Similarity Algorithm for Evaluating the Coverage of Domain Ontology ...

2014 8th Malaysian Software Engineering Conference (MySEC)

Similarity Algorithm for Evaluating the Coverage of Domain Ontology for Semantic Web Services Radziah Mohamad, Nurhamizah Mohd-Hamka Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia [email protected], [email protected] work from [9] additionally highlight the limitation of the ontology being validate for the same purpose.

Abstract— Ontology evaluation consists of verification and validation of ontology to achieve selected evaluation objectives. Domain ontology developments were time consuming and did need proper way of evaluation so that the ontology does support the knowledge it represent throughout certain domain. Ontology reuse becomes the main objectives as to increase the resource reusability of the already built ontology. Domain ontology evaluation currently validated via gold standard or expert validation and it might lead to biased validation. User has its own perception towards the criteria of the ontology they want, there are not exact standard of ontology quality since variety of requirements to select suitable ontology for their usage. Travel domain will be our case study domain and we will pick on any ontology consider as ‘travel ontology’ from the searched from ontology inventories in Web. We proposed algorithm measurement to validate the coverage of ontology gathered and display the result to users. This is to ensure user select the suitable ontology based on coverage result.

Aiming towards universal ontology evaluation tools is difficult since different types of knowledge domains and users aims for different evaluation objectives. Existing study made to come out with diverse methods of evaluation. Some of the methods known are criteria-based evaluation. It is one of the methods that used to scope criteria that needed to achieve for example, suitability [10], [11] and consistency [12]. References [13] and [14] works focused on managing criteria grouping. Literature works by [15] aims on selecting the suitable frame of references with its own criteria, for example, data driven based evaluations were mapped with coverage criteria and so forth. The purpose of evaluation of ontology is to identify the coverage of travel domain describe within ontology documents by string matching. Ontology evaluation work had consists of several hybrid methods, some of the concept of ontology evaluation were conceptualized in OntoUji [16]. The method is to find the similarity of synonym from keywords provided by user on top of ontology concept via breadth first search. We assume that higher similarity of statement that match the words provided caused higher coverage of travel domain it represents. We grouped the ontology documents based on the breadth size of the ontology concept documented. The grouping of the ontology file is used to avoid biased evaluation for example, small number of concept but higher rate of similarity hit.

Keywords— coverage; ontology evaluation; domain ontology

I. INTRODUCTION Ontology evaluation become the prerequisite element within ontology lifecycle similar to software engineering discipline approaches that involve process of software evaluation testing to gain user satisfaction. Ontology used to describe Web services to support the semantic meaning of information. Although study by [1] specify that Web services description language does not depends on how the ontology being represented, but gathering the correct and suitable ontology for user are not at ease. The evaluation of domain ontology focused in this study to evaluate whether the ontology achieve the coverage for the domain representation..

Our work proposed evaluation algorithm used to indicate the coverage of the searched ontology. Inspired from several proposals for ontology coverage method, we rely to the existing measurement and check whether the calculation does help improve the ranking of ontology, thus help user to select the suitable ontology for their usage. As coverage indicator, we pulled out list of string from WordNet to calculate the similarity of the Part-Of-Speech (POS) terms gained within the ontology document.

Coverage is one of the criteria for evaluating domain types of ontology [2]–[5]. SWOOGLE act as one of the ontology libraries and searching for the ontology and based on the matching keyword provided by user. References [6] state that Cupboard or OntoSelect [7] were also the suitable place to gain ontology document. Domain ontology for semantic Web services (SWS) required representation of vast coverage of domain [8]. It will then further used for description of web services to increase the chance of discovery, while existing

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Ontology documentations were published online to serve the knowledge representation for Semantic Web technology aims to provide data with meanings. The purpose of matching process is to allocate similar string from statement in ontology

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documents. This is to ensure that the ontology state terms related to domain knowledge it represented. As references, we refer to questions aims towards coverage criteria from [13] and enhance with related work that related towards synonym similarity string checking [17].

A. Ontology Coverage Evaluation Existing researchers had come out with the coverage evaluation measurement towards ontology. One of the methods proposed in literature for ontology coverage done by [4] which introduced the algorithm by Ontology Concept Coverage (OCC). The objectives of coverage evaluation to identify whether the proposed ontology in the web does cover the domain knowledge it represented. Our work consists of gaining keyword coverage from WordNet dictionary and Dewey. We focus on the domain of travel for our domain ontology evaluation.

