Graph-KD: Exploring Relational Information for Knowledge Discovery

Roland Roller, Gaurav Vashisth, Philippe Thomas, He Wang, Michael Mikhailov, Mark Stevenson

In: Proceedings of ISWC 2019 Posters & Demonstrations. International Semantic Web Conference (ISWC-2019) 18th October 26-30 Auckland New Zealand ISWC 2019.


This paper presents Graph-KD, a tool to navigate through large rela-tional knowledge sources. Graph-KD provides methods to understand relation-ships between concepts using open discovery, closed discovery and knowledgeinference. The purpose of the tool is the support of biomedical knowledge dis-covery and exploration. It is primarily intended to be used by medical researchersand presents a use case involving millions of relations from UMLS. Graph-KDis able to process even large graphs efficiently and can be accessed via a web-interface (

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German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz