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Proceedings ArticleDOI

Towards a probabilistic modal logic for semantic-based information retrieval

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TLDR
This paper demonstrates, using some particular probabilistic models which are strongly related to modal logic, that such an integration is feasible and natural and shows that this model verifies most of the conditions for an absolute probability function.
Abstract
Semantic-based approaches to Information Retrieval make a query evaluation similar to an inference process based on semantic relations. Semantic-based approaches find out hidden semantic relationships between a document and a query, but quantitative estimation of the correspondence between them is often empiric. On the other hand, probabilistic approaches usually consider only statistical relationships between terms. It is expected that improvement may be brought by integrating these two approaches. This paper demonstrates, using some particular probabilistic models which are strongly related to modal logic, that such an integration is feasible and natural. A new model is developed on the basis of an extended modal logic. It has the advantages of : (1) augmenting a semantic-based approach with a probabilistic measurement, and (2) augmenting a probabilistic approach with finer semantic relations than just statistical ones. It is shown that this model verifies most of the conditions for an absolute probability function.

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Citations
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Journal ArticleDOI

Current approaches to handling imperfect information in data and knowledge bases

TL;DR: A survey of methods for representing and reasoning with imperfect information can be found in this paper, where a classification of different types of imperfections and sources of such imperfections are discussed.
Journal ArticleDOI

“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval

TL;DR: The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented as mentioned in this paper, and various models proposed in the development of IR are described, classified, and compared using a common formalism.
Journal ArticleDOI

On modeling information retrieval with probabilistic inference

TL;DR: This article examines and extends the logical models of information retrieval in the context of probability theory, and the fundamental notions of term weights and relevance are given probabilistic interpretations.
Book

A Generative Theory of Relevance

TL;DR: This book makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism.
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Automatic indexing and abstracting of document texts

TL;DR: This paper aims to provide a history of indexing and Abstracting techniques used in the field since the 1970s, and some of the techniques used today are still in use.
References
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Book

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book

Introduction to Modern Information Retrieval

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Journal ArticleDOI

Probabilistic logic

TL;DR: In this paper, a semantical generalization of logic in which the truth values of sentences are probabilistic values (between 0 and 1) is presented, which applies to any logical system for which the consistency of a finite set of sentences can be established.
Book ChapterDOI

Probabilities of Conditionals and Conditional Probabilities

TL;DR: The truthful speaker wants not to assert falsehoods, wherefore he is willing to assert only what he takes to be very probably true as discussed by the authors, where assertability goes by subjective probability.
Journal ArticleDOI

Inference Networks for Document Retrieval

TL;DR: The use of inference networks to support document retrieval and a network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.