A context-aware semantic similarity model for ontology environments

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  • CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCEConcurrency Computat.: Pract. Exper. 2011; 23:505524Published online 5 November 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/cpe.1652

    A context-aware semantic similarity model for ontologyenvironments

    Hai Dong,, Farookh Khadeer Hussain and Elizabeth Chang

    Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth WA 6845, Australia


    While many researchers have contributed to the field of semantic similarity models so far, we find thatmost of the models are designed for the semantic network environment. When applying the semanticsimilarity model within the semantic-rich ontology environment, two issues are observed: (1) most of themodels ignore the context of ontology concepts and (2) most of the models ignore the context of relations.Therefore, in this paper, we present a solution for the two issues, including a novel ontology conversionprocess and a context-aware semantic similarity model, by considering the factors of both the contextof concepts and relations, and the ontology structure. Furthermore, in order to evaluate this model, wecompare its performance with that of several existing models performance in a large-scale knowledgebase, and the evaluation result preliminarily proves the technical advantage of our model in ontologyenvironments. Conclusions and future works are described in the final section. Copyright 2010 JohnWiley & Sons, Ltd.

    Received 6 July 2010; Accepted 9 July 2010

    KEY WORDS: ontology; OWL; semantic network; semantic similarity model


    Semantic relatedness refers to human judgment about the extent to which a given pair of concepts arerelated to each other [1]. Studies have shown that most people agree on the relative semantic relat-edness of most pairs of concepts [2, 3]. Therefore, many technologies have been developed to datein order to precisely measure the extent of similarity relatedness and similarity between conceptsin multiple disciplines, such as information retrieval (IR) [49], natural language processing (NLP)[1013], linguistics [14], health informatics [15], bioinformatics [1, 1619], web services [20],ontology extraction/matching [2123] and other fields. In the fields of IR and NLP, the researchesprimarily focus on word sense disambiguation [9, 10], multimodal document retrieval [24], textsegmentation [7, 12] and query preciseness enhancement [5, 6]. In the linguistic area, the researchesemphasize computing semantic similarity between uncertain or imprecise concept labels [14]. Inthe health domain, the researchers are mainly concerned with seeking similar health science terms.In the field of bioinformatics, the focus is on measuring the similarity between concepts from thegene ontology [1619]. In the field of web services, the researches concentrate on semantic servicediscovery [20]. In the field of ontology extraction/matching, semantic similarity models are usedin the process of ontology similarity measurement [2123]. Moreover, the semantic similarity

    Correspondence to: Hai Dong, Digital Ecosystems and Business Intelligence Institute, Curtin University ofTechnology, Perth WA 6845, Australia.

    E-mail: hai.dong@cbs.curtin.edu.au

    Copyright 2010 John Wiley & Sons, Ltd.


    models can be also used to estimate the similarity between land use and land cover classificationsystems [25].

    However, when exploring these semantic similarity models, we observe that most of the existingmodels focus only on the semantic network environment but ignore the special features of theontology environment. For example, most of the models do not have specific solutions to processthe context of concept attributes and the context of relations when estimating similarity betweenconcepts. Based on this finding, we develop a novel context-aware solution for the semanticsimilarity measure in the ontology environment. This solution contains an ontology conversionprocess and a hybrid semantic similarity model, which involves assessing the concept similarityfrom the perspectives of both the ontology structure and the context of ontology concepts andrelations.

    The remainder of the paper is organized as follows. In Section 2 we conduct a detailed compar-ison between ontology and the semantic network, and then review and analyze the existing semanticsimilarity models in order to discover the issues that arise when applying the models within theontology environment. In Section 3, we provide an ontology conversion process to preliminarilyaddress the issues found in Section 2. In Section 4, we present the proposed hybrid semantic simi-larity model. In Section 5, to thoroughly validate the model, we implement a series of experimentsand perform scientific evaluations and experimentations. The conclusion is drawn and future workis proposed in the final section.


    2.1. Ontology and semantic network

    In the field of information science, ontology is defined by Gruber [26] as an explicit specificationof conceptualization. An ontology primarily consists of the following components:

    Classes that define a group of individuals that share the same features. Properties that describe relations between classes. In OWL, there are two sorts of properties

    as follows:

    ObjectProperty that defines relations between two or more than two classes, and DatatypeProperty that defines relations between instances of classes and RDF literals and

    XML schema datatypes [27]. Restrictions and characteristics that describe constraints on relations. In OWL, restrictions

    include allValuesFrom (), someValuesFrom (), hasValue (), cardinality (=), minCardi-nality (), maxCardinality (); characteristics include FunctionalProperty (one property hasa unique value), InverseOf (one property is the inverse of another property), InverseFunc-tionalProperty (the inverse of one property is functional), TransitiveProperty (properties aretransitive) and SymmetricProperty (properties are symmetric) [27].

