Biomedical knowledge base construction from text and its applications in knowledge-based systems

While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledge about people, events, places, etc., domain-specific KBs, such as on health, are equally important, but are less explored. Consequently, a comprehensive and expressive health KB that spans all aspects of biomedical knowledge is still missing. The main goal of this thesis is to develop principled methods for building such a KB and enabling knowledge-centric applications. We address several challenges and make the following contributions: • To construct a health KB, we devise a largely automated and scalable pattern-based knowledge extraction method covering a spectrum of different text genres and distilling a wide variety of facts from different biomedical areas. • To consider higher-arity relations, crucial for proper knowledge representation in advanced domain such as health, we generalize the fact-pattern duality paradigm of previous methods. A key novelty is the integration of facts with missing arguments by extending our framework to partial patterns and facts by reasoning over the composability of partial facts. • To demonstrate the benefits of a health KB, we devise systems for entity-aware search and analytics and for entity-relationshiporiented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.