Abstract - Mining Patterns and Associations from Semantic Annotations
Linked Open Data initiatives have made available a diversity of scientific collections where scientists have annotated entities in Linked Open datasets with controlled vocabulary terms from ontologies. Annotations encode scientific knowledge which is represented in annotated graphs.
Determining relatedness between annotated entities becomes a building block for mining patterns and for the prediction of potential novel associations. First, a novel annotation similarity measure named AnnSim will be defined. AnnSim measures the relatedness between two entities based on the similarity of their annotations. Results of empirically studies will report on the performance of AnnSim on real-world datasets of drugs, diseases, and genes.
Second, the problem of predicting interactions between annotated entities will be described as well as an unsupervised method for link prediction, called semEP. Capability prediction of semEP will be illustrated in the context of the discovery of interactions between drugs and targets; semEP behavior will be compared with respect to existing machine learning approaches.
Marıa-Esther Vidal is a Full Professor of the Computer Science Department and Dean Assistant for Research on Applied Science and Engineering at the Universidad Simón Bolívar, Caracas, Venezuela. Her research in information management covers information integration, federated databases, graph data management, Linked Open Data, and the Semantic Web. Maria-Esther has addressed some of the most important challenges in selecting and modeling sources, rewriting queries, cost based optimization, graph query processing and optimization, and benchmarks for federated SPARQL query processing. Her proposed strategies have had significant relevant from the early days of information integration in the Web, in the late 90s, and to the emergence of the Semantic Web and SPARQL endpoints. She has published her research results in the premier conferences and journals in Database Management, Artificial Intelligence, and the Semantic Web.