Learning to Match Ontologies on the Semantic Web

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Learning to Match Ontologies on the Semantic Web (2003)

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by Anhai Doan , Jayant Madhavan , Robin Dhamankar , Pedro Domingos , Alon Halevy

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@MISC{Doan03learningto,
    author = {Anhai Doan and Jayant Madhavan and Robin Dhamankar and Pedro Domingos and Alon Halevy},
    title = {Learning to Match Ontologies on the Semantic Web},
    year = {2003}
}

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Abstract

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.

Keyphrases

semantic web match ontology domain constraint accurate semantic mapping describe experiment ontology glue several real-world domain key feature semantic mapping matching process well-founded probabilistic definition data instance web scale similar concept several practical similarity measure well-defined similarity notion multiple type commonsense knowledge different type information processing ontology mapping process taxonomic structure complex mapping many different ontology

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