Applied categorical data analysis
Applied categorical data analysis
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Combining model-oriented and description-oriented approaches for probabilistic indexing
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic retrieval based on staged logistic regression
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic retrieval revisited
The Computer Journal - Special issue on information retrieval
Inferring probability of relevance using the method of logistic regression
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Modular stratification and magic sets for Datalog programs with negation
Journal of the ACM (JACM)
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Towards data abstraction in networked information retrieval systems
Information Processing and Management: an International Journal
Probabilistic Datalog: implementing logical information retrieval for advanced applications
Journal of the American Society for Information Science
HySpirit - A Probabilistic Inference Engine for Hypermedia Retrieval in Large Databases
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
A Probabilistic Framework for Vague Queries and Imprecise Information in Databases
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Evaluating different methods of estimating retrieval quality for resource selection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
Information Retrieval
Probabilistic, object-oriented logics for annotation-based retrieval in digital libraries
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
A descriptive approach to classification
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
Generating search term variants for text collections with historic spellings
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
On the modelling of ranking algorithms in probabilistic datalog
Proceedings of the 7th International Workshop on Ranking in Databases
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This paper introduces PIRE, a probabilistic IR engine. For both document indexing and retrieval, PIRE makes heavy use of probabilistic Datalog, a probabilistic extension of predicate Horn logics. Using such a logical framework together with probability theory allows for defining and using data types (e.g. text, names, numbers), different weighting schemes (e.g. normalised tf, tf.idf or BM25) and retrieval functions (e.g. uncertain inference, language models). Extending the system thus is reduced to adding new rules. Furthermore, this logical framework provide a powerful tool for including additional background knowledge into the retrieval process.