Issue No. 04 - April (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.255
Qiang Shen , Aberystwyth University, Ceredigion
Tossapon Boongoen , Royal Thai Air Force Academy, Bangkok
Alias detection has been the significant subject being extensively studied for several domain applications, especially intelligence data analysis. Many preliminary methods rely on text-based measures, which are ineffective with false descriptions of terrorists' name, date-of-birth, and address. This barrier may be overcome through link information presented in relationships among objects of interests. Several numerical link-based similarity techniques have proven effective for identifying similar objects in the Internet and publication domains. However, as a result of exceptional cases with unduly high measure, these methods usually generate inaccurate similarity descriptions. Yet, they are either computationally inefficient or ineffective for alias detection with a single-property based model. This paper presents a novel orders-of-magnitude based similarity measure that integrates multiple link properties to refine the estimation process and derive semantic-rich similarity descriptions. The approach is based on order-of-magnitude reasoning with which the theory of fuzzy set is blended to provide quantitative semantics of descriptors and their unambiguous mathematical manipulation. With such explanatory formalism, analysts can validate the generated results and partly resolve the problem of false positives. It also allows coherent interpretation and communication within a decision-making group, using this computing-with-word capability. Its performance is evaluated over a terrorism-related data set, with further generalization over publication and email data collections.
Orders-of-magnitude reasoning, fuzzy set, link analysis, similarity measure, alias detection, intelligence data.
Q. Shen and T. Boongoen, "Fuzzy Orders-of-Magnitude-Based Link Analysis for Qualitative Alias Detection," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 649-664, 2010.