A Belief Model of Query Difficulty That Uses Subjective Logic

Lioma, Christina; Blanco, Roi; Palau, Raquel Mochales; Moens, Marie‐Francine

doi:10.1007/978-3-642-04417-5_9

Cited by 7 publications

(6 citation statements)

References 22 publications

(32 reference statements)

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“…In this example, the positive and negative evidence is drawn from lexical-semantic information in a simple way. Evidence can also be drawn from many other types of information, for instance syntactic, statistical, pragmatic or other, as described in [34]. The choice of evidence used here is illustrative.…”

Section: Mapping Opinions To Evidencementioning

confidence: 99%

“…Subjective Logic is not the only formalism to model degrees of uncertainty. Several other mathematical models have been proposed to this end, starting from the Bayesian model of subjective probabilities (see [13] for a historical survey), as well as generalisations of the Bayesian model (see [52] for a survey), the best-known of which is Dempster-Shafer's Belief Theory [7,46] (see [21] for an elaborated discussion on the differences between Dempster-Shafer and Subjective Logic, or [34] for a more succint version).…”

Section: Formal Logic In Irmentioning

confidence: 99%

“…It has been applied to build a complete and holistic IR framework [25], but also to integrate Web evidence into IR [55], to integrate evidence of query difficulty into Web IR in the form of semantic scope [43], as well as to relate dependent indices of IR systems [47]. Finally, to our knowledge, the only prior use of Subjective Logic to IR has been the recent work of [34], who used it to model query difficulty as a subjective belief, formulated on the basis of various types of linguistic evidence.…”

Section: Formal Logic In Irmentioning

confidence: 99%

See 2 more Smart Citations

A subjective logic formalisation of the principle of polyrepresentation for information needs

Lioma

Larsen

Schuetze

et al. 2010

Proceedings of the Third Symposium on Information Interaction in Context

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Interactive Information Retrieval refers to the branch of Information Retrieval that considers the retrieval process with respect to a wide range of contexts, which may affect the user's information seeking experience. The identification and representation of such contexts has been the object of the principle of Polyrepresentation, a theoretical framework for reasoning about different representations arising from interactive information retrieval in a given context. Although the principle of Polyrepresentation has received attention from many researchers, not much empirical work has been done based on it. One reason may be that it has not yet been formalised mathematically.In this paper we propose an up-to-date and flexible mathematical formalisation of the principle of Polyrepresentation for information needs. Specifically, we apply Subjective Logic to model different representations of information needs as beliefs marked by degrees of uncertainty. We combine such beliefs using different logical operators, and we discuss these combinations with respect to different retrieval scenarios and situations. A formal model is introduced and discussed, with illustrative applications to the modelling of information needs.

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Section: Mapping Opinions To Evidencementioning

confidence: 99%

Section: Formal Logic In Irmentioning

confidence: 99%

Section: Formal Logic In Irmentioning

confidence: 99%

See 1 more Smart Citation

A subjective logic formalisation of the principle of polyrepresentation for information needs

Lioma

Larsen

Schuetze

et al. 2010

Proceedings of the Third Symposium on Information Interaction in Context

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show abstract

“…Belief revision [54] is being investigated to cope with vague queries. Logic is also used inside the IR system for specific tasks, for example, ranking [82], query representation [58], estimation of query difficulty [57], and meta-fusion of classical IR [39]. Zerarga and Djouadi [98] present the generalization that the logical approach provides of various IR models.…”

Section: Recent Logical Ir Systemsmentioning

confidence: 99%

Modeling Information Retrieval by Formal Logic

et al. 2019

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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-theart 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.K. Abdulahhad et al. information need, formulated by a query. Based on this definition, an IRS has to deal with a collection of documents, with users' information needs, and with the notion of relevance.In IR, documents are carriers of information; in their original forms, they are humanunderstandable objects (e.g., Web pages, articles, books, and images), which an IRS must transform into machine-understandable objects. This process is called indexing, and its outcome is the association of a set of features (terms in textual documents) with documents. These features constitute the basic elements employed to formally represent a document. A user's information needs are motivated by a user's information gap; a query is a representation of these needs. Once formal representations have been provided for both documents and queries, the system compares them to assess the relevance of each document to the considered query.Relevance is a complex notion composed of several dimensions, such as topicality, popularity, and novelty, as has been well pointed out in the literature [33,65,95,96]. An IRS can only estimate relevance, and generally topicality is the core relevance dimension. The assessment of topical relevance 1 relies on the definition of a model, the IR model, which provides a formal means to represent and compare both documents and queries. In this survey we use the term retrieval status value (RSV) to indicate the numerical value produced by the estimate of topical relevance.Different mathematical theories have been employed to define IR models, w...

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“…Any type and amount of contextual information that can potentially be useful may be used as evidence, for instance statistical, linguistic, algorithmic or other features of the queries (e.g. see [14] for pragmatic query features). Additional evidence can be modelled by introducing more opinions.…”

Section: Mapping Opinions To Evidencementioning

confidence: 99%

Proceedings of the 4th Information Interaction in Context Symposium

2012

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According to the principle of polyrepresentation, retrieval accuracy may improve through the combination of multiple and diverse information object representations about e.g. the context of the user, the information sought, or the retrieval system [9,10]. Recently, the principle of polyrepresentation was mathematically expressed using subjective logic [12], where the potential suitability of each representation for improving retrieval performance was formalised through degrees of belief and uncertainty [15]. No experimental evidence or practical application has so far validated this model.We extend the work of Lioma et al. (2010) [15], by providing a practical application and analysis of the model. We show how to map the abstract notions of belief and uncertainty to real-life evidence drawn from a retrieval dataset. We also show how to estimate two different types of polyrepresentation assuming either (a) independence or (b) dependence between the information objects that are combined. We focus on the polyrepresentation of different types of context relating to user information needs (i.e. work task, user background knowledge, ideal answer) and show that the subjective logic model can predict their optimal combination prior and independently to the retrieval process.

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A Belief Model of Query Difficulty That Uses Subjective Logic

Cited by 7 publications

References 22 publications

A subjective logic formalisation of the principle of polyrepresentation for information needs

A subjective logic formalisation of the principle of polyrepresentation for information needs

Modeling Information Retrieval by Formal Logic

Proceedings of the 4th Information Interaction in Context Symposium

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