Detection of Knowledge Nodes and Relations

Jian Qin, Bei Yu, Liya Wang

Research output: Contribution to conferencePaperpeer-review


A bottleneck problem in detecting knowledge nodes and their relations is how to extract accurately and correctly and codify the complex knowledge assertions from full-text documents (human intelligence) into the format of “machine intelligence” (computer-processable knowledge assertions). This paper reports a preliminary study that aims at this bottleneck problem by starting from the fundamentals of KR—representing knowledge from full-text documents by using knowledge node and rela-tion recognition methods and tools. We collected data from full-text biomedical research publications and used manual and automatic tools to investigate the strengths and limitations of these methods. The findings show that MetaMap did a better job in detecting concepts from texts while SemRep is capable of extract relations between k-nodes. The paper presents the findings from the perspectives of degree of abstraction, types of k-nodes and relations, and linguistic structures and the eval-uation results using the BLEU and cosine similarity measures.
Original languageEnglish (US)
Number of pages9
StateUnpublished - 2018
EventAssociation for Information Science and Technology Annual Meeting - Vancouver, Canada
Duration: Nov 11 2018Nov 15 2018
Conference number: 2018


ConferenceAssociation for Information Science and Technology Annual Meeting
Abbreviated titleASIST
Internet address


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