Knowledge Representation and Management - Rassinoux, A.-M.; Section Editor for the IMIA Yearbook Section on Knowledge Representation and Management (Yearbook of Medical Informatics, 2010)
Objectives: To summarize current outstanding research in the field of knowledge representation and management.Method: Synopsis of the articles selected for the IMIA Yearbook 2010.Results: Four interesting papers, dealing with structured knowledge, have been selected for the section knowledge representation and management. Combining the newest techniques in computational linguistics and natural language processing with the latest methods in statistical data analysis, machine learning and text mining has proved to be efficient for turning unstructured textual information into meaningful knowledge. Three of the four selected papers for the section knowledge representation and management corroborate this approach and depict various experiments conducted to. extract meaningful knowledge from unstructured free texts such as extracting cancer disease characteristics from pathology reports, or extracting protein-protein interactions from biomedical papers, as well as extracting knowledge for the support of hypothesis generation in molecular biology from the Medline literature. Finally, the last paper addresses the level of formally representing and structuring informa- tion within clinical terminologies in order to render such information easily available and shareable among the health informatics com- munity.Conclusions: Delivering common powerful tools able to automati- cally extract meaningful information from the huge amount of elec- tronically unstructured free texts is an essential step towards promot- ing sharing and reusability across applications, domains, and institutions thus contributing to building capacities worldwide.
Keywords: Structured knowledge; natural language processing (NLP); text mining; knowledge extraction; knowledge representation