Context-Sensitive Referencing for Ontology Mapping Disambiguation

Abstract

Ontologies can be used for e-business integration, for example by describing existing e-business standards as ontologies. If cooperating parties use different ontologies, ontology mappings are needed, which can be ambiguous, thus making ontology mapping disambiguation necessary. Different disambiguation strategies exist, such as community-driven or context-sensitive referencing of ontologies, where the latter is what we developed in our project. In this paper, we show that community-driven referencing can be realized using a context-sensitive referencing service in a way that the user administration is transparent to the referencing system.

Keywords

• Semantic Synchronization
• Ontology Mapping
• Ontological Engineering
• Context-Sensitivity
• Communities

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top of JENA2 [ 4 ] and Java that allows connecting such components to form a coherent semantic referencing service [ 5 ], as well as reusing techniques from information retrieval (IR).

The service allows users to find references between ontologies. References may either be created manually or established automatically by a mapping tool. However, as stated in [ 6 ], more than one reference can exist for the same element, caused by different modelling approaches and granularities of the individual standards, even more so if proprietary or in-house standards are used. Therefore, reference disambiguation strategies are needed, which filter appropriate results and/or sort results by relevance. The framework developed in our project evaluates context information to provide reference disambiguation.

The rest of this paper is structured as follows. Section 2 describes the basics on ontologies, references, and context. Section 3 explains two approaches for reference disambiguation: community-based and context-sensitive referencing. Section 4 shows how community-based referencing can be realized using contextsensitive referencing. Section 5 provides an overview on related work, and section 6 closes with a discussion of our results. 2

Ontologies, Semantic References, and Context

Ontologies are structured, machine-readable representations of knowledge. There are many different definitions of what an ontology actually is (for a comprehensive overview see [ 1 ]), however, we will look at ontologies as a collection of definitions of elements and their relations. Ontologies can be represented in different languages, the most dominant are RDF Schema [ 7 ], and the various dialects of OWL [ 8 ]. Ontologies are considered as a means for e-business integration [ 9 ], however, if two or more cooperating parties use different ontologies, further steps have to be taken to allow seamless interoperability.

Therefore, ontology matching solutions are needed, which produce mappings from elements in one ontology to elements in another. There are two main categories of ontology matching algorithms [ 10 ]. One are element-based approaches, which try to match single elements of an ontology, either using only the information given in the ontology itself (e.g., by measuring string distance using the edit distance), or by using external information, e.g. upper-level ontologies, such as WordNet [ 11 ]. The second are structure-level approaches, which do not only analyze elements isolated from each other, but also their relations and patterns they form in graphs. An overview and more detailed analysis of matching approaches can be found in [ 10 ] and [ 12 ]. Some approaches, like [ 13 ], combine the weighted results of several matching solutions in order to obtain mappings of higher quality.

Ontology matching tools provide references. In extension of [ 14 ], references can be described as a five-dimensional vector of the form ref erence := hentity1, entity2, type, conf idence, acceptancei . (1) The first two entries entity1 and entity2 are URIs of the elements from both ontologies to be referenced, type describes the kind of relation (like “equal”, “subclass of”, etc.), conf idence describes the degree of probability of the relation, and acceptance expresses the users’ rating of that reference. For example, the reference r1 = hStandardA#X, StandardB#Y, equal, 0.87, 0.95i (2) is read as “Element X in StandardA and element Y in StandardB are equal with a probability of 87%, and 95% of all users agreed on that statement”. The acceptance value is calculated from the users’ ratings.

