Model-based Merging of Open-Domain Ontologies

Conceptual knowledge, encoded in ontologies or knowledge graphs, plays an essential role in many areas, including Semantic Web, Information Retrieval, and Natural Language Processing. Considerable attention has recently been devoted to the problem of unifying and linking available ontologies. While the vast majority of existing work focuses on matching or aligning resources, in this paper, we investigate the application of belief merging theory to ontology merging to obtain a unique perspective. We consider the setting where different ontologies share the same terminology (i.e., assuming that they are already mapped to each other). However, they express knowledge in different and potentially conflicting ways. In order to get a unified view of the knowledge conveyed by the different ontologies, we start by providing a semantic-based merging model. Our method retrieves all the interpretations in which the outcome can be found. We support demonstrating the method's effectiveness by an experimental evaluation of the method on existing open-domain ontologies.