic similarity between words after classifying matchmaking level. We consider the different

factor to measure the weighted relation distance between words in ontology. The experimental outputs shows our method is effective and outperforms large verity of semantic similarity measure developed with help of numerous resources. In our method measuring their similarity is defined in the same ontology. In future work, we extend our measuring similarity between the words in different ontology. Presently our work cannot support multiple inheritance ontologies.

ACKNOWLEDGMENT

The authors would like to express their thanks to the Chairman, Principal and faculty of Information Technology Department, J.J.College of Engineering and Technology and TRP Engineering College for the environment provided.

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