DocumentCode
2302610
Title
Crowdsourcing techniques to create a fuzzy subset of SNOMED CT for semantic tagging of medical documents
Author
Parry, David T. ; Tsai, Tsung-Chun
Author_Institution
Sch. of Comput. & Math. Sci., Auckland Univ. of Technol., Auckland, New Zealand
fYear
2010
fDate
18-23 July 2010
Firstpage
1
Lastpage
8
Abstract
Ontologies and other schemes are useful for allowing semantic tagging of documents for many applications on the semantic web. Representing uncertainty on the semantic web is becoming increasingly common, using fuzzy ontologies and other techniques. Very large ontologies and vocabularies have been created, however users may find it difficult to select the correct concept or term when there are large numbers of items that on face value appear to represent the same idea. Creating subsets of ontologies is a popular approach to solving this problem but this may not fit well with the need to deal with complex domains. However crowdsourcing techniques, which harness the power of large groups, may be more effective than document analysis or expert opinion. In Crowdsourcing, large numbers of people collaborate by performing relatively simple tasks usually using applications distributed via the World Wide Web. This approach is being tested in the medical domain using a very large clinical vocabulary, SNOMED CT.
Keywords
document handling; fuzzy set theory; medical computing; ontologies (artificial intelligence); semantic Web; SNOMED CT vocabulary; crowdsourcing techniques; fuzzy subset; medical documents; ontologies; semantic Web; semantic tagging; Encoding; Medical services; Ontologies; Semantic Web; Semantics; Ultrasonic imaging; Vocabulary; fuzzy logic; ontologies; semantic web;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on
Conference_Location
Barcelona
ISSN
1098-7584
Print_ISBN
978-1-4244-6919-2
Type
conf
DOI
10.1109/FUZZY.2010.5584055
Filename
5584055
Link To Document