Moving beyond sameAs with PLATO: Partonomy Detection for Linked Data
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The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.
Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.
In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.
Yeh, P. Z.,
& Sheth, A. P.
(2012). Moving beyond sameAs with PLATO: Partonomy Detection for Linked Data. Proceedings of the 23rd ACM Conference on Hypertext and Social Media, 33-42.
Presented at the 23rd ACM Conference on Hypertext and Social Media, Milwaukee, WI, June 25-28, 2012.