Abstract
Scientific publications are the most important resources available to the research communities. Researchers want their work to be widely recognized and available and also need powerful search engines to identify other publications and researchers working in the same area. Therefore, a good representation and organization of scientific products is crucial for an accurate retrieval of information. This paper describes an approach for automated population and semantic enrichment of an ontology model that represents scientific publications. Specifically, the type of enrichment used in this approach consists of implementing semantic similarity measurements between publications. Several experiments were performed to identify the best similarity measurement, using a statistical approach and the precision of the measurements.
Highlights
Universities and research institutions count with highly specialized researchers who are continuously producing data, information, papers, tools, etc
This paper describes an approach for automated population and semantic enrichment of an ontology model that represents scientific publications
The scientific products and publications generated by researchers are an important resource available to the research communities
Summary
Universities and research institutions count with highly specialized researchers who are continuously producing data, information, papers, tools, etc. This paper describes an approach for automated population and semantic enrichment of an ontology model that represents scientific publications. This paper reports an approach to automatically populate and enrich a scientific publication ontology model.
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