II. RELATED WORK Ontology evaluation methods are in various forms and signify different achievement on requirements. Literature study towards ontology evaluation by previous researchers like [18], [19] state the diversity of methods, approach and types of current ontology evaluation progress. In SWS, work by [8] state two different types of semantic Web services ontology, one is generic and the other is domain ontology types used in adding up the semantic aspect on the description of Web services in Web. Thus increase the discovery of Web services, unlike the syntactic description of Web services by Web Services Description Language (WSDL) [1].

Apart from directly validate ontology from algorithm by POS similarity checking, previous works manage ontology evaluation by proposing recommendation system for ontology domain. Reference [28] proposed recommendation system based on the travel ontology they developed from scratch based on user preferences. The works enable concept search to match user own of scenario, which they combine OWL-S to match with WSDL. OWL-S indicates the concept of services profile, while WSDL describe services in tagging form. The works recommend services profile match with services description by rules set. The work were enhanced in [29] by using ontology retrieval query on statement of subject, predicate and object control.

OntoQA [20], OntoFetcher [21] are the example of tools developed on evaluating ontology provided by users. The focused here is, on top of methods of evaluation proposed, there are slightly patterns of criteria most of researchers need to take focused in order to provide the suitable measurement for evaluation of ontology. Criteria focused in early findings, for example coverage, were proposed by [8] and [22] besides others criteria like correctness [23].

III. EVALUATION ALGORITHM Coverage consists of the breadth size of concept that covers the keywords projected on similarity within the ontology documents. Our flow of evaluation consist of gaining ontology from web sources using ‘travel’ keywords and collect as much as possible ontology that hit the keyword provided. The ontology will then go through validation to check on the similarity it hit on with the synonym of the keyword ‘travel’ gain from WordNet.

Coverage of ontology focused to check whether the ontology document describes the domain that it represent. Specific domain knowledge consists of set of terms and relationship between terms that made them related with the domain it specified. For example, hotel, booking and flight were commonly related to tourism or travel domain. Previous research compares string of keywords provided by user with the concept, individuals or property within ontology documents like [24] but their aim is to improve similarity measurement based on user profiling. Besides, synonym tagging is also used to detect the similarity of keywords [8], [17]. Research done by [25] hybrid several ontology evaluation approach including synonym types of similarity measures that focused on biomedical domain ontology. There are large numbers of domain ontology in Web, for example Hotel related ontology [26], service ontology [27] and Quran ontology [30]. Our study aims on the domain of Travel. Related study has proposed to follow WordNet as frame of references but focusing on different area, for example publication domain [17] and programming domain [24]. There are limited existence of concept similarity algorithms related with semantic web service [27]. It is because ontology is a combination of domain and not just focusing on one exact domain. It may include financial domain or mixtures of variety of domain in a single ontology which made it difficult to focus on one exact domain coverage for the hybrid types of web services domain ontology. While some add user profiling method to declare the ownership of the ranking of ontology made by individual user measurement [31] and [24] that insert user profiling for the input of similarity measurement.

Fig. 1. Methodology of Coverage Evaluation for Travel Domain

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Several algorithms proposed towards ontology similarity matching mention in section 2. We used similarity string algorithm [32] and enhanced it by the matching keywords we gain from WordNet POS synonym. The structure of the coverage evaluation proposed will be considered the following algorithm summary.

1. Insert the directory of downloaded ontology file located in the local directory 2. Read related POS-VERB from the provided keyword 3. Read the statement of ontology document for similarity by subject, predicate and object 4. Loop for POS-Verb a. Loop to subject, predicate, object i. Calculate each percentage similarity of POSVerb to subject, predicate object 5. Loop for POS-Noun a. Loop to subject, predicate, object i.Calculate each percentage similarity of POSVerb to subject, predicate object 6. Display the total number of similar string within ontology document and the POS-VERB and POSNoun string 7. Save the list

1. Gain keyword input by user 2. Download ontology document from web using keywords provided 3. Use the keywords and find the synonym of it from WordNet dictionary 4. Calculate the similarity string of the synonym terms with the statement in ontology document 5. Select the coverage measurement 6. Rank the ontology based on highest to lowest measure 7. Display the result of coverage and ranking to user

Fig. 4. Similarity Calculation Algorithm Fig. 2. Summary for Coverage Evaluation for Domain Ontology

Fig 2 describe the summary of methodology work out for the coverage validation flow for travel ontology. The search of synonym of keywords provided were describe in Fig 3. Our work used similarity string checking [32] to detect the percentage of subject, predicate or object that match the POSVerb and POS-Noun gain from WordNet.

