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Exploiting semantics from ontologies and shared annotations to find patterns in annotated linked open data

Abstract:

Linked Open Data initiatives have made available a diversity of collections that domain experts have annotated with controlled vocabulary terms from ontologies. The challenge is to explore these rich and complex annotated datasets, together with the domain semantics captured within ontologies, to discover patterns of annotations across multiple concepts that may lead to potential discoveries. We identify an annotation signature between a pair of concepts based on shared annotations and ontological relatedness. Formally, an annotation signature is a partitioning of the edges that represent the relationships between shared annotations. A clustering algorithm named AnnSigClustering is proposed to generate annotation signatures. Evaluation results over drug and gene datasets demonstrate the effectiveness of using annotation signatures to find patterns.