A knowledge-based model using ontologies for personalized web information gathering
Web Intelligence and Agent Systems
Discovering, ranking and annotating cross-document relationships between concepts
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
HCAMiner: mining concept associations for knowledge discovery through concept chain queries
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations
Discovering temporal bisociations for linking concepts over time
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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
| Hi-index | 0.00 |
In this paper, we present Concept Chain Queries (CCQ), a special case of text mining in document collections focusing on detecting links between two topics across text documents. We interpret such a query as finding the most meaningful evidence trails across documents that connect these two topics. We propose to use link-analysis techniques over the extracted features provided by Information Extraction Engine for finding new knowledge. A graphical text representation and mining model is proposed which combines information retrieval, association mining and link analysis techniques. We present experiments on different datasets that demonstrate the effectiveness of our algorithm. Specifically, the algorithm generates ranked concept chains and evidence trails where the key terms representing significant relationships between topics are ranked high1.