Exploiting Semantics to Predict Potential Novel Links from Dense Subgraphs

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Provided by: RWTH Aachen University
Topic: Data Management
Format: PDF
Knowledge graphs encode semantic knowledge that can be exploited to enhance different data management tasks, e.g., query answering, ranking, or data mining. The authors tackle the problem of predicting interactions between drugs and targets, and propose esDSG, an unsupervised approach able to predict links from subgraphs that are not only highly dense, but that comprise both similar drugs and targets. The esDSG approach extends a state-of-the-art approximate densest subgraph algorithm with knowledge about the semantic similarity of the nodes in the original graph, and then predicts potential novel interactions from the computed dense subgraph.
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