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Modeling Information Retrieval by Formal Logic: A Survey

Authors:

Karam Abdulahhad,

Catherine Berrut,

Jean-Pierre Chevallet,

Gabriella PasiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 52, Issue 1

Article No.: 15, Pages 1 - 37

https://doi.org/10.1145/3291043

Published: 21 February 2019 Publication History

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Abstract

Several mathematical frameworks have been used to model the information retrieval (IR) process, among them, formal logics. Logic-based IR models upgrade the IR process from document-query comparison to an inference process, in which both documents and queries are expressed as sentences of the selected formal logic. The underlying formal logic also permits one to represent and integrate knowledge in the IR process. One of the main obstacles that has prevented the adoption and large-scale diffusion of logic-based IR systems is their complexity. However, several logic-based IR models have been recently proposed that are applicable to large-scale data collections. In this survey, we present an overview of the most prominent logical IR models that have been proposed in the literature. The considered logical models are categorized under different axes, which include the considered logics and the way in which uncertainty has been modeled, for example, degrees of belief or degrees of truth. Accordingly, the main contribution of the article is to categorize the state-of-the-art logical models on a fine-grained basis, and for the considered models the related implementation aspects are described. Consequently, the proposed survey is finalized to better understand and compare the different logical IR models. Last, but not least, this article aims at reconsidering the potentials of logical approaches to IR by outlining the advances of logic-based approaches in close research areas.

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Index Terms

Modeling Information Retrieval by Formal Logic: A Survey
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Logic and Uncertainty in Information Retrieval
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The use of logic in Information Retrieval (IR) enables one to formulate models that are more general than other well known IR models. Indeed, some logical models are able to represent, within a uniform framework, various features of IR systems, such as ...
Relevance terminological logics Towards logic-based information retrieval
Sentence-based relevance flow analysis for high accuracy retrieval

Traditional ranking models for information retrieval lack the ability to make a clear distinction between relevant and nonrelevant documents at top ranks if both have similar bag-of-words representations with regard to a user query. We aim to go beyond ...

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Reviewer: Jolanta MizeraPietraszko

As the title indicates, formal logic is used for modeling information retrieval (IR). Readers can expect a literature review (of IR models), supported with graphs, mathematical formulas, and examples that lead to some interesting conclusions. For those working in the area of IR, it may be a controversial assumption that "logic-based IR models upgrade the IR process from document-query comparison to an inference process," as the very first IR classical models relied just on logic-for instance, matching query to documents, query formulation, query-document matrix, definition of relevance, and so on. Semantic queries express inferences studied by the IR community without formal logic theory; therefore, its omission is a huge gap of this work. Karen Spärck Jones, a pioneer of term frequency inverse document frequency (tf-idf), is not even mentioned; Gerard Salton, "the father of IR," has only one reference. Another huge gap is the survey structure logic. Instead of developing the concepts of modeling IR, showing the progress of formal logic applications in the area over the years, the article gives mathematical formulas proposed by some researchers and chosen somehow at random. I would appreciate some examples, as well as more information about which models became popular and which went unnoticed. Readers will first learn some IR terminology in a very generic sense. The potential of this work is presented next, defined as logical models, to help understand relevance, or represent non-logical models or include domain knowledge. Logic-based models are listed with the respective authors: document and query representation, implication-based categorization, uncertainty versus multiple truth values, ranking to uncertainty, and propositional models. The limited references for the models make it difficult to see which of them have really contributed to IR over the years, and there are no graphs, diagrams, or pseudocode representations. The article may be recommended to mathematicians who like formal logic from a theoretical viewpoint only, without any vision of implementation to IR.

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Published In

ACM Computing Surveys Volume 52, Issue 1

January 2020

758 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3309872

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 February 2019

Accepted: 01 November 2018

Revised: 01 August 2018

Received: 01 September 2017

Published in CSUR Volume 52, Issue 1

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Logic and Uncertainty in Information Retrieval

Relevance terminological logics Towards logic-based information retrieval

Sentence-based relevance flow analysis for high accuracy retrieval

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