OWL-NETS: Transforming OWL Representations for Improved Network Inference

Abstract

Our knowledge of the biological mechanisms underlying complex human disease is largely incomplete. While Semantic Web technologies, such as the Web Ontology Language (OWL), provide powerful techniques for representing existing knowledge, well-established OWL reasoners are unable to account for missing or uncertain knowledge. The application of inductive inference methods, like machine learning and network inference are vital for extending our current knowledge. Therefore, robust methods which facilitate inductive inference on rich OWL-encoded knowledge are needed. Here, we propose OWL-NETS (NEtwork Transformation for Statistical learning), a novel computational method that reversibly abstracts OWL-encoded biomedical knowledge into a network representation tailored for network inference. Using several examples built with the Open Biomedical Ontologies, we show that OWL-NETS can leverage existing ontology-based knowledge representations and network inference methods to generate novel, biologically-relevant hypotheses. Further, the lossless transformation of OWL-NETS allows for seamless integration of inferred edges back into the original knowledge base, extending its coverage and completeness.

Fig. 1

Overview of the OWL-NETS Method.

Fig. 2

Query 2 OWL representation network (Left); OWL-NETS abstraction network (middle); and CCDF power-law fit (right). NETS nodes shown in magenta, purple, and teal. Orange ’OWL-Specific Semantic Structure’ nodes are needed to create valid OWL expressions and are only part of the OWL representation networks. NETS edge relations shown in solid and dashed lines.

Fig. 3

Query 3 OWL representation network (Left); OWL-NETS Abstraction Network (middle); and CCDF power-law fit (right). Node size is only for visualization. NETS nodes shown in magenta, purple, and teal. Orange ’OWL-Specific Semantic Structure’ nodes are needed to create valid OWL expressions and are only part of the OWL representation networks. NETS edge relations shown in solid and dashed lines.