Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Partnerships and Cooperations
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Section: New Results

Dynamic aspects of networks of ontologies

Huge quantities of data described by ontologies and linked together are made available. These are generated in an independent manner by autonomous providers such as individuals or companies. They are heterogeneous and their joint exploitation requires connecting them, ending up as a mesh of reticulated knowledge.

However, data and knowledge have to evolve facing changes in what they represent, changes in the context in which they are used and connections to new data and knowledge sources. As their production and exchange are growing larger and more connected, their evolution is not anymore compatible with manual curation and maintenance. We work towards their continuous evolution as it is critical to their sustainability.

Two different approaches are currently explored.

Evolution of ontology networks and linked data

Participants : Adam Sanchez Ayte [Correspondent] , Jérôme David, Jérôme Euzenat.

As link keys are obtained by statistical analysis of datasets (§ 6.3.4 ), they are both data-dependent and computation-intensive. Therefore, their recalculation should be avoided if possible. We are developing methods to analyse if changes performed in the data, necessarily require link key recomputation.

To reach this goal, we are developing an approach considering datasets as logical theories. In this context, changes that affect a link key are meta-logical operations. We adopt the framework of belief revision to define postulates that evolution operators must satisfy.

This work is part of the PhD thesis of Adam Sanchez Ayte developed in the Lindicle project (§ 8.1.2 ).

Cultural alignment repair

Participant : Jérôme Euzenat [Correspondent] .

Alignments between ontologies may be established through agents holding such ontologies attempting at communicating and taking appropriate action when communication fails. This approach, that we call cultural repair, has the advantage of not assuming that everything should be set correctly before trying to communicate and of being able to overcome failures. We tested this approach on alignment repair, i.e., the improvement of incorrect alignments. For that purpose, we performed a series of experiments in which agents react to mistakes in alignments. Agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We showed that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments. The obtained results are on par with a baseline of state-of-the-art alignment repair algorithms [7] [17]

The benchmarks, results and software are available at .