    Axioms that describe the rules followed by an ontology when applying it to a domain. In OWL,the class axioms include one of (enumerated classes) , disjointWith (classes are disjointedwith each other), equivalentClass (two classes are equivalent) , subClassOf (one class is aspecification of another class) [27].

    A semantic network is defined as a graphic notation for representing knowledge in patternsof interconnected nodes and arcs [28]. WordNet is a typical example of a semantic network, inwhich words or phrases are represented as nodes and are linked by multiple relations. The mostcommon relations are meronymy (A is a part of B), holonymy (B is part of A), hyponymy (A isa subordinate of B), hypernymy (A is superordinate of B), synonymy (A is a synonym of B), andantonymy (A is the opposite of B).

    In Table I, we make a general comparison between ontologies and semantic networks based ontheir components. The main differences are that ontology concepts and relations can be definedwith more attributes, restrictions and characteristics, compared with single-word/phrase-composed

    Copyright 2010 John Wiley & Sons, Ltd. Concurrency Computat.: Pract. Exper. 2011; 23:505524DOI: 10.1002/cpe


    Table I. Comparison between ontologies and semantic networks.

    Components Ontologies Semantic networks

    Classes Have individuals Do not have individualsProperties Have object properties and

    datatype propertiesDo not have datatype properties

    Restrictions and characteristics Have restrictions andcharacteristics

    Do not have restrictions andcharacteristics

    Axioms Have axioms Do not have one of anddisjointWith

    counterparts in semantic networks. Therefore, it can be concluded that ontologies can express moresemantic information than can semantic networks.

    2.2. Semantic similarity models

    In the literature, there are many similarity measures. For the purpose of discussion, we divide theminto three main categories according to the utilized information as followsedge (distance)-basedmodels [4, 6, 9, 2932], node (information content)-based models [10, 33, 34] and hybrid models[11, 3537]. In the remainder of the section, we will briefly introduce the three categories andthe typical models in each category, and analyze their limitations when applying them within theontology environment.

    Edge (distance)-based models. Edge-based models are based on the shortest path betweentwo nodes in a definitional network. Definitional networks are a type of hierarchical/taxonomicsemantic network, in which all nodes are linked by isa relations [28]. The models are based onthe assumption that all nodes are evenly distributed and are of similar densities and the distancebetween any two nodes is equal. They can also be applied to a network structure.

    One typical edge-based model was provided by Rada et al. [4], and is described asFor two nodes C1 and C2 in a semantic network,

    Distance(C1,C2)=Minimum number of edges seperating C1 and C2 (1)and the similarity between C1 and C2 is given by

    simRada(C1,C2)=2MaxDistance(C1,C2) (2)where Max is the maximum depth of a definitional network.

    In order to ensure that the interval of simRada is between 0 and 1, Equation (2) can also beexpressed as

    simRada(C1,C2)=1 Distance(C1,C2)2Max (3)

    Leacock and Chodorow [29] considered that the number of edges on the shortest path betweentwo nodes should be normalized by the depth of a taxonomic structure, which is expressedmathematically as

    Distance(C1,C2)= Minimum number of edges seperating2Max (4)

    and the similarity between C1 and C2 is given by

    simLeacock(C1,C2)= log(Distance(C1,C2)) (5)Wu and Palmer [30] mentioned the node that subsumes two nodes when computing the similarity

    between the two nodes, which can be expressed mathematically as follows:

    simWu&Palmer (C1,C2)= 2N3N1+ N2+2N3 (6)

    Copyright 2010 John Wiley & Sons, Ltd. Concurrency Computat.: Pract. Exper. 2011; 23:505524DOI: 10.1002/cpe


    where C3 is the most informative node that subsumes C1 and C2, N1 is the minimum number ofedges from C2 to C3, N2 is the minimum number of edges from C2 to C3, N3 is the depth of C3.

