Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval

Automatic Alignment of Medical Terminologies with General Dictionaries for an Efficient Information Retrieval

Laura Diosan (Institut National des Sciences Appliquées, France & Babes-Bolyai University, Romania), Alexandrina Rogozan (Institut National des Sciences Appliquées, France) and Jean-Pierre Pécuchet (Institut National des Sciences Appliquées, France)
DOI: 10.4018/978-1-60566-274-9.ch005
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Abstract

The automatic alignment between a specialized terminology used by librarians in order to index concepts and a general vocabulary employed by a neophyte user in order to retrieve medical information will certainly improve the performances of the search process, this being one of the purposes of the ANR VODEL project. The authors propose an original automatic alignment of definitions taken from different dictionaries that could be associated to the same concept although they may have different labels. The definitions are represented at different levels (lexical, semantic and syntactic), by using an original and shorter representation, which concatenates more similarities measures between definitions, instead of the classical one (as a vector of word occurrence, whose length equals the number of different words from all the dictionaries). The automatic alignment task is considered as a classification problem and three Machine Learning algorithms are utilised in order to solve it: a k Nearest Neighbour algorithm, an Evolutionary Algorithm and a Support Vector Machine algorithm. Numerical results indicate that the syntactic level of nouns seems to be the most important, determining the best performances of the SVM classifier.
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Introduction

The need for terminology integration has been widely recognized in the medical world, leading to efforts to define standardized and complete terminologies. It is, however, also acknowledged in the literature that the creation of a single universal terminology for the medical domain is neither possible, nor beneficial because different tasks and viewpoints require different, often incompatible conceptual choices (Gangemi, Pisanelli & Steve, 1999). As a result, a number of communities of practice, differing in that they only commit to one of the proposed standards, have evolved. This situation demands for a weak notion of integration, also referred to as alignment, in order to be able to exchange information between different communities. In fact, the common points of two different terminologies have to be found in order to facilitate interoperability between computer systems that are based on these two terminologies. In this way, the gaps between general language and specialist language could be bridged.

Information retrieval systems are based on specific terminologies describing a particular domain. Only the domain experts share the knowledge encoded in those specific terminologies, but they are completely unknown to the neophytes. In fact, neophyte users formulate their queries by using naïve or general language. An information retrieval system has to be able to take into account the semantic relationships between concepts belonging to both general and specialised language, in order to answer the requests of naive users. The Information retrieval system has to map the user’s query (expressed in general terms) into the specialised dictionary. The search task must be done by using both general and specialised terms and, maybe, their synonyms (or other semantic related concepts - hypernyms, hyponyms, and antonyms) from both terminologies.

The problem is how to automatically discover the connections between a specialised terminology and a general vocabulary shared by an average user for information retrieval on Internet (see Figure 1). This problem could be summarised as the automatic alignment of specialized terminologies and electronic dictionaries in order to take full advantage of their respective strengths.

Figure 1.

VODEL and Information Retrieval. The elements designed by dot lines refer to the classic techniques of Information Retrieval domain, while those designed by solid lines relate to our models, developed during VODEL project.

The main objective of our work is to enrich the information retrieval system with a set of links, which allow for a better exploitation of specialised terminologies and electronic dictionaries. Several algorithms, well known in the community of Machine Learning, are utilised in order to realise an automatic alignment process. A non-expert user would therefore access documents indexed through the concepts of a professional dictionary if these notions are correlated by semantic links to a general dictionary. An important idea is to look for the terms of the non-expert query by using a specialized terminology and vice versa.

Therefore, one of the most important tasks is to achieve an automatic alignment of specialized vs. general terms that correspond to the same (or very similar) concepts. The main aim is to find a mapping between different formulations, but of the same meaning, in our case the sense of a concept being represented by its definition(s) from one or more dictionaries (i.e. to associate definitions from different dictionaries that correspond to the same/similar concept(s). This alignment of definitions, which is one of the goals of the French VODEL projecta as well (Lortal et al. 2007, Dioşan et al. 2007, Dioşan et al. 2008a, Dioşan et al. 2008b), certainly needs to improve the fusion between the specialized terminology and the general vocabulary employed by a neophyte user in order to retrieve documents from Internet.

The main aim is to design a Machine Learning’s algorithm that will decide whether two given definitions, expressed as text sentence(s), refer to the same concept or not. In order to perform this alignment, each definition (corresponding to a given concept and taken from a dictionary), is first turned into a bag of words (by using some NLP techniques), each word being than enriched with syntactic and semantic information.

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Table of Contents
Preface
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
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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®
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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
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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
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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
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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
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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
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Chapter 8
Torsten Schiemann, Ulf Leser, Jörg Hakenberg
Ambiguity is a common phenomenon in text, especially in the biomedical domain. For instance, it is frequently the case that a gene, a protein... Sample PDF
Word Sense Disambiguation in Biomedical Applications: A Machine Learning Approach
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Chapter 9
M. Narayanaswamy, K. E. Ravikumar, Z. Z. Hu, K. Vijay-Shanker, C. H. Wu
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Chapter 10
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This chapter argues that in order to extract significant knowledge from masses of technical texts, it is necessary to provide the field specialists... Sample PDF
CorTag: A Language for a Contextual Tagging of the Words Within Their Sentence
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Chapter 11
Yun Niu, Graeme Hirst
The task of question answering (QA) is to find an accurate and precise answer to a natural language question in some predefined text. Most existing... Sample PDF
Analyzing the Text of Clinical Literature for Question Answering
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Chapter 12
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This chapter presents the challenge of integrating knowledge at higher levels of discourse than the sentence, to avoid “missing the forest for the... Sample PDF
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Chapter 13
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In this chapter, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of information (medical... Sample PDF
A Neural Network Approach Implementing Non-Linear Relevance Feedback to Improve the Performance of Medical Information Retrieval Systems
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Chapter 14
Yitao Zhang, Jon Patrick
The fast growing content of online articles of clinical case studies provides a useful source for extracting domain-specific knowledge for improving... Sample PDF
Extracting Patient Case Profiles with Domain-Specific Semantic Categories
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Chapter 15
Laura I. Furlong, Ferran Sanz
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Identification of Sequence Variants of Genes from Biomedical Literature: The OSIRIS Approach
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Chapter 16
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Molecular Biology research projects produced vast amounts of data, part of which has been preserved in a variety of public databases. However, a... Sample PDF
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Chapter 17
Burr Settles
ABNER (A Biomedical Named Entity Recognizer) is an open-source software tool for text mining in the molecular biology literature. It processes... Sample PDF
A Software Tool for Biomedical Information Extraction (And Beyond)
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Chapter 18
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Valuable knowledge has been distributed in heterogeneous formats on many different Web sites and other sources over the Internet. However, finding... Sample PDF
Problems-Solving Map Extraction with Collective Intelligence Analysis and Language Engineering
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Chapter 19
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Chapter 20
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About the Contributors