Section: New Results

Data interlinking

The web of data uses semantic web technologies to publish data on the web in such a way that they can be interpreted and connected together. It is thus important to be able to establish links between these data [7], both for the web of data and for the semantic web that it contributes to feed. We consider this problem from different perspectives.

Interlinking cross-lingual RDF data sets

Participants : Tatiana Lesnikova, Jérôme David [Correspondent] , Jérôme Euzenat.

Rdf data sets are being published with labels that may be expressed in different languages. Even systems based on graph structure, ultimately rely on anchors based on language fragments. In this context, data interlinking requires specific approaches in order to tackle cross-lingualism. We proposed a general framework for interlinking rdf data in different languages and implemented two approaches: one approach is based on machine translation, the other one takes advantage of multilingual references, such as BabelNet.

This year, we evaluated machine translation for interlinking concepts, i.e., generic entities named with a common noun or term, as opposed to individual entities. In previous work, the evaluated method has been applied on named entities. We conducted two experiments involving different thesauri in different languages. The first experiment involved concepts from the TheSoz multilingual thesaurus in three languages: English, French and German. The second experiment involved concepts from the EuroVoc and agrovoc thesauri in English and Chinese respectively. We demonstrated that machine translation can be beneficial for cross-lingual thesauri interlining independently of a dataset structure [12].

This work has been part of the PhD of Tatiana Lesnikova [6] developed in the Lindicle project (§7.1.1).

Uncertainty-sensitive reasoning for inferring sameAs facts in Linked Data

Participants : Manuel Atencia Arcas [Correspondent] , Jérôme David.

A major challenge in data interlinking is to design tools that effectively deal with incomplete and noisy data, and exploit uncertain knowledge. We modelled data interlinking as a reasoning problem with uncertainty. For that purpose, we introduced a probabilistic framework for modelling and reasoning over uncertain rdf facts and rules that is based on the semantics of probabilistic Datalog. We have designed an algorithm, ProbFR, based on this framework. Experiments on real-world datasets have shown the usefulness and effectiveness of our approach for data linkage and disambiguation [9].

This work was carried out in collaboration with Mustafa Al-Bakri and Marie-Christine Rousset (LIG).

Tableau extensions for reasoning with link keys

Participants : Manuel Atencia Arcas [Correspondent] , Jérôme Euzenat, Maroua Gmati.

Link keys allow for generating links across datasets expressed in different ontologies (see §3.3). But they can also be thought of as axioms in a description logic. As such, they can contribute to infer ABox axioms, such as links, or terminological axioms and other link keys. Yet, no reasoning support existed for link keys. We extended the tableau method designed for ALC to take link keys into account [10]. We showed how this extension enables combining link keys with classical terminological reasoning with and without ABox and TBox and generate non trivial link keys.