knowledge representation and indexing using the unified medical language system
DESCRIPTION
Knowledge Representation and Indexing Using the Unified Medical Language System. Kenneth Baclawski * Joseph “Jay” Cigna * Mieczyslaw M. Kokar * Peter Major † Bipin Indurkhya ‡ * Northeastern University † Jarg Corporation ‡ Tokyo University of Agriculture and Technology. Purpose. - PowerPoint PPT PresentationTRANSCRIPT
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Knowledge Representation and Indexing Using the Unified Medical Language System
Kenneth Baclawski*
Joseph “Jay” Cigna*
Mieczyslaw M. Kokar*
Peter Major † Bipin Indurkhya ‡ * Northeastern University† Jarg Corporation‡ Tokyo University of Agriculture and Technology
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Purpose
Biomedical Information Searches
Ontologies & the UMLS
Knowledge Representation
Input - Natural Language Processing
Retrieval - Ontologies & Semantic Frameworks
Information Visualization - Keynets
Results of Usability Studies
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Problem: Low Quality Search Searching using keyword matching often has high
volume and low precision. Discrete keywords do not represent knowledge. Result of a search are not be arranged in a
semantically relevant way. Examining search results is often tedious. Search results include only textual documents.
Introduction
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Introduction
Solution: Ontologies
Model for knowledge extraction/management using a domain-specific vocabulary and theories expressing the meaning of the vocabulary within the community using the vocabulary.
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Advantages of Ontologies
Allows semantically correct retrieval based on domain specific criteria.
No limit to the depth of knowledge that can be represented, managed and retrieved.
Multiplicity of information objects retrieved:images, video, sound, etc. as well as text.
Results of a search are grouped by how documents are relevant to the whole query.
The ontology can be updated as new terminology and relationships are introduced.
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UMLS
US National Library of Medicine since 1986 Overcomes retrieval problems
– Differences in terminology– Distributed database sources
Develops machine-readable “knowledge sources”
Allow researchers and health professionals to retrieve and integrate electronically available biomedical information.
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Free Iteratively refined and expanded from
feedback Maps many different names for the same
concept Grateful Med and PubMed are applications
of the UMLS
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Semantic Categories – > 130 semantic categories
Semantic Relationships– “ is a “, “ part of”, “disrupts”
Semantic Concepts (Vocabulary)– > 1,000,000 concepts map to categories
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Natural Language Processing using an Ontology
syntactic
semantic
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Keynets
A technique for representing information in a visual manner that can be manipulated into meaningful associations for refinement of the knowledge extracted.
Exploits human – computer interactivity inherent in knowledge processing.
Based on Information Visualization Concept (Schneiderman, 1998)
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Knowledge Representation using the UMLS and Keynets
Acyclic directed graph. Provides a consistent categorization for all concepts. Shows the important relationships between the
concepts. NLP using the UMLS produces Keynets, a new
search strategy for knowledge processing of biomedical information.
“Fc-receptors on NK cells”
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Usability Study
The purpose was to explore the reactions of users to different representations of biomedical information– Keywords: Fc-receptors, cells, NK cells
– Keynet: Sample: n = 11; MD, PhD, Biomedical engineers,
Pharmacologists - individuals who would typically be required to search for biomedical information
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UMLS Keywords and Keynets
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Survey Format
Three SectionsI. Demographics.
II. 9 semantic differential focused questions.
III. Open ended questions to assess subjects overall impressions of using keynets and information visualization for knowledge representation,
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Semantic Differential Question
scale 1-9 , 0 = N/Ae.g. confusing/clear 1 most like first word or “confusing” 9 most like the last word “clear”
confusing clear 1 2 3 4 5 6 7 8 9
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Semantic Differential Question
1a How would you rate the Keynet version in its ability to represent the biomedical text given?
confusing clear 1 2 3 4 5 6 7 8 9
1b How would you rate the Keyword version in its ability to represent the biomedical text given?
confusing clear 1 2 3 4 5 6 7 8 9
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UMLS Keywords and Keynets
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1 3 5 7 9
confusing/clear
terrible/w onderful
frustrating/satisfying
dull/stimulating
diff icult/easy
w eak/pow erful
rigid/f lexible
inconsistent/consistent
ambiguous/precise
Keynet
Keyword
Que
stio
nSurvey Results
n=11
Score
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Results of Usability Study
Level of Understanding of Keynets– Remarkably high given short time to complete study,
population diversity, different examples used.– Example – missing relationship detected (7 of 11)
Limit Complexity– Representations should be concise
drilling down only at the user’s request Keywords versus Keynet
– No statistical difference, Keynets are as least as useful as Keywords in representation of biomedical information retrieval.
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Summary
A new strategy is suggested for searching and retrieving biomedical information using NLP, the UMLS and Keynet displays of the retrieved results.
Issues of semantic versus syntactic representations for biomedical information retrieval.
Issues relating information visualization for the processing of biomedical information retrieval.
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Conclusion
•Consider the computer-human interactivity issues
•A picture is worth a thousand keywords!!
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Acknowledgement This project was performed as part of the “Biomedical Science Information Retrieval
and Management” project supported by grant # 1 R43 LM06665-01 from the National Institute of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
A portion of this study was conducted in part at Jarg Corporation, 332b Second Ave., Waltham, MA 02451-1104.
Travel expenses for this presentation were provided by a grant from the Dept. of Energy.
www.jarg.com
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Addendum
Technical information related to Keynetshttp://www.ccs.neu.edu/home/kenb/key/