Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Logic programming and databases
Logic programming and databases
Models for retrieval with probabilistic indexing
Information Processing and Management: an International Journal - Modeling data, information and knowledge
Where should the person stop and the information search interface start?
Information Processing and Management: an International Journal
A probabilistic learning approach for document indexing
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
The “HyTime ”: hypermedia/time-based document structuring language
Communications of the ACM
Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
A model of information retrieval based on a terminological logic
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
Some inconsistencies and misidentified modeling assumptions in probabilistic information retrieval
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
“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Probabilistic Datalog: implementing logical information retrieval for advanced applications
Journal of the American Society for Information Science
Information Retrieval Methods for Multimedia Objects
State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5-10 December 1999]
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Building bridges within learning communities through ontologies and "thematic objects"
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
Using the shape recovery method to evaluate indexing techniques
Journal of the American Society for Information Science and Technology
Term Impacts as Normalized Term Frequencies for BM25 Similarity Scoring
SPIRE '08 Proceedings of the 15th International Symposium on String Processing and Information Retrieval
International Journal of Approximate Reasoning
Improving Arabic information retrieval system using N-gram method
WSEAS Transactions on Computers
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Retrieval models form the theoretical basis for computing the answer to a query. They differ not only in the syntax and expressiveness of the query language, but also in the representation of the documents. Following Rijsbergen's approach of regarding IR as uncertain inference, we can distinguish models according to the expressiveness of the underlying logic and the way uncertainty is handled. Classical retrieval models are based on propositional logic. In the vector space model, documents and queries are represented as vectors in a vector space spanned by the index terms, and uncertainty is modelled by considering geometric similarity. Probabilistic models make assumptions about the distribution of terms in relevant and nonrelevant documents in order to estimate the probability of relevance of a document for a query. Language models compute the probability that the query is generated from a document. All these models can be interpreted within a framework that is based on a probabilistic concept space. For IR applications dealing not only with texts, but also with multimedia or factual data, propositional logic is not sufficient. Therefore, advanced IR models use restricted forms of predicate logic as basis. Terminological/description logics are rooted in semantic networks and terminological languages like e.g. KL-ONE. Datalog uses function-free horn clauses. Probabilistic versions of both approaches are able to cope with the intrinsic uncertainty of IR.