Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Multilingual keyword extraction for term suggestion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
COPLINK: managing law enforcement data and knowledge
Communications of the ACM
Automatic thesaurus generation for Chinese documents
Journal of the American Society for Information Science and Technology
Acquiring causal knowledge from text using the connective marker tame
ACM Transactions on Asian Language Information Processing (TALIP)
Mining learner profile utilizing association rule for web-based learning diagnosis
Expert Systems with Applications: An International Journal
Automated criminal link analysis based on domain knowledge: Research Articles
Journal of the American Society for Information Science and Technology
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Constructing Bayesian networks for criminal profiling from limited data
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
An intelligent decision-support model using FSOM and rule extraction for crime prevention
Expert Systems with Applications: An International Journal
The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis
Journal of the American Society for Information Science and Technology
Hi-index | 12.05 |
An efficient term mining method to build a general term network is presented. The resulting term network can be used for entity relation visualization and exploration, which is useful in many text-mining applications such as crime exploration and investigation from vast piles of crime news or official criminal records. In the proposed method, terms from each document in a text collection are first identified. They are subjected to an analysis for pairwise association weights. The weights are then accumulated over all the documents to obtain final similarity for each term pair. Based on the resulting term similarity, a general term network for the collection is built with terms as nodes and non-zero similarities as links. In application, a list of predefined terms having similar attributes was selected to extract the desired sub-network from the general term network for entity relation visualization. This text analysis scenario based on the collective terms of the similar type or from the same topic enables evidence-based relation exploration. Some practical instances of crime exploration and investigation are demonstrated. Our application examples show that term relations, be it causality, subordination, coupling, or others, can be effectively revealed by our method and easily verified by the underlying text collection. This work contributes by presenting an integrated term-relationship mining and exploration approach and demonstrating the feasibility of the term network to the increasingly important application of crime exploration and investigation.