Knowledge Representation and Management: Benefits and Challenges of the Semantic Web for the Fields of KRM and NLP

Summary

Objectives

To summarize excellent current research in the field of knowledge representation and management (KRM).

Method

A synopsis of the articles selected for the IMIA Yearbook 2011 is provided and an attempt to highlight the current trends in the field is sketched.

Results

This last decade, with the extension of the text-based web towards a semantic-structured web, NLP techniques have experienced a renewed interest in knowledge extraction. This trend is corroborated through the five papers selected for the KRM section of the Yearbook 2011. They all depict outstanding studies that exploit NLP technologies whenever possible in order to accurately extract meaningful information from various biomedical textual sources.

Conclusions

Bringing semantic structure to the meaningful content of textual web pages affords the user with cooperative sharing and intelligent finding of electronic data. As exemplified by the best paper selection, more and more advanced biomedical applications aim at exploiting the meaningful richness of free-text documents in order to generate semantic metadata and recently to learn and populate domain ontologies. These later are becoming a key piece as they allow portraying the semantics of the Semantic Web content. Maintaining their consistency with documents and semantic annotations that refer to them is a crucial challenge of the Semantic Web for the coming years.

Keywords

Semantic Web - Natural language processing (NLP) - knowledge extraction - semantic annotation

 

• References

• 1 Berners-Lee T, Hendler J, Lassila O. The Semantic Web. Sci Am 2001; 34-43.
• 2 Feigenbaum L, Herman I, Hongsermeier T, Neumann E, Stephens S. The Semantic Web in action. Sci Am 2007; Dec; 297 (06) 64-71.
• 3 Klein D, Manning CD. Accurate unlexicalized parsing. Proc of the 41st Meeting of the Association for Computational Linguistics 2003; 423-30.
• 4 Bontcheva K, Tablan V, Maynard D, Cunningham H. Evolving GATE to Meet New Challenges in Language Engineering. Nat Lang Eng 2004; 10: 349-73.
• 5 Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Nat Lang Eng 2004; 10 (3:4): 327-48.
• 6 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.
• 7 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.
• 8 Rassinoux AM. Decision Support, Knowledge Representation and Management: Transforming Textual Information into Useful Knowledge. In: Kulikowski C, Geissbuhler A, editors.Yearb Inform Med 2010; 64-7.
• 9 Bui QC, Nualláin BO, Boucher CA, Sloot PM. Extracting causal relations on HIV drug resistance from literature. BMC Bioinformatics 2010; 11: 101.
• 10 Cao YG, Cimino JJ, Ely J, Yu H. Automatically extracting information needs from complex clinical questions. J Biomed Inform 2010; Dec; 43 (06) 962-71.
• 11 Coulet A, Shah NH, Garten Y, Musen M, Altman RB. Using text to build semantic networks for pharmacogenomics. J Biomed Inform 2010; Dec; 43 (06) 1009-19.
• 12 Denny JC, Peterson JF, Choma NN, Xu H, Miller RA, Bastarache L, Peterson NB. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010; Jul-Aug; 17 (04) 383-8.
• 13 Jahiruddin Abulaish M, Dey L. A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora. J Biomed Inform 2010; Dec; 43 (06) 1020-35.
• 14 Bodenreider O. Lexical, terminological and ontological resources for biological text mining. In: Ananiadou S, McNaught J, editors. Text mining for biology and biomedicine: Artech House 2006; 43-66.
• 15 Maynard D, Li Y, Peters D. NLP Techniques for Term Extraction and Ontology Population. In: Buitelaar P, Cimiano P. editors. Bridging the Gap between Text and Knowledge - Selected..
• 16 Liu K, Hogan WR, Crowley RS. Natural Language Processing methods and systems for biomedical ontology learning. J Biomed Inform 2011; 44: 163-79.
• 17 Uren V, Cimiano P, Iria J, Handschuh S, Vargas-Vera M, Motta E, Ciravegna F. Semantic annotation for knowledge management: Requirements and a survey of the state of the art. Journal of Web Semantics 2006; 14-28.
• 18 Bada M, Hunter L. Desiderata for ontologies to be used in semantic annotation of biomedical documents. J Biomed Inform 2011; 44 (01) 94-101.