Collaborative Support for Community Data Sharing

Gianluca Correndo, Harith AlaniUniversity of Southampton,

Electronic and Computer Science Department{gc3, ha}@ecs.soton.ac.uk,SO17 1BJ, United Kingdom

Abstract

The Semantic Web aims to create a web of datawhere contents can be easily discovered and integratedusing metadata. Many ontologies have been proposedover the years in different domains, thus producing asemantic heterogeneity that is difficult to manage. Var-ious automated ontology mapping techniques and toolshave been developed to facilitate the bridging and inte-gration of distributed data repositories. Nevertheless,such tools are still in need of human supervision toensure accuracy. The spread of Web 2.0 approachesdemonstrate the possibility and the added value of us-ing collaborative techniques for improving data sharingand consensus reaching. In this paper, we describe ourprototype for collaborative ontology mapping and datasharing. The possibility to exploit ontology alignmentsfor querying data is a key capability for data sharing ina networked ontology environment.

1 Introduction

The envision of a Web of data is one of the mostprominent targets of Semantic Web (SW). To helpreach this goal, knowledge repositories need to pub-lish semantic representations of their data models toenable other machines to understand and query theircontent. Due to the highly distributed and decouplednature of the information present on the web, the visionof the SW is moving towards a scenario where the taskof creating and maintaining ontologies, that formalisedata semantics, is going to be handed to the commu-nity that actually uses them [13]. Such an effort mustbe supported by tools and methodologies that allowlatent models to emerge as a product of a collabora-tive effort and dialogue. To this end, much researchand development has focused on building tools and ca-pabilities for ontology and KB construction. However,support for distributed teams to remotely and continu-

ously collaborate on mapping ontologies and knowledgerepositories is still underdeveloped.

In this paper we describe our prototype for data in-tegration based on collaborative and social ontologymapping, our system allows to: align local ontolo-gies to shared ones; exploit social interaction and col-laboration to improve alignment quality; reuse userontology alignment information for enhancing futureautomated alignments and query heterogeneous datasources.

2 Related Work

Investigations into enhancing user knowledgethrough collaboration and sharing goes back to theearly nineties [12]. The Semantic Web has taken thisapproach further by providing the tools and languagesto construct networked semantic representational lay-ers to increase understandability, integration, and reuseof information. The rise of Web 2.0 approaches hasthen demonstrated the effectiveness and popularity ofcollaborative knowledge construction and sharing en-vironments that adopted lighter version of ontologies,where the emphasis is put on the easiness of sharingknowledge rather than creating or adopting static for-mal ontologies [2]. Harnessing Web 2.0 features to facil-itate the construction, curation, and sharing of knowl-edge is currently pursued by different communities.

Collaborative Protege [14] was recently developed asan extension to Protege to support users to edit ontolo-gies collaboratively, by providing them with services forproposing and tracking changes, casting votes, and dis-cussing issues, thus infusing classical ontology editingwith a number of popular social interaction features.Another ontology editor with collaborative support isHozo [10], which focuses on managing ontology mod-ules and their change conflicts.

Other approaches use social tagging as the maindriver for enacting collaborative lightweight ontologybuilding [7, 16]. Similarly, other tools are focussing on

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

978-0-7695-3496-1/08 $25.00 © 2008 IEEE

DOI 10.1109/WIIAT.2008.429

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2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

978-0-7695-3496-1/08 $25.00 © 2008 IEEE

DOI 10.1109/WIIAT.2008.429

17

editing instance data by adopting a wiki philosophy,like OntoWiki [1], or by implementing a frameworkAPI, like DBin [15]. Most of the tools listed abovefocus on supporting users to collaboratively constructontologies or to collaboratively populate an ontologywith instance data. Unlike these tools, however, ourproposed system, OntoMediate, extends the collabora-tive notion to support the task of ontology mapping,where users can collaborate and interact to map theirexisting ontologies and maintain a quality mapping as-set within the community. Such alignments are thenused to achieve ontological mediation for accessing lo-cally hosted data.