1. Calculate similar keyword search with POS-VERB and POS-NOUN 2. Sum the result of POS-VERB and POS-NOUN related to the keyword 3. Identify the ID of the POS-VERB with the domain from WordNet Domains 4. Identify the ID of the POS-NOUN with the domain from WordNet Domains 5. Find the domains of the keyword from the ID identified 6. Calculate the percentage of similarity by 7. Total numbers of ontology terms content similar with VERB/total POS-VERB x 100% 8. Total numbers of ontology terms content similar with NOUN/total POS-NOUN x 100% 9. Display the similarity and coverage measurement result

1. Insert keyword 2. Call function connect WordNet and send keyword a. Connect WordNet dictionary database by calling the library named Java WordNet Library (JWNL) b. Initialize to JWNL library connection by connect to xml file connection WordNet library named file_properties.xml c. The insertion of located directory of file_properties.xml to successfully initialize the connection 3. Display the result of coverage and ranking to user 4. Try: Call function check Synonym of inserted keywords in WordNet a. Declaration of WordNet Dictionary class b. Declare the IndexWord method from JWNL to call word situated in WordNet dictionary c. Set the types POS word to lookup in the WordNet dictionary i. POS-VERB ii. POS-NOUN d. Return list of POS-VERB and POS-NOUN from WordNet e. Display the list of POS-VERB and POS-NOUN 5. Call function to check similarity of list of POS-VERB or POS-NOUN in ontology file (Algorithm 2) 6. Catch: JWNL Exception if none POS exist related to the keyword inserted

Fig. 5. Coverage Calculation Process Algorithm

The coverage of ontology document to represent travel domain were indicated in Fig 5. The work proposed to detect quantity of similar keywords with statement in ontology documents. Fig 4 calculate percentage of POS-Noun and POSVerb by looping into each subject, predicate and object by using string matching algorithm in [32]. The result were illustrated in Table II in section 4.

Fig. 3. Algorithm to Search Similarity of Terms within WordNet

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TABLE I.

IV. COVERAGE MEASUREMENT

Ontology 1

Based on the literature study, we enhanced the algorithm to suit the coverage measurement proposed from former researcher. Focused towards the mapping of WSDL and OWL-S types of semantic Web services description, work by [33] also deal with WordNet keyword discovery and use to find ontology based on the extended keyword suggest from WordNet synonyms. The offset values of each keyword gain from WordNet are then extended towards the label domain related with the synset. A work proposed for domain for WordNet, called WordNetDomains [34] and used as indicator whether the ontology cover the domain it being represented by the number of hit of keywords within the content of the ontology.

POS-NOUN

Object

travel traveling travelling change_of_location travel locomotion travel

Offset

Predicate

Subject

Keyword

0 0 0 0 0 0 0

14 0 0 0 14 0 14

291908 291908 291908 7210982 7210982 279352 279352

0 0 0 0 0 0 0 0 0 0 0 0 0 0

14 24 0 0 14 0 14 0 0 14 0 14 14 0

1818343 1818343 1818343 1818343 1828364 1828364 1825699 1825699 1825699 1829559 1829559 1823864 2083100 2083100

60 0 0 0 60 0 60 Ontology 1

References [35] propose density measurement towards the matching density between variety documents gained from web with the ontology content. The work focused on ontology for car advertisement and enhanced with the work from [36] that used the method of density calculation towards recognizing the document from web from related ontology. We apply similarity matching using work proposed in [32] known as Longest Common Substring measures situated as (1). similarity(s1,s2) = 2 x |pairs(s1) ∩ pairs (s2)| |pairs(s1)| + |pairs(s2)|

RESULT OF KEYWORD SIMILAR IN ONTOLOGY DOCUMENTS

POS-VERB

travel go move locomote travel journey travel trip jaunt travel journey travel travel move_around

(1)

Study proposed by Guarino in [37] towards the coverage of the ontology, measures on the recall factors and recompiled by [13] in the section of coverage measurement. Coverage measures involved similarity in terms overlap or by the matching numbers of terms from selected frame of references [15], in our case, the WordNet act as our frame of reference.

60 0 0 0 60 0 60 0 0 60 0 60 60 0

Main issue in gathering and reading tag in ontology file is they are not being standardized. The tags that create to develop ontology are differing from another. For example, to read the concept within a file, some of them use tag, while some use the tag to represent the concept within the ontology. It make the further step to evaluate the ontology took more times because we have to settle with the reading and gathering the concept within the ontology. Different prefix had being used for ontology file, but Jena handles the different prefix as XML tagging. From POS search in terms of travel, the following result shows the related terms with the keyword travel.

