Exploiting semantics to pbkp_redict potential novel links from dense subgraphs

Abstract:

Knowledge graphs encode semantic knowledge that can be exploited to enhance different data management tasks, e.g., query answering, ranking, or data mining. We tackle the problem of pbkp_redicting interactions between drugs and targets, and propose esDSG, an unsupervised approach able to pbkp_redict links from subgraphs that are not only highly dense, but that comprise both similar drugs and targets. TheesDSG 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 pbkp_redicts potential novel interactions from the computed dense subgraph.We have conducted an initial experimental study on a benchmark of drug-target interactions. Our observed results suggest that esDSG is able to identify interactions in graphs where existing approaches cannot perform equality well. Further, a large number of esDSG pbkp_redictions can be validated using external databases as STITCH and Kegg. These results, although initial, reveal how semantics in conjunction with topological information of the knowledge graph may have a great impact on pattern discovery tasks.

Año de publicación:

2015

Keywords:

Fuente:

scopus