A Model for Information Retrieval Based on Possibilistic Networks

Brini, Asma; Dubois, Didier

doi:10.1007/11575832_31

Cited by 16 publications

(18 citation statements)

References 15 publications

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“…This term is called discriminative term and it is a term that frequently appears in few elements of the semistructured document [6]. The factor commonly used in IR to quantify the discriminative power of a term is idf (ief in semistructured IR).…”

Section: Value Of the Arc Element Node-term Node (Relevance Of The Comentioning

confidence: 99%

“…In order to evaluate the quantities, П(di/Q) and N(di /Q), we proceed in the following way (Remark: we were inspired by the model developed by A. Brini, M. Boughanem and D. Dubois [6] and used for full text retrieval,…”

Section: Query Evaluation Processmentioning

confidence: 99%

“…In order to evaluate the various degrees Π and N between the nodes of the network, we were inspired by the model [6] used for full text information retrieval.…”

Section: Query Evaluation Processmentioning

confidence: 99%

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Possibility and necessity measures for relevance assessment

Mechmache

Alimazighi

2007

Proceedings of the ACM First Ph.D. Workshop in CIKM

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The major question raised in information retrieval on semistructured documents relates to the manner of effectively handling the structure and the contents of the document for better answering the user's needs. These needs can be formulated by queries composed of only key words or key words and structural constraints. In this paper, we are interested in Information Retrieval in semi-structured document like XML. For these purposes, we present a model for the semi-structured information retrieval, based on the possibilistic networks. The documentelements and elements -terms relations are modelled by measures of possibility and necessity. In this model, the user's query starts a process of propagation to recover documents or portions of documents necessarily or at least possibly relevant. An example of such a research is proposed in order to illustrate the presented approach.

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Section: Value Of the Arc Element Node-term Node (Relevance Of The Comentioning

confidence: 99%

Section: Query Evaluation Processmentioning

confidence: 99%

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Possibility and necessity measures for relevance assessment

Mechmache

Alimazighi

2007

Proceedings of the ACM First Ph.D. Workshop in CIKM

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“…This latter point is handled at the technical level by a completion procedure that assesses positive weights to terms that do not appear directly in the documents [17]. The possibility of the completion of queries has also been raised [19].…”

Section: Fuzzy Concept-based Indexingmentioning

confidence: 99%

Vagueness and uncertainty in information retrieval

Kraft

Pasi

Bordogna

2006

Proceedings of the 2006 International Workshop on Research Issues in Digital Libraries

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The field of fuzzy information systems has grown and is maturing. In this paper, some applications of fuzzy set theory to information retrieval are described, as well as the more recent outcomes of research in this field. Fuzzy set theory is applied to information retrieval with the main aim being to define flexible systems, i.e., systems that can represent and manage the vagueness and subjectivity which characterizes the process of information representation and retrieval, one of the main objectives of artificial intelligence.

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“…. Others have extended the classical IR model to take into account particular situations, for instance to use ordinal terms weights [5], or to use both possibility and necessity measures for terms weights [6]. Most of these papers are not really related to our approach.…”

Section: Related Workmentioning

confidence: 99%

Graded-Inclusion-Based Information Retrieval Systems

Bosc¹,

Claveau²,

Pivert³

et al. 2009

Lecture Notes in Computer Science

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Abstract. This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. In this framework, documents and queries are represented by fuzzy sets, which are paired with operations like fuzzy implications and T-norms. Through different experiments, it is shown that only some among the wide range of fuzzy operations are relevant for information retrieval. When appropriate settings are chosen, it is possible to mimic classical systems, thus yielding results rivaling those of state-of-the-art systems. These positive results validate the proposed approach, while negative ones give some insights on the properties needed by such a model. Moreover, this paper shows the added-value of this graded inclusion-based model, which gives new and theoretically grounded ways for a user to easily weight his query terms, to include negative information in his queries, or to expand them with related terms.

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A Model for Information Retrieval Based on Possibilistic Networks

Cited by 16 publications

References 15 publications

Possibility and necessity measures for relevance assessment

Possibility and necessity measures for relevance assessment

Vagueness and uncertainty in information retrieval

Graded-Inclusion-Based Information Retrieval Systems

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