In order to disambiguate such semantic references, we have developed an approach which uses context information. There has been a lot of research on context in the fields of machine translation and IR, yielding several ways of describing context. In machine translation, shallow and deep approaches [ 15 ], bag of words and relational approaches [ 16 ] are distinguished to solve the problem of word sense disambiguation. In IR, context data can be represented in different forms, from simple binary vectors to highly complex graphs, as proposed by [ 17 ]. An introduction to context queries in IR can be found in [ 18 ]. 3 3.1

Approaches for Mapping Disambiguation

Community-Based Referencing

The idea of context mapping disambiguation by using communities has first been developed by Anna V. Zhdanova and Pavel Shvaiko in [ 19 ]. The general problem of community-based referencing can be formally defined as follows: Definition 1. Given a user being member in a non-empty set of communities SU , find those references for an element x from a set of ontologies O1 to a set of ontologies O2 that have been created by a user being member in a non-empty set of communities SC under the condition that SU ∩ SC is not empty.

That means that a user issuing a query for semantic references on an element is presented all references for that element created by users with whom he has at least one community in common (note that we are considering the creators of ontology references, not of the ontologies themselves). The user’s login and community data are directly processed by the referencing system.

Although the authors of [ 19 ] primarily focused on mapping reuse, this community-driven approach can also be seen as an ontology mapping disambiguation strategy: different semantic references caused by ambiguous use of elements in different communities are filtered and thereby disambiguated. We will call a semantic referencing service that allows disambiguation by using context information a community-based semantic referencing service.

Figure 1 demonstrates the idea of community-driven mapping disambiguation. There are two references for the element “switch” from a rather coarsegrained proprietary standard P to the more fine-grained standard eCl@ss [ 20 ], each having its right to exist in a given context. User 1 is a network administrator using standard P for ordering an ethernet LAN switch. Since the supplier uses eCl@ss, user 1 queries the semantic referencing system for references for the element “switch”. The system returns the reference to “19-03-01-17” (which is the eCl@ss code for “network switch”) created by user 2, since both users are in the “networks”-community, but does not return the reference to “27-14-40-47” (which is the eCl@ss code for “toggle switch”) created by user 3, since users 1 and 3 do not share any communities. The list of references that exist for the element “switch” is thus filtered and thereby disambiguated. 3.2

Context-Sensitive Referencing

A different approach for disambiguating semantic references is the evaluation of the context of the term to be referenced. The general problem of context-sensitive referencing can be defined as follows: Definition 2. Given some context information C (x), find the references for an element x from a set of ontologies O1 to a set of ontologies O2, with an acceptance value accC(x) (which is the higher the more appropriate the reference is in this context), calculated dynamically for that context information and exceeding a minimum acceptance threshold accmin.

Such an acceptance value accC(x) can be obtained in different ways. Since one of the design aims of our system was to minimize the need for manual preparatory work, we decided to calculate accC(x) based on user ratings. Each user can rate (in the easiest case: accept or deny) a reference in his or her context, and the ratings are stored in the system. Each time a user requests a reference for an element in a context, the acceptance value is calculated using the distance-weighted k-nearest-neighbor rule [ 21 ], with the difference between the similarity of the request’s context CQ (x) and the rating’s context CR (x) as distances, given any similarity function sim. In other words, accC(X) (Ref ) is calculated as

accdef accCQ(X) (Ref ) = (PR∈Ratings(Ref) sim(CsQum(xs)i,mCR(x)) · acc (R) sumsim > 0 sumsim = 0 (3) where sumsim is calculated as

X

R∈Ratings sumsim := sim (CR (x) , CQ (x)).

(4) and accdef is a configurable parameter which serves as a default acceptance if no ratings exist or if none of the ratings is at least minimally similar to the query’s context. In the latter case, it is also possible to use the unweighted median of all ratings.

We will call a semantic referencing service which uses context-sensitive reference disambiguation a context-sensitive semantic referencing service. As already stated in section 2, there are different ways to describe context. Since different client applications can have different strategies of gathering context information, using more specific context information (as in deep and relational approaches) narrows the variety of possible client applications. Therefore, we decided for a relational approach which uses a weighting factor for each context term, where the context terms are simple strings. Therefore, the context of an element x is defined as a set of context terms C (x), and a normalized weighting function ω, defined as ωC(X) : C (X) → [ 0, 1 ] with

max ωC(X) = 1. y∈C(X) (5)

That function can also be interpreted as a reverse of a distance function: the higher a context term’s weight, the closer it is to the term in question.

