2004 | OriginalPaper | Buchkapitel
Ontology Matching: A Machine Learning Approach
verfasst von : AnHai Doan, Jayant Madhavan, Pedro Domingos, Alon Halevy
Erschienen in: Handbook on Ontologies
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
This chapter studies ontology matching: the problem of finding the semantic mappings between two given ontologies. This problem lies at the heart of numerous information processing applications. Virtually any application that involves multiple ontologies must establish semantic mappings among them, to ensure interoperability. Examples of such applications arise in myriad domains, including e-commerce, knowledge management, e-learning, information extraction, bio-informatics, web services, and tourism (see Part D of this book on ontology applications).Despite its pervasiveness, today ontology matching is still largely conducted by hand, in a labor-intensive and error-prone process. The manual matching has now become a key bottleneck in building large-scale information management systems. The advent of technologies such as the WWW, XML, and the emerging Semantic Web will further fuel information sharing applications and exacerbate the problem. Hence, the development of tools to assist in the ontology matching process has become crucial for the success of a wide variety of information management applications.In response to the above challenge, we have developed GLUE, a system that employs learning techniques to semi-automatically create semantic mappings between ontologies. We shall begin the chapter by describing a motivating example: ontology matching on the Semantic Web. Then we present our GLUE solution. Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings.