2020/06/07 by Maulik R. Kamdar, Kamdar, Maulik R., Mark A. Musen +1
Computer Science · Biochemistry, Genetics and Molecular Biology · #Semantic Web and Ontologies #Biomedical Text Mining and Ontologies
paper · pdf · doi:10.48550/arxiv.2006.04161
While the biomedical community has published several "open data" sources in\nthe last decade, most researchers still endure severe logistical and technical\nchallenges to discover, query, and integrate heterogeneous data and knowledge\nfrom multiple sources. To tackle these challenges, the community has\nexperimented with Semantic Web and linked data technologies to create the Life\nSciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from\nmore than 80 publicly available biomedical linked data graphs into an LSLOD\nschema graph and conduct an empirical meta-analysis to evaluate the extent of\nsemantic heterogeneity across the LSLOD cloud. We observe that several LSLOD\nsources exist as stand-alone data sources that are not inter-linked with other\nsources, use unpublished schemas with minimal reuse or mappings, and have\nelements that are not useful for data integration from a biomedical\nperspective. We envision that the LSLOD schema graph and the findings from this\nresearch will aid researchers who wish to query and integrate data and\nknowledge from multiple biomedical sources simultaneously on the Web.\n