Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Towards interactive query expansion
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Inference networks for document retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance feedback and other query modification techniques
Information retrieval
Information retrieval
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Selection of search terms based on user profile
SAC '92 Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990's
Relevance feedback and inference networks
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A user-centred evaluation of ranking algorithms for interactive query expansion
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Applying Bayesian networks to information retrieval
Communications of the ACM
Searching structured documents with the enhanced retrieval functionality of free WAIS-sf and SFgate
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of search term weighting: term relevance vs. inverse document frequency
SIGIR '81 Proceedings of the 4th annual international ACM SIGIR conference on Information storage and retrieval: theoretical issues in information retrieval
Capturing term dependencies using a language model based on sentence trees
Proceedings of the eleventh international conference on Information and knowledge management
Automatic extraction of informative blocks from webpages
Proceedings of the 2005 ACM symposium on Applied computing
Automatic Identification of Informative Sections of Web Pages
IEEE Transactions on Knowledge and Data Engineering
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Identifying content blocks from web documents
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Modeling information retrieval with probabilistic argumentation systems
IRSG'98 Proceedings of the 20th Annual BCS-IRSG conference on Information Retrieval Research
Proceedings of the 3rd international conference on Knowledge capture
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The Web continues to grow at a tremendous rate. Search engines find it increasingly difficult to provide useful results. To manage this explosively large number of Web documents, automatic clustering of documents and organising them into domain dependent directories became very popular. In most cases, these directories represent a hierarchical structure of categories and sub-categories for domains and sub-domains. To fill up these directories with instances, individual documents are automatically analysed and placed into them according to their relevance. Though individual documents in these collections may not be ranked efficiently, combinedly they provide an excellent knowledge source for facilitating ontology construction in that domain. In (mainly automatic) ontology construction steps, we need to find and use relevant knowledge for a particular subject or term. News documents provide excellent relevant and up-to-date knowledge source. In this paper, we focus our attention in building business ontologies. To do that we use news documents from business domains to get an up-to-date knowledge about a particular company. To extract this knowledge in the form of important “terms” related to the company, we apply a novel method to find “related terms” given the company name. We show by examples that our technique can be successfully used to find “related terms” in similar cases.