Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Applying Bayesian networks to information retrieval
Communications of the ACM
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Modern Information Retrieval
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
A Vector Space Retrieval Method with Causal Relationship Computation Functions for Event Data
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Proceedings of the 2007 conference on Information Modelling and Knowledge Bases XVIII
A Vector Space Model on Hierarchical Structures with Dynamic Mapping Operator Creation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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In this paper, we present a superordinate and subordinate relationship computation method. We have proposed a vector space search method for computing causal relationships. We extend this method to compute directionality relationships of superordinate and subordinate concepts. By using general knowledge expression with a tree structure, we generate vector spaces for computing those directionality relationships. This method dealing with the combination of those directionality relationships makes it possible to realize a superordinate and subordinate relationship computation, according to various objectives related to directionality relationships. We have implemented a superordinate and subordinate relationship computing system for directionality relationships among technical terms in the aerospace engineering field. We clarify the effectiveness and feasibility of our system by several experiments.