Knowledge Representation and Management

Summary

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

 

• References

• 1 Popowich F. Using text mining and natural language processing for health care claims processing. SIGKDD Exploration Newsletter 2005; 07 (01) 59-66.
• 2 Miyao Y, Sagae K, Saetre R, Matsuzaki T, Tsujii J. Evaluating contributions of natural language parsers to protein-protein interaction extraction. Bioinformatics 2009; Feb 1; 25 (03) 394-400.
• 3 Roos M, Marshall MS, Gibson AP, Schuemie M, Meij E, Katrenko S. et al. Structuring and extracting knowledge for the support of hypothesis generation in molecular biology. BMC Bioinformatics 2009; 10 Suppl 10 S9.
• 4 Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008; 128-44.
• 5 Friedman C, Johnson S. Natural language and text processing in biomedicine. In: Shortliffe E, Cimino JJ. editors. Biomedical Informatics Computer Applications in Health Care and Biomedicine. 2006
• 6 Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Brief Bioinform 2005; Mar; 06 (01) 57-71.
• 7 Rassinoux AM. Decision Support, Knowledge Representation and Management: Structuring Knowledge for Better Access. In: Geissbuhler A, Kulikowski C. editors. IMIA Yearbook of Medical Informatics 2008. Methods Inf Med 2008; 47 Suppl 1: 80-2.
• 8 Feigenbaum L, Herman I, Hongsermeier T, Neumann E, Stephens S. The Semantic Web in Action. Scientific American Magazine 2007; 297: 90-7.
• 9 https://cabig-kc.nci.nih.gov/Vocab/KC/index.php/OHNLP
• 10 Savova G, Kipper-Schuler KC, Buntrock J, Chute C. UIMA-based clinical information extraction system. Language Resources and Evaluation Conference 2008 (LREC) Towards enhanced interoperability for large HLT systems: UIMA for NLP; Marrakech. Morocco: 2008
• 11 Pakhomov J, Buntrock J, Duffy P. High throughput modularized NLP system for clinical text. In: Proceedings of the Association for Computational Linguistics (ACL’05) 2005; 25-8.
• 12 Coden A, Savova G, Sominsky I, Tanenblatt M, Masanz J, Schuler K. et al. Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. J Biomed Inform 2009; Oct; 42 (05) 937-49.
• 13 Rassinoux AM. Decision Support, Knowledge Representation and Management: Towards Interoperable Medical terminologies. In: Geissbuhler A, Kulikowski C. editors. IMIAYearbook of Medical Informatics 2009. Methods Suppl. 2009: 99-102.
• 14 Rosenbloom ST, Brown SH, Froehling D, Bauer BA, Wahner-Roedler DL, Gregg WM. et al. Using SNOMED CT to Represent Two Interface Terminologies. J Am Med Inform Assoc 2009; Jan- Feb; 16 (01) 81-8.
• 15 Yu AC. Methods in biomedical ontology. J Biomed Inform 2006; 39: 252-66.
• 16 Rector AL, Brandt S. Why do it the hard way? The case for an expressive description logic for SNOMED. J Am Med Inform Assoc 2008; 15 (06) 744-51.
• 17 Chen ES, Maloney FL, Shilmayster E, Goldberg HS. Laying the groundwork for enterprise-wide medical language processing services: architecture and process. AMIA Annu Symp Proc 2009; Nov 14; 2009: 97-101.