Since many context similarity measures are defined for vectors, with the context terms used as dimensions and the weights as values, the weighting function can also be regarded as a weighting vector wC(X) with wi,C(X) := ωC(X) (ti) , ti ∈ C (X) , 1 ≤ i ≤ |C (X)| . (6)

With those definitions, an acceptance value can be calculated for each reference, determining that reference’s appropriateness in the query’s context. Thereby, semantic references can be disambiguated. Details on context-sensitive reference disambiguation can be found in [ 22 ]. 4 4.1

Community information as a special kind of context

Using communities as context information

A query for references in a community-driven scenario, as stated in definition 1, can be identified by a query term X and by a set SU of community identifiers, where SU ⊆ S, and S represents the set of all communities. A query in a contextsensitive scenario, as stated in definition 1, is identified by a query term X, a context set C (X) (containing context terms), and a weighting function ωC(X) as defined in (5).

C (X) := S and ωC(X) (t) := (1 0 ∀ t ∈ SU ∀ t ∈ S − SU

We are now going to show that our context-sensitive reference disambiguation approach answers context-based queries as defined above such that the following requirements are fulfilled: Requirement 1: All references created by users that share at least one community with the user issuing the query are returned.

Requirement 2: No references created by users that do not share any community with the user issuing the query are returned.

To this end, we use the cosine similarity [ 18 ] as a similarity measure, and a default acceptance accdef = 0. Furthermore, we assume that for each reference that one and only one rating exists, whose context is the community information of the reference’s creator as defined above and whose acceptance value is 1. We will elaborate on how to assure this assumption in the next section.

Let wCQ(X) be the query’s weighting vector and wCQ(X) be the rating’s vector (containing the community information of the reference’s creator), according to (6).

The cosine similarity is defined as

Since, according to definition 1, the result set would be empty if the user was not a member of any community, we assume that each user issuing a query is a member of at least one community.

In order to transform a community-driven query to a context-sensitive one, we treat the community identifiers as simple strings and define: (7) (8) (9) simcos ¡wCQ(X), wCR(X)¢ :=

wCQ(X) • wCR(X) . °°wCQ(X)°° °°wCR(X)°°

Since each user is a member of at least one community, at least one element in both wQ and wR has a value of 1, thus, the denominator never equals 0. Furthermore, wCQ(X) • wCR(X) is greater than zero if and only if both vectors contain a non-zero element in the same position, e.g. if both users have at least one community in common, and zero otherwise. Thus, (3) reduces to accCQ(X) (Ref ) = (> 0 if sim ¡wCQ(X), wCR(X)¢ > 0 0

if sim ¡wCQ(X), wCR(X)¢ = 0

Thus, if all semantic references are filtered with a threshold of accmin = 0, and only references with an acceptance value accCQ(X) (Ref ) > 0 are returned, the two requirements stated above are fulfilled. That shows that our system can provide community-driven reference disambiguation, put down to contextsensitive referencing.

Providing community-based reference disambiguation by a context-sensitive referencing service

Our original context-sensitive referencing service provides three main functions: – Create a new reference, – get a list of references in a given context, – and rate a reference in a given context.

In order to assure that only one rating exists for each reference, as proposed in the section above, those functions are encapsulated to form a community-based referencing service as follows: – Each time a user creates a reference using the community-driven referencing service, the reference is automatically rated with an acceptance value of 1 in the context derived from the user’s community information.

– The request for a list of references remains the same.