An approach related to OntoMediate, that ad-dresses ontology mapping within communities, is theZhadanova and Shvaiko [17] method. The authors pro-posed to use similarity of user and group profiles as adriver for suggesting ontology alignments reuse. Thefocus of that work was on building such profiles to per-sonalise reuse of ontology mappings. In OntoMediate,we are exploring the use of collaborative features (dis-cussions, voting, change proposals) to facilitate the cu-ration and reuse of mappings by the community, to fa-cilitate a social and dynamic integration of distributedknowledge bases. Our approach is novel in the way itaddresses the task of aligning ontologies, by extend-ing and enhancing automatic mapping tools with a fullcommunity support. In our approach, alignments areseen as a resource, built and shared by a community.The community is able to investigate, argue, and cor-rect individual mappings, using various supporting ser-vices provided by OntoMediate.

3 OntoMediate Features for Commu-nity Support

In the OntoMediate project [3] we are studying howsocial interactions, collaboration and user feedback canbe used in a community, in order to facilitate the align-ment of ontologies and to share mapping results.

3.1 OntoMediate System

The prototype has been designed to be extendible,allowing off-the-shelf tools to be integrated and iscomposed of three main subsystems: ontologies anddatasets manager; ontology alignment environment; so-cial interaction environment.

Ontologies and Datasets Manager. This partof the system allows users to register (as well as un-register) the datasets they intend to share with thecommunity and the ontologies that describe their datavocabulary. The ontologies that are loaded onto thesystem need to be aligned with one or more shared on-

tologies in order to enable access to its data by thecommunity.

Ontology Alignment Environment. Our sys-tem provides an API for automated ontology alignmenttools to be plugged in. Examples of tools already in-tegrated with our system are CROSI mapping system[9] and Falcon OA [8]. These tools allow the systemto support the alignment task by proposing to the usersome initial candidate mappings. The system allowsthe user to link his/her local ontologies to shared andagreed ontological models that can be accessed by any-one in the community.

Social Interaction Environment. This function-ality allows community members that deal with similardata - and therefore have a mutual interest to maintaina good quality alignment asset - to socially interactwith each other. The system provides a way to browseand review the local alignments toward shared con-cepts, allowing users to spot errors, provide feedback,propose and discuss alternative solutions and conflict-ing interpretation (see Figure 1). Aim of the socialinteraction system is to exploit community feedbackin order to enhance the overall quality of the ontol-ogy alignment and achieve agreement on semantics ofconcepts by means of community acceptance.

In OntoMediate system, one of the aims is to reuseuser input to increase the quality of data integrationand ease future alignments toward the same target on-tology. One way for achieving this goal is to take intoconsideration lexical information originated by users’ontologies. In fact, lexical labels can be adopted bythe shared model as rdfs:label that can be consideredin future automatic alignment tasks in an attempt toimprove performance and accuracy of automatic map-ping tools. The working assumption is that allowingthe system to learn the community lexicon will improvefuture automatic ontology alignments that usually relyon such information.

3.2 Data Integration support

Because the information could be described differ-ently in different ontologies (e.g. using different sys-tems of measurement) we developed a service for trans-lation of RDF data and queries between aligned on-tologies. So far the features described above allow acommunity to maintain a network of ontologies, datasources and an alignment asset that enable access todata sets that are described with local ontologies. Inorder to integrate data from sources described by dif-ferent ontologies, the collaboratively maintained align-ments must be exploited for translating queries anddata.

Different approaches have been proposed over the

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Figure 1. Ontology evolution forum

years by the semantic web community for dealing withthis task by using alignments for creating:

• rules for the translation of data between twoaligned ontologies, namely the source and the tar-get ontology

• queries for the creation of data compliant to atarget ontology addressing data sources compliantto a source ontology

In OntoMediate the ontological mediation isachieved by means of query translation. Similarly tothe approach adopted by Euzenat et al. [6] the align-ments managed by the users and the topology of thedata network are both exploited to translate queriesand results from one ontology to another. Within theOntoMediate scenario, the shared ontologies are usedto define the SPARQL queries that translate betweenthe terms of one or more local ontologies for accessingthe user data. Moreover, since the results need to bedescribed with the same ontology used for the query inorder to be understood by the issuer of the query, thetranslation mechanism developed allows to retranslatethe results back into the source ontology.

