Mining concept associations for knowledge discovery through concept chain queries
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Improving cross-document knowledge discovery using explicit semantic analysis
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Mining semantic relationships between concepts across documents incorporating wikipedia knowledge
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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This paper focuses on detecting links between two concepts across text documents (e.g. two persons). We interpret such a query as finding the most meaningful evidence trail across documents that connect these two concepts. Here we propose a fast and efficient algorithm to perform this task. It is based on the idea of hypothesis generation originated by Swanson called "complementary structures in disjoint literatures" (CSD). We adapted the technique by (i) developing an alternate method of generating semantic profiles and (ii) extending the technique to generate concept chains. Counterterrorism corpus is used to evaluate the performance of this approach and demonstrates the effectiveness of our algorithm.