Bidirectional associative memories
IEEE Transactions on Systems, Man and Cybernetics
International Journal of Man-Machine Studies
Journal of the American Society for Information Science
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Concept Based Adaptive IR Model Using FCA-BAM Combination for Concept Representation and Encoding
Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval
Representation of Concept Lattices by Bidirectional Associative Memories
Neural Computation
Representation of Complex Concepts for Semantic Routed Network
ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
Heuristic-Based Approach for Constructing Hierarchical Knowledge Structures
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Short Communication: Concept lattice reduction using fuzzy K-Means clustering
Expert Systems with Applications: An International Journal
Constructing tree-based knowledge structures from text corpus
Applied Intelligence
Conceptual-driven classification for coding advise in health insurance reimbursement
Artificial Intelligence in Medicine
A lattice-based approach for mathematical search using Formal Concept Analysis
Expert Systems with Applications: An International Journal
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Query terms abstraction layers
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
Cross-lingual information retrieval by feature vectors
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
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This paper reports our experimental investigation into the use of more realistic concepts as opposed to simple keywords for document retrieval, and reinforcement learning for improving document representations to help the retrieval of useful documents for relevant queries. The framework used for achieving this was based on the theory of Formal Concept Analysis (FCA) and Lattice Theory. Features or concepts of each document (and query), formulated according to FCA, are represented in a separate concept lattice and are weighted separately with respect to the individual documents they present. The document retrieval process is viewed as a continuous conversation between queries and documents, during which documents are allowed to learn a set of significant concepts to help their retrieval. The learning strategy used was based on relevance feedback information that makes the similarity of relevant documents stronger and non-relevant documents weaker. Test results obtained on the Cranfield collection show a significant increase in average precisions as the system learns from experience.