A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Probabilistic model for contextual retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
A review of ontology based query expansion
Information Processing and Management: an International Journal
Query expansion with terms selected using lexical cohesion analysis of documents
Information Processing and Management: an International Journal
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A Boolean Model in Information Retrieval for Search Engines
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Pairwise optimized Rocchio algorithm for text categorization
Pattern Recognition Letters
Using Tag-Neighbors for Query Expansion in Medical Information Retrieval
ICISA '11 Proceedings of the 2011 International Conference on Information Science and Applications
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Query expansion is a technique of information retrieval system which considered to the context of the user's queries in order to improve the retrieval effectiveness. There are several method expansion methods have been investigated. An ontology-based approach is one of the expansion approaches which used as knowledge-based for searching. This paper proposed an ontology-based query expansion using a concept of the combination of an IR technique and association rule mining to examine and evaluate of an agricultural expert retrieval systems. An association rule mining is applied in the inference engine in order to optimize the user's queries. A set of inference rules are also created to support the expertise retrieval task. The result set is depends on the new query which expands from user's queries as keywords using an agricultural ontology structures. The experts who have expertise in topics or keywords, type of plants and problem solving are the elements of the ontology and will be used to search the relevant publications. The experiments were conducted using publications from collections of the Thai National AGRIS center. The results show that the improvement of this expansion method yields better performance than using basic query expansion search with the F-measure equal to 98.87%.