Currently, ontological relations are used to defineequivalences among concepts and object properties.Local entities that are not equivalent to an entity be-longing to a shared ontology, shall be proposed anddiscussed as an extension to the shared model. Align-ments among datatype properties allow to define n : 1relations and complex value transformations, by meansof scripts, needed to bridge different encoding systems.A typical example where such complex transformationsare needed is when different information systems de-cide to encode point coordinates with two different ex-pressions(e.g. is1 with cartesian and is2 with cylindri-cal coordinates). The notion of point is easily map-

pable between the two ontologies (i.e. is1 : Point ≡is2 : Point) but the information described within thetwo concepts are not always directly mappable. Inorder to be able to translate a point from a carte-sian system (x, y, z) to a cylindrical system (ρ, ϕ, z)we need to be able to describe value transformations(i.e. ρ = f(x, y) =

√x2 + y2). For representing such

alignments, the usual representation by quadruple [5]〈e, e′, R, n〉 has been extended: the source entity e isreplaced by an ordered set of entities E and a scriptingfunction s is associated with the alignment. The rep-resentation of an alignment in our system is thereforea quintuple: 〈E, e′, R, n, s〉.

Our translation algorithm visits and rewrites the al-gebraic description of a SPARQL query [4]. This imple-mentation exploits the features provided by Jena APIfor: rewriting the RDF triple patterns present in theoriginal query; obtaining a query that matches the tar-get ontology; retrieving the required instance set; andrearranging the result set to fit the source ontology.

4 Comparison

This section provides a brief comparison of the fea-tures provided by OntoMediate and other tools forcollaborative knowledge construction and sharing [11](the comparison is limited to the tools presented at theCKC 2007 workshop1). Table 1 compares the featuresprovided by some of the latest tools for collaborativeknowledge construction and sharing. All the tools man-age ontological structures except Bibsonomy that doesnot handle properties and instances and SOBOLEOthat does not handle properties. Only Protege and On-toMediate manage ontology alignments but only Onto-Mediate provides query translation capabilities. Bib-

1The comparison is based on the features that the tools hadin the versions that participated in the CKC 2007 challenge.

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Sonomy, Hozo and OntoMediate allow users to managepersonal spaces where the users are free to share onlypart of their ontology and instances. Among the so-cial features compared, OntoMediate is the only toolthat provides both argumentation, discussion and usernotification features.

Table 1. Features comparisonB

ibSonom

y

DB

in

Hozo

OntoW

iki

Collab.

Protege

SO

BO

LEO

OntoM

edia

te

Hierarchy of concepts � � � � � � �Properties � � � � �Instances � � � � � �Ontology mapping �∗ �Query translation �Ratings or voting � � �Personal space � � �History of changes � � � �Discussion � � � �Argumentation � �User notification � �Web browser interface � � � �

5 Summary and Future Work

This paper presented a prototype for supportingontology mapping and data sharing with communityinteractions, where users can collaborate on aligningtheir ontologies, and manually-driven alignments canbe stored and reused later. The alignments can thenbe exported or used to translate queries into differ-ent ontologies for merging results from different datasources. Next, we plan to run experiments to test thevalidity of the social approach, and the usability of theservices and features that it provides. We will alsoimplement services for handling the discovery and ex-ploitation of instance mappings. Such service will beof paramount importance for the data integration tasksince it is likely that information relative to the sametopic shall be scattered in different data sets.

6 AcknowledgementsThis work was funded by a grant awarded to General Dynam-

ics UK Ltd. and the University of Southampton as part of theData and Information Fusion Defence Technology Centre (DIFDTC) initiative. The views and conclusions contained in thisdocument are those of the authors and should not be interpretedas representing the official policies, either expressed or implied,of the UK Ministry of Defence, or the UK Government.

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