A fuzzy order-of-magnitude approach to qualitative link analysis

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dc.contributor.author Shen, Qiang
dc.contributor.author Boongoen, Tossapon
dc.contributor.author Price, Chris
dc.date.accessioned 2011-09-27T09:03:02Z
dc.date.available 2011-09-27T09:03:02Z
dc.date.issued 2011-09-27
dc.identifier.citation Shen , Q , Boongoen , T & Price , C 2011 , ' A fuzzy order-of-magnitude approach to qualitative link analysis ' Proceedings of the 25th International Workshop on Qualitative Reasoning , pp. 147-158 . en
dc.identifier.other PURE: 179274
dc.identifier.other dspace: 2160/7605
dc.identifier.uri http://hdl.handle.net/2160/7605
dc.description Q. Shen, T. Boongoen and C. Price. A fuzzy order-of-magnitude approach to qualitative link analysis. Proceedings of the 25th International Workshop on Qualitative Reasoning, pp. 147-158, 2011. en
dc.description.abstract Numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, for cases involving unduly high similarity measures, these methods usually generate inaccurate results. Also, they are often restricted to measuring over single properties only. This paper presents an order-of-magnitude based similarity mechanism that integrates multiple link properties to derive semantic-rich similarity descriptions. The approach extends conventional order-of-magnitude reasoning with the theory of fuzzy sets. The inherent ability of this work in computing-with-words also allows coherent interpretation and communication within a decision-making group. The proposed approach is applied to supporting the analysis of intelligence data. When evaluated over a difficult terrorism-related dataset, experimental results show that the approach helps to partly resolve the problem of false positives. en
dc.format.extent 12 en
dc.language.iso eng
dc.relation.ispartof Proceedings of the 25th International Workshop on Qualitative Reasoning en
dc.title A fuzzy order-of-magnitude approach to qualitative link analysis en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en


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