Combating identity fraud is crucial and urgent as false identity has become the common denominator of all serious crime, including mafia trafficking and terrorism. Typical approaches to detecting the use of false identity rely on the similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of deceptive and erroneous description. This barrier can be overcome through link information presented in communication behaviors, financial interactions and social networks. Quantitative link-based similarity measures have proven effective for identifying similar problems in the Internet and publication domains. However, these numerical methods only concentrate on link structures, and fail to achieve accurate and coherent interpretation of the information. Inspired by this observation, this paper presents a novel qualitative similarity measure that makes use of multiple link properties to refine the underlying similarity estimation process and consequently derive semantic-rich similarity descriptors. The approach is based on order-of-magnitude reasoning. Its applicability and performance are experimentally evaluated over a terrorism-related dataset, and further generalized with publication data.