Annual review of information science and technology, vol. 22
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Information retrieval from hypertext: update on the dynamic medical handbook project
HYPERTEXT '89 Proceedings of the second annual ACM conference on Hypertext
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
An architecture for probabilistic concept-based information retrieval
SIGIR '90 Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval
Inference networks for document retrieval
Inference networks for document retrieval
Relevance feedback and other query modification techniques
Information retrieval
Text-based intelligent systems
Information retrieval in hypertext systems: an approach using Bayesian networks
Electronic Publishing—Origination, Dissemination, and Design
Index expression belief networks for information disclosure
International Journal of Expert Systems
Applying Bayesian networks to information retrieval
Communications of the ACM
Automatic thesaurus construction using Bayesian networks
Information Processing and Management: an International Journal - Special issue: history of information science
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Information Retrieval
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Automatic Query Expansion for Japanese Text Retrieval
Automatic Query Expansion for Japanese Text Retrieval
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Relevance Feedback in the Bayesian Network Retrieval Model: An Approach Based on Term Instantiation
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Implementing relevance feedback in the Bayesian network retrieval model
Journal of the American Society for Information Science and Technology - Mathematical, logical, and formal methods in information retrieval
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Intelligent information systems
The effectiveness of automatically structured queries in digital libraries
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Bayesian networks and information retrieval: an introduction to the special issue
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
A Bayesian network approach to searching Web databases through keyword-based queries
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Concept-based ranking: a case study in the juridical domain
Information Processing and Management: an International Journal - Special issue: Bayesian networks and information retrieval
Journal of Biomedical Informatics
Coexistence of fuzzy and crisp concepts in document maps
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A hybrid information retrieval model using metadata and text
ICADL'05 Proceedings of the 8th international conference on Asian Digital Libraries: implementing strategies and sharing experiences
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
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Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. IJsing a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.