Knowledge%20Representation%20and%20Indexing%20Using%20the%20Unified%20Medical%20Language%20System

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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 ProcessingRetrieval - Ontologies
  Semantic Frameworks Information Visualization -
  Keynets
• Results of Usability Studies

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Introduction

• 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.

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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
  retrievedimages, 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
• gt 130 semantic categories
• Semantic Relationships
• is a , part of, disrupts
• Semantic Concepts (Vocabulary)
• gt 1,000,000 concepts map to categories

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Natural Language Processing using an Ontology
semantic
syntactic
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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
Fc-receptors on NK cells

• 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.

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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 Sections
• Demographics.
• 9 semantic differential focused questions.
• 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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Survey Results
Question
n11

• 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 users 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/