Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Text mining: generating hypotheses from MEDLINE
Journal of the American Society for Information Science and Technology
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery across Documents through Concept Chain Queries
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Infoxtract: A customizable intermediate level information extraction engine
Natural Language Engineering
Improving Text Classification by Using Encyclopedia Knowledge
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
MeSoOnTV: a media and social-driven ontology-based TV knowledge management system
Proceedings of the 24th ACM Conference on Hypertext and Social Media
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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Cross-document knowledge discovery is dedicated to exploring meaningful (but maybe unapparent) information from a large volume of textual data. The sparsity and high dimensionality of text data present great challenges for representing the semantics of natural language. Our previously introduced Concept Chain Queries (CCQ) was specifically designed to discover semantic relationships between two concepts across documents where relationships found reveal semantic paths linking two concepts across multiple text units. However, answering such queries only employed the Bag of Words (BOW) representation in our previous solution, and therefore terms not appearing in the text literally are not taken into consideration. Explicit Semantic Analysis (ESA) is a novel method proposed to represent the meaning of texts in a higher dimensional space of concepts which are derived from large-scale human built repositories such as Wikipedia. In this paper, we propose to integrate the ESA technique into our query processing, which is capable of using vast knowledge from Wikipedia to complement existing information from text corpus and alleviate the limitations resulted from the BOW representation. The experiments demonstrate the search quality has been greatly improved when incorporating ESA into answering CCQ, compared with using a BOW-based approach.