Using Biomedical Terminological Resources for Information Retrieval

Using Biomedical Terminological Resources for Information Retrieval

Piotr Pezik (European Bioinformatics Institute, UK), Antonio Jimeno Yepes (European Bioinformatics Institute, UK) and Dietrich Rebholz-Schuhmann (European Bioinformatics Institute, UK)
DOI: 10.4018/978-1-60566-274-9.ch004
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The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a number of example resources which can be used to compile terminologies for biomedical IR and explain some of the common problems with such resources including redundancy, term ambiguity, and insufficient coverage of concepts and incomplete Semantic organization of such resources for text mining purposes. They also discuss some techniques used to address each of these deficiencies, such as static polysemy detection as well as adding terms and linguistic annotation from the running text. In the second part of the chapter, the authors show how query expansion based on using synonyms of the original query terms derived from terminological resources potentially increases the recall of IR systems. Special care is needed to prevent a query drift produced by the usage of the added terms and high quality word sense disambiguation algorithms can be used to allow more conservative query expansion. In addition, they present solutions that help focus on the user’s specific information need by navigating and rearranging the retrieved documents. Finally, they explain the advantages of applying terminological and Semantic resources at indexing time. The authors argue that by creating a Semantic index with terms disambiguated for their Semantic types and larger chunks of text denoting entities and relations between them, they can facilitate query expansion, reduce the need for query refinement and increase the overall performance of Information Retrieval. Semantic indexing also provides support for generic queries for concept categories, such as genes or diseases, rather than singular keywords.
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Compilation Of Lexical Resources

A number of life science data resources lending support to text mining solutions are available, although they differ in quality, coverage and suitability for IR solutions. In the following section we provide an outline of the databases commonly used to aid Information Retrieval and Information Extraction.

Public Resources for Biomedical and Chemical Terminologies TheUnified Medical Language System (UMLS)a: is a commonly used terminological resource provided by the National Library of Medicine. UMLS is a compilation of several terminologies and it contains terms denoting diseases, syndromes and gene ontology terms among others. The UMLS is characterized by a wide coverage and a high degree of concept type heterogeneity, which may make it difficult to use when a specific subset of terms is required. The UMLS Metathesaurus forms the main part of UMLS and it organizes over 1 million concepts denoted by 5 million term variants. The Metathesaurus has been used for the task of named entity recognition e.g. (Aronson et al., 2001). Also, more specialized subsets have been compiled out of this resource and used for the identification of disease names, e.g. (Jimeno et al., 2008). An assessment of UMLS’s suitability for language processing purposes was carried out by (McCray et al., 2001).

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Table of Contents
Violaine Prince, Mathieu Roche
Chapter 1
Sophia Ananiadou
Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a... Sample PDF
Text Mining for Biomedicine
Chapter 2
Dimitrios Kokkinakis
The identification and mapping of terminology from large repositories of life science data onto concept hierarchies constitute an important initial... Sample PDF
Lexical Granularity for Automatic Indexing and Means to Achieve It: The Case of Swedish MeSH®
Chapter 3
M. Teresa Martín-Valdivia, Arturo Montejo-Ráez, M. C. Díaz-Galiano, José M. Perea Ortega, L. Alfonso Ureña-López
This chapter argues for the integration of clinical knowledge extracted from medical ontologies in order to improve a Multi-Label Text... Sample PDF
Expanding Terms with Medical Ontologies to Improve a Multi-Label Text Categorization System
Chapter 4
Piotr Pezik, Antonio Jimeno Yepes, Dietrich Rebholz-Schuhmann
The present chapter discusses the use of terminological resources for Information Retrieval in the biomedical domain. The authors first introduce a... Sample PDF
Using Biomedical Terminological Resources for Information Retrieval
Chapter 5
Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet
The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a... Sample PDF
Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval
Chapter 6
Vincent Claveau
This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This... Sample PDF
Translation of Biomedical Terms by Inferring Rewriting Rules
Chapter 7
Nils Reiter, Paul Buitelaar
This chapter is concerned with lexical enrichment of ontologies, that is how to enrich a given ontology with lexical information derived from a... Sample PDF
Lexical Enrichment of Biomedical Ontologies
Chapter 8
Torsten Schiemann, Ulf Leser, Jörg Hakenberg
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Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
Chapter 9
M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu
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Information Extraction of Protein Phosphorylation from Biomedical Literature
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CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
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Analyzing the Text of Clinical Literature for Question Answering
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Discourse Processing for Text Mining
Chapter 13
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A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
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Extracting Patient Case Profiles with Domain-Specific Semantic Categories
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Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
Chapter 16
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Chapter 17
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A Software Tool for Biomedical Information Extraction (And Beyond)
Chapter 18
Asanee Kawtrakul, Chaveevarn Pechsiri, Sachit Rajbhandari, Frederic Andres
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Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
Chapter 19
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Seekbio: Retrieval of Spatial Relations for System Biology
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