Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Intelligent signal analysis and recognition using a self-organizing database
IEA/AIE '88 Proceedings of the 1st international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Retrieval and organizational strategies in conceptual memory: a computer model
Retrieval and organizational strategies in conceptual memory: a computer model
A self-organizing retrieval system for graphs (organic, machine, chemistry, learning, partial-ordering)
Knowledge acquisition via incremental conceptual clustering
Knowledge acquisition via incremental conceptual clustering
Chained forests for fast subsumption matching
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
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In this paper we show how to maintain a partially-ordered hierarchy of patterns by subpattern-of for efficient associative retrieval. The techniques described here are most applicable for databases of complex data structures such as graphs or matrices as opposed to simple data structures such as relations, sets, lists or frames that are seen in most information retrieval systems. Any representation scheme for the patterns in the database may be used as long as the user provides the system with the function that compares two patterns in the representation scheme and determines if one pattern is a subpattern of the other, if the patterns are identical or the patterns are incomparable. The user also provides utilities for reading and writing the patterns.