OWL reasoning over big biomedical data

Recently, the emerging accumulation of biomedical data on the Web (e.g. vast amounts of protein sequences, genes, gene products, drugs, diseases and chemical compounds, etc.) has shaped a big network of isolated professional knowledge. Embedded with domain knowledge from different disciplines all regarding to human biological systems, the decentralized data repositories are implicitly connected by human expert knowledge. Lots of biomedical data sources are published separately in the form of semantic ontologies represented by Web Ontology Language (OWL) syntax, which is naturally based on linked graphs. When we are faced with such massive, disparate and interlinked data, biomedical data analysis becomes a challenge. In this paper, we present a general OWL reasoning framework for the analysis of big biomedical data and implement a MapReduce-based property chain reasoning prototype system. OWL reasoning method is ideally suitable for problems involved complex semantic associations because it is able to infer logical consequences based on a set of asserted rules or axioms. MapReduce framework is used to solve the problem of scalability. In our experiment, we focus on the discovery of associations between Traditional Chinese Medicine (TCM) and Western Medicine (WM). The results show the system achieves high performance, accuracy and scalability.