Summary

In BMFacts, we aim to construct an RDF triplestore of such biomedical facts. It will use Semantic Web technologies and Linked Data to manage and exploit the resulting knowledge base, called Biomedical Facts Repository (BMFR). BMFR uses UMLS codes to identify concepts within inferred triples and proposes new types of binary associations, using and extending relations from the UMLS Semantic Network. We want to investigate the potential of BMFR for supporting the knowledge needed in health related decision support and clinical document retrieval. Linked Data principles will be applied in order to extend the content of the BMFR with external datasets and support translational medicine through the generated triples.

The content of BMFR will furthermore be refined in three ways: (i) by comparing to facts from the Linking Open Data (LOD) cloud, (ii) by using additional metadata and extracts from MEDLINE, and (iii) by matching free-text renderings of the predications against large medical reference corpora.

After this cleansing process, the BMFR will be benchmarked using two application scenarios: (i) a question answering framework targeting laypersons' information needs regarding diabetes mellitus and related diseases, for which a gold standard exists; and (ii) a clinical query infrastructure on a corpus of anonymised clinical texts.