Discovering identity in intelligence data using weighted link based similarities: A case study of Thailand

Resolving ambiguous and unknown identities is crucial to intelligence analysis in which fraud and deceptive names are frequently used by criminals and terrorists to make their activities unnoticeable. Typical approaches rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of falsely-defined and unknown instances. This barrier can be overcome through link information presented in communication behaviors, financial interactions and social networks. Link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. Inspired by this observation, the paper presents new link-based algorithms that do not only concentrate on link structure as adopted by the existing methods, but also bring link properties into consideration. Intuitively, links are weighted in accordance to their uniqueness. Their performance are experimentally evaluated with datasets related to terrorism and similar tasks, espcially the data collection extracted from evidence ontology that is used for investigation of unrest in southern Thailand.