V. RESULT AND DISCUSSION Ontology measurement for domain coverage called upon the coverage measurement gained from section 4 and the result state as the following. The total results represent the total number of add up subject, predicate and object in the searched ontology document. We aimed on full matching keyword summation in the matching process. At the following table, we just focused on the ‘travel’ keyword search match. The keyword ‘travel’ returns several others related synonym in verb and noun POS when connecting to WordNet dictionary. The aims is to have several others string similarity matching keywords to increase the chances that the ontology documents might have others synonym words others than keywords ‘travel’. Ontology document consist of trees of concept or instances connecting with each other via properties and have its own depth and breadth of tree size.

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TABLE II. DETAIL SIMILARITY RESULT OF KEYWORDS IN STATEMENT IN ONTOLOGY DOCUMENTS

TABLE III. RESULT OF MATCH POS-NOUN AND POS-VERB

Ontology

Keyword Used ‘travel’

Subject (S)

Predicate (P)

Object (O)

Statement

… … (S)http://data.ok.gov/resource/_76f947us/56888 (P)member (O)dsbase:_76f9-47us

… … 0.0

… … 0.0

… … 0.0

(S)http://data.ok.gov/resource/_76f947us/56888 (P)account_description_major_class (O)TRAVEL

0.0

0.0

1.0

(S) http://data.ok.gov/resource/_76f947us/56888 (P) rowID (O) 56888 (S) http://data.ok.gov/resource/_76f947us/56888 (P) accounting_period (O) 11 (S)http://data.ok.gov/resource/_76f947us/56888 (P)account_description_sub_class

0.0

0.048

0.0

0.0

(O)TRAVEL - AGENCY DIRECT PMTS (S)http://data.ok.gov/resource/_76f947us/56888 (P)ocp_agncy_name (O)MARGINALLY PROD. OIL & GAS WELLS

O1 O2 O3 O4 O5 O6 O7

POSNOUN = 7 (n1)

POSVERB =14(v1)

Total Match POSNOUN (nmatch)

Total Match POSVERB (vmatch)

3 3 3 5 3 4 3

6 7 6 10 10 7 7

Density POS-NOUN (a) (nmatch)/n1

Density POS-VERB (b) (vmatch/v1)

0.429 0.429 0.429 0.714 0.429 0.571 0.429

0.428 0.5 0.429 0.714 0.714 0.5 0.5

Average (a+b)/2

0.429 0.465 0.429 0.714 0.572 0.536 0.465

The result shows the total match of keywords gained from WordNet within the ontology documents. The average of the density provides the summary of measurement of coverage for the ontology. The higher average indicates the higher coverage of keywords similarity matched within the ontology. From the result shows that, ontology O4 gains the highest average density and ontology O1 shows the lowest average points. This shows that the ranking of the coverage of the ontology are within the following order {O4, O5, O6, (O7, O2), (O1, O3)}, based on the average result gained from the evaluation process.

0.0

0.0

VI. CONCLUSION AND FUTURE WORK 0.0

0.0

0.44

0.0

0.0

0.077

Our work proposed an algorithm to evaluate the coverage of the ontology on travel domain it represented. The ontology documents were classified based on the size of the ontology breadth and keywords matched within the statement of subject, predicate and object. The keywords used were then connected to WordNet to list out the synonym of POS type verb and noun related to it. Besides finding the similarity of keyword given with the statement within ontology documents, the synonyms gained from WordNet were also used to check the matching words. The algorithm of finding similarity of keywords by focusing on breadth searching of keywords matched, the coverage of travel domain represented will be identified by analyzing the match of statement from ontology document with the keywords provided by user and also the synonym of the keywords gain from WordNet as references. The algorithm will further enhanced by including other related references; travel recommendation system and travel data dictionary.

Table II shows the similarity percentage of keywords travel when it compared part by part with the statement of subject, predicate and object of ontology documents. The result shows some part of the result calculated. As far as it seems, the longest common substring mention in (1) show significant result compared to table 1 that state the quantity of Boolean correct hits for keywords that full match with the statement within ontology document. Thus, the full match strings need to calculated by average to compare the percentage of similarity of table 2.

ACKNOWLEDGMENT We would like to thank Ministry of Education Malaysia (MOE) and Universiti Teknologi Malaysia (UTM) for sponsoring the research through the grant with vote number 4F165 and for providing the facilities and support for the research.

The percentage of string match in table 2 might occur to identify the coverage of the ontology for travel domain. Table III indicates the string match gathered from Table I that indicates the count of match terms within ontology documents by Boolean check. The calculation needs to be improved to match with Table II results in future works.

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