iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge
This provides biomedical researchers with an automated tool for unified, current knowledge access, though it is incremental as it builds on existing integration methods.
The study tackled the challenge of accessing up-to-date disease-specific knowledge by proposing a framework to automatically retrieve and integrate such knowledge into a semantic graph, applied to three diseases with exemplary queries demonstrating its potential for retrieval and analysis.
In biomedical research, unified access to up-to-date domain-specific knowledge is crucial, as such knowledge is continuously accumulated in scientific literature and structured resources. Identifying and extracting specific information is a challenging task and computational analysis of knowledge bases can be valuable in this direction. However, for disease-specific analyses researchers often need to compile their own datasets, integrating knowledge from different resources, or reuse existing datasets, that can be out-of-date. In this study, we propose a framework to automatically retrieve and integrate disease-specific knowledge into an up-to-date semantic graph, the iASiS Open Data Graph. This disease-specific semantic graph provides access to knowledge relevant to specific concepts and their individual aspects, in the form of concept relations and attributes. The proposed approach is implemented as an open-source framework and applied to three diseases (Lung Cancer, Dementia, and Duchenne Muscular Dystrophy). Exemplary queries are presented, investigating the potential of this automatically generated semantic graph as a basis for retrieval and analysis of disease-specific knowledge.