With this approach, we have created a community-driven semantic referencing service by encapsulating our context-sensitive semantic referencing service, where the latter remains unchanged. The referencing system only processes context data, thus abstracting away from user and community administration. In principal, the algorithm is generic enough to solve other context-based disambiguation tasks as well. 5

Related Work

In the area of ontological engineering, much research work has already been conducted on ontology matching and ontology reasoning. Ontology matching deals with finding similarities between ontologies, often in order to merge them [ 10 ]. Ontology reasoning tries to derive new knowledge from knowledge already present in an ontology. There are also approaches trying to improve ontology mappings by means of ontology reasoning [ 23 ], while others propose an ontology mapping language capable of mapping heterogeneous information, like concepts to relations [ 24 ].

Some research projects deal with providing semantic references between ebusiness standards to allow semantic integration. Besides the already mentioned community-based approach developed by Zhdanova and Shvaiko [ 19 ], some other projects exist. [ 25 ] combine agents and ontology mapping to allow automatic e-business transactions. Some approaches try to collect references under the umbrella of one global ontology, like WordNet [ 26 ]. [ 27 ] propose a hierarchy of ontologies connected by mappings. Zimmermann and Euzenat haven shown in [ 28 ] that a context-sensitive approach is not possible for ontology alignment. However, it is a feasible approach for disambiguating semantic references. Other works, like [ 29 ], use ontologies, for example, to disambiguate items like person names in unstructured text by searching context terms in ontologies, unlike our approach, where context terms can be arbitrary strings that need not exist in any ontology.

The problem of context-sensitive referencing can be regarded as a special information retrieval problem. Extensive research has been conducted in this area. The present approaches stretch from using simple context term vectors [ 18 ] to describe context in rich semantic structures like RDF graphs [ 17 ]. There are also community-based information retrieval approaches, like [ 30 ], which uses the visualization of different perspectives in distinct communities for sharing information across community borders.

While our system is based on creating a collection of references, other approaches try on-the-fly mapping of ontologies [ 31 ], which is a reasonable approach when, like in the case of very large ontologies, the collection of mappings tends to become rather extensive. There are also works on matching blocks of partioned ontologies [ 32 ], which could be a possible approach to deal with the problem of large ontologies. 6

Discussion

In this paper, we have shown that a context-sensitive semantic referencing service, combined with user’s ratings, can also be used for providing communitybased semantic referencing. Both are feasible approaches for ontology mapping disambiguation, each having their advantages and drawbacks: – Both approaches provide mechanisms to create a growing knowledge base of semantic references. – Community-based referencing needs the additional implementation of user and community administration, while context-sensitive referencing also works from scratch (our implementation of the service also works with empty context information). – On the other hand, community-based referencing is an appropriate approach to ensure that references remain private in a community and users from other communities will never come to see those references. – The rating mechanism underlying our context-sensitive approach can also be made transparent to the user by observing the user’s behavior: if a user works with a reference, it receives a positive rating, if s/he decides not to work with a proposed reference, it receives a negative rating. – Both approaches have to cope with erroneous user’s entries. Communitybased referencing only has to deal with wrong references. Context-sensitive referencing also has to handle wrong ratings, which can mislead the system to calculate a wrong acceptance value and thus present a reference not appropriate in a context as being highly appropriate, and vice versa. However, the ratio of correct ratings to incorrect ones is high enough, the weight of wrong ratings decreases, and it is likely that many negative ratings will make a wrong reference fall below the lower acceptance threshold and thus make it “disappear” from the list of results displayed for the user. – Since the usage context of a term in general can be expected to be similar within a community and different between distinct communities, context information can be looked at as implicit community information, and vice versa.

The approach presented in this paper does not yet allow using context-sensitive and community-driven semantic referencing in parallel (e.g. to further disambiguate different references used in a community). However, if this can be achieved by allowing two sets of context (the community information and the actual context information), calculating an acceptance value for each context and applying filters to each of the calculated acceptance values. Such an approach would also make the use of further types of context information possible, like documents, bookmarks, the user’s role in a company, or previous projects the user has worked on, as proposed by [ 17 ].

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