    Node (information content)-based models. Information content-based models are used to judgethe semantic similarity between concepts in a definitional network or in a corpus, based onmeasuring the similarity by taking into account information content, namely the term occurrence incorpora or the subsumed nodes in taxonomies. These models can avoid the disadvantage of the edgecounting approaches that cannot control variable distances in a dense definitional network [10].

    Resnik [10] developed such a model whereby the information shared by two concepts can beindicated by the concept THAT subsumes the two concepts in a taxonomy. Then, the similaritybetween the two concepts C1 and C2 can be mathematically expressed as follows:

    simResnik(C1,C2)= maxCS(C1,C2)

    [ log(P(C))] (7)

    where S(C1,C2) is the set of concepts that subsume both C1 and C2, and P(C) is the possibilityof encountering an instance of concept C .

    Lins [33] semantic similarity model is the extension of Resniks model, which measures thesimilarity between two nodes as the ratio between the amount of commonly shared informationof the two nodes and the amount of information of the two nodes, which can be mathematicallyexpressed as follows:

    simLin = 2simResnik(C1,C2)I C(C1)+ I C(C2) (8)

    Pirro [34] proposed a feature-based similarity model, which is based on Tverskys theory thatthe similarity between two concepts is a function of common features between the two conceptsminus those in each concept but not in another concept [38]. By integrating Resniks model, thesimilarity model can be mathematically expressed as follows:


    3simResnik(C1,C2) I C(C1) I C(C2) if C1 =C21 if C1 =C2


    Hybrid models. Hybrid models are composed of multiple factors for similarity measure. Jiangand Conath [35] developed a hybrid model that uses the node-based theory to enhance the edge-based model. Their method takes into account the factors of local density, node depth and linktypes. The weight between a child concept C and its parent concept P can be measured as

    wt(C, P)=(+(1) E



    )(I C(C) I C(P))T (C, P) (10)

    where d(P) is the depth of node P , E(P) is the number of edges in the child links, E is the averagedensity of the whole hierarchy, T (C, P) represents the link type, and and (0,01) arethe control parameters of the effect of node density and node depth on the weight.

    The distance between two concepts is defined as follows:


    C{path(C1,C2)L S(C1,C2)}wt(C, p(C)) (11)

    where path(C1,C2) is the set that contains all the nodes in the shortest path from C1 to C2, andL S(C1,C2) is the most informative concept that subsumes both C1 and C2.

    In some special cases such as when only the link type is considered as the factor of weightcomputing (=0, =1 and T (C, P)=1), the distance algorithm can be simplified as follows:

    Distance(C1,C2)= I C(C1)+ I C(C2)2simResnik(C1,C2) (12)where I C(C)= log P(C).

    Finally, the similarity value between two concepts C1 and C2 is measured by converting thesemantic distance as follows:

    simJiang&Conath(C1,C2)=1Distance(C1,C2) (13)

    Copyright 2010 John Wiley & Sons, Ltd. Concurrency Computat.: Pract. Exper. 2011; 23:505524DOI: 10.1002/cpe


    Table II. Comparison of the typical semantic similarity models.

    Category Models Working environment Measure factors

    Edge-based Rada et al. [4] Definitional networks Shortest pathLeacock andChodorow [29]

    Definitional networks Shortest path

    Wu and Palmer [30] Definitional networks Shortest path and node depthNode-based Resnik [10] Definitional networks

    or corporaSubsumed nodes in definitional networksor word occurrences in corpora

    Lin [33] Definitional networksor corpora

    Subsumed nodes in definitional networksor word occurrences in corpora

    Pirro [34] Definitional networksor corpora

    Subsumed nodes in definitional networksor word occurrences in corpora

    Hybrid Jiang and Conrath [35] Semantic networks Shortest path, subsumer, local density,node depth and link types

    Li et al. [11] Semantic networks Shortest path, node depth and local density

    In addition, Secos [39] research showed that the similarity equation can also be expressed as

    simJiang&Conath(C1,C2)=1 Distance(C1,C2)2 (14)

    The testing results show that the parameters and do not heavily influence the similaritycomputation [35].

    Li et al. [11] proposed a hybrid semantic similarity model combining structural semantic infor-mation in a nonlinear model. The factors of path length, depth and density are considered in theassessment, which can be mathematically expressed as

    simLi (C1,C2)=

    el eh eh

    eh +eh if C1 =C21 if C1 =C2


    where l is the shortest path length between C1 and C2, h is the depth of the subsumer of C1 andC2, and are the effects of l and h on the similarity measure.

    In orde...


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