Semantic Graph Mining for e-Science

In this paper, we present a methodology, called Semantic Graph Mining, for computer-aided extraction of actionable rules from consolidated semantic graphs of statements. First, generate semantic annotations of a set of heterogeneous knowledge/information resources in terms of domain ontology. Second, merge a semantic graph by means of semantic integration of the annotated resources. Third, discover and recognize patterns from the graph. Fourth, generate and evaluate a set of candidate rules, which are organized and indexed for interactive discovery of actionable rules. As initial implementation efforts of the methodology, a generic architecture of specialized knowledge discovery services is proposed, and an application in biomedicine is initiated.