Overall Objectives
Scientific Foundations
Application Domains
New Results
Other Grants and Activities

Section: New Results

Ontology matching and alignment

We pursue our work work on ontology matching and alignment support with basic contributions.


Participants : Cássia Trojahn dos Santos [Contact] , Jérôme Euzenat, Jérôme David.

Evaluation of ontology matching algorithms involves to confront them with test ontologies and to compare the results. Since 2004, we run the Ontology Alignment Evaluation Initiative (OAEI ) which organises evaluation campaigns for assessing the degree of achievement of actual ontology matching algorithms. This year, the evaluation campaign had 15 different teams entered the evaluation which consisted of 6 different sets of tests. The participating systems and evaluation results were presented in the Fourth Workshop on Ontology Matching, that has been held in Shanghai, China [19] [8] .

The main activities carried out in 2010 were related to the automation and execution of the OAEI 2010 campaign, in the framework of the SEALS project (see § 8.2.1 ). It involved the following main tasks:

This work has been used in OAEI 2010 evaluation campaign. More information on OAEI can be found at .

Ontology distances

Participants : Jérôme David [Contact] , Jérôme Euzenat, Giuseppe Pirrò.

Measuring similarity between ontologies can be very useful for different purposes, e.g., finding an ontology to replace another, or finding an ontology in which queries can be translated. This year, we have completed our new family of ontology measures computed in the alignment space, by contrast with more classical measures working in the ontology space [4] . Measures working in the alignment space evaluate the similarity between two ontologies with regard to the available alignments between them. We had introduced two sets of such measures relying on the existence of paths between ontologies or on the ontology entities that are preserved by the alignments. The former accounts for known relations between ontologies, while the latter reflects the possibility to perform actions such as instance import or query translation. These measures have all been implemented in the OntoSim library (§ 5.2 ), that has been used in experiments. The experiments showed that entity preserving measures are correlated with the best ontology space measures. Moreover they show a linear degradation with the alteration of alignments, testifying their robustness.

In addition, we developed work on linguistically-grounded similarity between ontology concepts. While similarity only considers subsumption relations to assess how two objects are alike, relatedness takes into account a broader range of relations (e.g., part-of). In particular, we have presented a framework, which maps the feature-based model of similarity into the information theoretic domain. It introduces a new way to compute information content directly from an ontology structure taking into account the whole set of semantic relations defined in an ontology. The proposed framework enables to rewrite existing similarity measures that can be augmented to compute semantic relatedness. Upon this framework, a new measure called FaITH (Feature and Information THeoretic) has been devised. Extensive experimental evaluations confirmed the suitability of the framework [10] .

Another aspect that has been investigated is how to compute similarity between sentences exploiting not only nous definitions but also other parts of speech, e.g., verbs, [9] . In this respect, as a source of linguistic knowledge, WordNet has been used. Ongoing research concerns how to extend the similarity framework to expressive knowledge representation languages such as Description Logics.

Instance matching for linked data

Participants : François Scharffe, Jérôme Euzenat [Contact] , Jérôme David.

The web of data consists of using semantic web technologies to publish data on the web in such a way that they can be interpreted and connected together. It is thus critical to be able to establish links between these data, both for the web of data and for the semantic web that it contributes to feed. We have proposed a general framework for analysing the task of linking data and we have shown how the diverse techniques developed for establishing these links fit in the framework [14] . We have also proposed an architecture allowing to associate various interlinking systems and to make them collaborate with systems developed for ontology matching that present many commonalities with link discovery techniques. This work will be pursued in the context of the Datalift project (see § 8.1.1 ).

Consistency-based argumentation for matching

Participants : Cássia Trojahn dos Santos [Contact] , Jérôme Euzenat.

We had used argumentation theory for reconciling different views on alignments. However, the argumentation process does not guarantee that the resulting (agreed) alignment is consistent. This year, we have worked on argumentation and consistency checking for alignment agreement. We have provided an argumentation model that combines both argumentation and logical consistency checking for proving consistent agreed alignments. We have evaluated our approach using the Conference dataset of OAEI . However, so far evaluations have shown that if argumentation or consistency checking alone provide definitely better agreed alignments, their combination does not improve on this [12] .

Multilingual ontology matching

Participants : Cássia Trojahn dos Santos [Contact] , Jérôme Euzenat, Giuseppe Pirrò, Jérôme David.

This year we had the occasion of considering matching resources, ontologies and thesauri, using different and multiple natural languages. Hence, we developed specific techniques for dealing with this.

We have specified a minimal API for multilingual ontology matching which offers two strategies, direct translation-based and indirect. The first strategy considers direct match between two ontologies, with the help of external resources [11] , i.e., translators, while indirect alignment [15] , proposed by Jung and colleagues, is based on composition of alignments. We have provided an implementation for both approaches. For evaluating them, we have extended the OAEI benchmark test case for including a Portuguese ontology.

We also had the opportunity to experiment with thesauri expressed in SKOS (Simple Knowledge Organization System) involving many different languages in the context of the TAE project (see § 7.1.1 ). In this respect, we analysed linguistic properties of thesauri and the usage of semantic similarity measures to assess similarity between labels in concept definitions. We also developed techniques for finding consensus between matchers in different pairs of languages which have already shown interesting results. Current research concerns the definition of a strategy to identify anchors between SKOS concepts and ConceptScheme. Anchors may provide a relevant basis on which applying more sophisticated strategies.


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