OntoSum

Ontologies have been realized as the key technology to shaping and exploiting information for the effective management of knowledge and for the evolution of the Semantic Web and its applications. In such a distributed setting, ontologies establish a common vocabulary for community members to interlink, combine, and communicate knowledge shaped through practice and interaction, binding the knowledge processes of creating, importing, capturing, retrieving, and using knowledge.

However, it seems that there will always be more than one ontology even for the same domain. In such a setting, where different conceptualizations of the same domain exist, information services must effectively answer queries, bridging the gaps between conceptualizations of the same domain. Towards this target, networks of semantically related information must be created at-request. Therefore the alignment (or coordination) of ontologies is a major challenge for bridging the gaps between agents (software and human) with different conceptualizations.

“Simple” cases of heterogeneity include ontologies that use different lexicalizations of the same ontology element (e.g., “car” and “road-vehicle”). More complicated situations appear in cases where ontologies are structured (in terms of concepts’ relations) in completely different ways.

Information integration and effective management of information will be admittedly achieved through reaching an agreement, by producing a single, commonly-agreed and shared reference ontology (ontology merging), or by achieving coordination so that each party uses its own ontology, but with it also establishes concept and relation mappings with other ontologies. In any case, tools for supporting the ontology alignment task are of paramount importance. Specifically, given two ontologies O1 and O2, establishing an alignment between them involves computing pairs of ontology elements (one from O1 and one from O2) that have the same intended meaning.

Our method (CSR: Classification-based Method for the Alignment of Ontologies) which locates subsumption mapping relations between pairs of concepts, each one belonging in a different ontology, can be found here.

We have created three corpora for the evaluation of CSR method, which targets to the computation of subsumption mapping relations. Two of these corpora have been used in the evaluation of mapping systems in the Oriented Matching track of the Ontology Alignment Evaluation Initiative (OAEI) 2009 (download here, more info here).

Related publications are available here.