general introduction to measures

1. Measuring the Similarity and Relatedness of Concepts in the Medical Domain : IHI 2012 Tutorial Ted Pedersen, Ph.D. * Serguei Pakhomov, Ph.D. # Bridget McInnes, Ph.D. # Ying Liu, Ph.D. # University of Minnesota * Department of Computer Science, Duluth # College of Pharmacy, Twin Cities {tpederse,pakh0002,bthomson,liux0395}@umn.edu

3. The contents of this tutorial are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

5. How to measure using information from ontologies, definitions, and corpora

6. How to use the freely available software UMLS::Similarity and UMLS::Interface

7. How to conduct experiments using freely available reference standards

8. How to integrate these measures into clinical NLP applications

11. Introducing Measures of Semantic Similarity and Relatedness (without tears) Ted Pedersen Department of Computer Science University of Minnesota Duluth, MN 55812 [email_address] http://www.d.umn.edu/~tpederse

20. Least Common Subsumer (LCS)

27. Jiang & Conrath, 1997 (jcn)

28. Lin, 1998 (lin)

31. Assumes all paths have same “weight”

34. shortest is-a path(a,b)

37. path(food poisoning, strep throat) = 1/7

38. etc...

39. path(strep throat, tetanus) = .25

40. path (bacterial infections, yeast infections) = .25

42. The path measure says “yes, they are.”

44. Deeper concepts more specific

45. Paths between deeper concepts travel less semantic distance

47. depth (a) + depth (b)

48. depth(x) = shortest is-a path(root,x)

49. wup(strep throat, tetanus) = (2*2)/(4+3) = .57

50. wup (bacterial infections, yeast infections) = (2*1)/(2+3) = .4

52. Path says that strep throat and tetanus (.25) are equally similar as are bacterial infections and yeast infections (.25)

54. Term frequency +Inherited frequency

56. Low frequency concepts often much more specific than high frequency ones

57. Information Content term frequency (tf)

58. Information Content inherited frequency (if)

59. Information Content (IC = -log (f/N) final count (f = tf + if, N = 365,820)

61. IC (a) + IC (b)

62. Look familiar?

63. 2* depth (LCS (a,b) )

64. wup(a,b) = ------------------------------

65. depth(a) + depth (b)

70. Wu and Palmer say that strep throat and tetanus (.57) are more similar than are bacterial infection and yeast infection (.4)

71. Path says that strep throat and tetanus (.25) are equally similar as are bacterial infection and yeast infection (.25)

74. No is-a relations so can't be similarity

81. But, definitions are short, inconsistent

83. Adapted Lesk, Banerjee & Pedersen, 2003

84. Two separate ontologies...

85. Could join them together … ?

86. Each concept has definition

87. Find overlaps in definitions...

93. Alopecia : … a result of cancer treatment

98. Suppose cancer_treatment and chemotherapy don't occur in text with each other. But, suppose that “survival” occurs with each.

99. cancer_treatment and chemotherapy are second order co-occurrences via “survival”

101. Defined concept now represented by an averaged vector of co-occurrences

102. Measure relatedness of concepts via cosine between their respective vectors

105. Links to web interfaces

106. Software downloads

109. J. Caviedes and J. Cimino. Towards the development of a conceptual distance metric for the UMLS. Journal of Biomedical Informatics, 37(2):77-85, April 2004.

110. J. Jiang and D. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings on International Conference on Research in Computational Linguistics, pages 19-33, Taiwan, 1997.

111. C. Leacock and M. Chodorow. Combining local context and WordNet similarity for word sense identification. In C. Fellbaum, editor, WordNet: An electronic lexical database, pages 265-283. MIT Press, 1998.

113. D. Lin. An information-theoretic definition of similarity. In Proceedings of the International Conference on Machine Learning, Madison, August 1998.

114. H.A. Nguyen and H. Al-Mubaid. New ontology-based semantic similarity measure for the biomedical domain. In Proceedings of the IEEE International Conference on Granular Computing, pages 623-628, Atlanta, GA, May 2006.

115. S. Patwardhan and T. Pedersen. Using WordNet-based Context Vectors to Estimate the Semantic Relatedness of Concepts. In Proceedings of the EACL 2006 Workshop on Making Sense of Sense: Bringing Computational Linguistics and Psycholinguistics Together, pages 1-8, Trento, Italy, April 2006.

117. P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448-453, Montreal, August 1995.

118. H. Schütze. Automatic word sense discrimination. Computational Linguistics, 24(1):97-123, 1998.

119. J. Zhong, H. Zhu, J. Li, and Y. Yu. Conceptual graph matching for semantic search. Proceedings of the 10th International Conference on Conceptual Structures, pages 92-106, 2002

121. http://www.geneontology.org/GO.tools_by_type.semantic_similarity.shtml

124. http://search.cpan.org/dist/WebService-UMLSKS-Similarity/