Abstract
Linked Open Data initiatives have made available a diversity of collections that domain experts have annotated with controlled vocabulary terms from ontologies. We identify annotation signatures of linked data that associate semantically similar concepts, where similarity is measured in terms of shared annotations and ontological relatedness. Formally, an annotation signature is a partition or clustering of the links that represent the relationships between shared annotations. A clustering algorithm named AnnSigClustering is proposed to generate annotation signatures. Evaluation results over drug and disease datasets demonstrate the effectiveness of using annotation signatures to identify patterns among entities in the same cluster of a signature.
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© 2014 Springer International Publishing Switzerland
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Palma, G., Vidal, ME., Raschid, L., Thor, A. (2014). Exploiting Semantics from Ontologies and Shared Annotations to Partition Linked Data. In: Galhardas, H., Rahm, E. (eds) Data Integration in the Life Sciences. DILS 2014. Lecture Notes in Computer Science(), vol 8574. Springer, Cham. https://doi.org/10.1007/978-3-319-08590-6_12
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DOI: https://doi.org/10.1007/978-3-319-08590-6_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08589-0
Online ISBN: 978-3-319-08590-6
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