Abstract
This paper proposes a similarity ranking method for entities in the real world. Real world entities like people or objects often have some relationship between themselves. Finding such relationships from real world data can greatly enhance recognition of real world situations. However, it is difficult to capture such relationships from real world sensors alone. Nowadays, activities of people are often shared via Web. The activities can be represented as a relationship between people with shared items such as books, movies or other items. In semantic Web research, such relational information has been modeled in ontologies. The proposed ranking method of this paper is a method that finds meaningful relationships between entities in ontologies. In the first step, the method discovers pairs of entities which have meaningful connections in an ontology. Then it ranks the pairs according to similarities between entities. Unlike previous work, the proposed method assumes not only instance level connections, but also ontology schema level connections. This approach enables machines to access previously hidden indirect relationships into the similarity rankings. The experiments using an existing people-experience ontology show that the proposed method outperforms previous methods.
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Han, YJ., Park, SB., Lee, SJ., Park, S.Y., Kim, K.Y. (2010). Ranking Entities Similar to an Entity for a Given Relationship. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_38
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DOI: https://doi.org/10.1007/978-3-642-15246-7_38
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