The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Adapting a spatial access structure for document representations in vector space
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Finding topic words for hierarchical summarization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
MIRE: A Multidimensional Information Retrieval Engine for Structured Data and Text
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
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This paper describes a new method for incorporating a hierarchical category dimension into an Information Retrieval framework. The approach is to use the synonym sets and the hyponym ("is-a") relations defined within Wordnet in order to derive a conceptual hierarchical category dimension. The hierarchical nature of a category dimension not only provides an overview of a set of documents but also facilitates the effectiveness and the efficiency of searching documents. An evaluation is performed on two different types of models and the multidimensional approach shows a significant reduction in the number of page accesses over a large document collection.