Semantic enrichment in ontologies for analysis and matching

Ontology—a formal, explicit, shared conceptualization of a domain—is intended to facilitate semantic interoperability among distributed and intelligent information systems where diverse software components, computing devices, knowledge, and data, are involved. Since a single global ontology is no longer sufficient to support a variety of tasks performed on differently conceptualized knowledge, ontologies have proliferated in multiple forms of heterogeneity—terminological heterogeneity, taxonomical heterogeneity, schematic heterogeneity, and instantiation heterogeneity—even for the same domain, and such ontologies are called heterogeneous ontologies. For interoperating among information systems through heterogeneous ontologies, mapping mechanisms need to bridge their knowledge gaps. Ontology matching (or mapping) is a process of finding correspondences between semantically related entities in heterogeneous ontologies. The main aim of this research is to deal with wide-scale semantic heterogeneity in ontology matching. Although several efforts in ontology mapping have already been contributed, they have different focuses, assumptions, and limitations. A common point among existing methods is that possible correspondences between two ontologies are determined by the similarity of entity names; this is known as name-based matching. In order to decide semantic correspondences between concepts, those methods need to analyze the similarities between all related properties and instances; this is known as content-based matching. In the case of wide-scale semantic heterogeneity, content-based matching becomes complex, and user’s approval or expert-interaction needs to verify mapping results. In my research, I focus on two issues. The first issue is that the chance of correspondence between two terminologically quite different concepts is very less or not obtainable through name-based matching, because the name of a concept cannot express the precise semantics of the concept. In practice, two concepts with the same name may have different semantics, or two differently-naming concepts may have the same semantics. Thus, what is an alternative approach besides name-based matching, to find the possible correspondences between terminologically heterogeneous ontologies? The second issue is how to reduce complexity, concerning wide-scale semantic heterogeneity in content-based matching. To accomplish the major aim and focuses, my underlying assumption is the more explicit semantics is specified in ontologies, the feasibility of matching will be greater. In order to improve the accuracy and automation of mapping processes, it is necessary that ontologies be well conceptualized with adequate semantics. Hence, an important step in handling semantic heterogeneity should be the attempt to enrich (and clarify) the semantics of concepts in ontologies. Therefore, I proposed a semantically-enriched model of ontologies (called EnOntoModel) in which every domain concept is treated as a sort—an entity type that carries a criteria for determining the individuation, persistence, and identity of its instances— regarding every individual defined in a universe of discourse is countable and identifiable. In the philosophical literature, ontological concepts can be classified into four disjoint sort categories: type, quasi-type, role, and phase. I set up a logic-based formal system