Foundations of Data Interoperability on the Web: A Web Science Perspective

摘要

In this paper, when we use the term ontology, we are primarily referring to linked data in the form of RDF(S). The problem of ontology mapping has attracted considerable attention over the last few years, as the deployment of ontologies is increasing with the advent of the Web of Data. We identify two sharply distinct goals for ontology mapping, based on real-world use cases. These goals are: (i) ontology development, and (ii) facilitating interoperability. We systematically analyze the goals, side-by-side, and contrast them for the first time. Our analysis demonstrates the implications of the goals on ontology mapping and mapping representation. Many studies on ontology mapping have focused on ontology merging. Ontology merging is an ontology development task (goal ⅰ). With the increase in the number of web-based information systems that utilize ontologies, the need for facilitating interoperability between these systems is becoming more visible (goal ⅱ). We show the consequences of focusing on interoperability with illustrative examples and provide an in-depth comparison to the information integration problem in databases. The consequences include: (ⅰ) an emphasis on class matching, as a critical part of facilitating interoperability, and (ⅱ) an emphasis on the representation of correspondences, since the merging of ontologies is not suitable for interoperability. For class matching, various class similarity metrics are formalized and an algorithm which utilizes these metrics is designed. For representation, we present a novel W3C-compliant representation, named skeleton. An algorithm for creating the skeleton, for interoperability between ontologies, is also developed. Finally, we experimentally evaluate the effectiveness of the class similarity metrics on real-world ontologies.