Knowledge node and relation detection

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 relation 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 evaluation results using the BLEU and cosine similarity measures.