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Hochschulschrift

Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems

MPG-Autoren
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Ernst,  Patrick
Databases and Information Systems, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Ernst, P. (2018). Biomedical Knowledge Base Construction from Text and its Applications in Knowledge-based Systems. PhD Thesis, Universität des Saarlandes, Saarbrücken. doi:10.22028/D291-27105.


Zitierlink: http://hdl.handle.net/21.11116/0000-0001-1864-4
Zusammenfassung
While general-purpose Knowledge Bases (KBs) have gone a long way in compiling comprehensive knowledgee 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-relationship-oriented exploration. Extensive experiments and use-case studies demonstrate the viability of the proposed approaches.