The semantic web relies on the expression of formalized knowledge on the web (in languages like RDF). Like the web, the semantic web will have to be distributed and heterogeneous. As such, the integration of resources found on the semantic web is one of its main problems. For contributing solving this problem, data is expressed in the framework of ontologies (theories describing the vocabulary used for expressing data). However, ontologies themselves can be heterogeneous and have to be reconciled.
One way to reconcile ontologies is to find the correspondences between their entities. This is called ontology matching  and the resulting set of correspondences is called an alignment. Ontology matching provides a set of correspondences supposed to hold between entities of two ontologies provided as input. A correspondence is defined by a relation between two entities (which can be classes, individuals, properties, termes or formulas involving such terms).
Context-based ontology matching is a relatively new way of finding relationships between two ontologies. It works by taking advantage of intermediate ontologies to which the two ontologies to be matched are connected. Then, the relationships between connected entities are composed to provide a correspondences between these entities. There have been several systems implementing context-based matching in specific ways. In particular, some work have considered using one intermediate domain-specific ontology in medicine , some systems use upper-level ontologies, while Scarlet  was designed to use all the ontologies on the web.
We have started to design a framework which offers much flexibility in the way context-based matching can be achieved. This framework reveals that there are many options in designing context-based matching algorithms:
The goal of this post-doctoral position is to investigate this framework on the theoretical and/or experimental level. This can be by implementing and experimenting this framework and to experimentally determine what are the most efficient combinations of parameters and why. This can also be in precisely defining the algorithms and showing how they approach an ideally complete algorithm.
Qualification: PhD or equivalent in computer science. Knowledge in knowledge representation, ontologies or ontology matching is welcome.
Hiring date: as soon as possible.
Place of work: The position is located at INRIA Grenoble Rhône-Alpes, Montbonnot (near Grenoble, France) a main computer science research lab, in a stimulating research environment. Research will be carried out in the Exmo team under the supervision of Jérôme Euzenat. It will require the involvement of the candidate in related projects.
Duration: 12 months (extendible to longer period)
Salary: 2357 EUR/month (before charges)
For further information, contact
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Note that, however, the procedure can be faster if judged necessary. In addition, send the same files to the contact above.
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