The constituent object parser: syntactic structure matching for information retrieval

Metzler, Douglas P.; Haas, Stephanie W.

doi:10.1145/65943.65949

Cited by 32 publications

(5 citation statements)

References 6 publications

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“…The retrieval results from syntactic relation matching appears to be no better than the results obtainable using index phrases generated using statistical methods, such as those described by Fagan (1989). Metzler & Haas (1989), Metzler, Haas, Cosic & Weise (1990), Schwarz (1990), and Ruge, Schwarz, & Warner (1991) performed syntactic processing to produce dependency trees that indicate which terms modify which other terms. Smeaton & van Rijsbergen (1988) found that the premodifier-headnoun relation (e.g., adjective-noun) has a bigger impact on retrieval than other relations.…”

Section: Relation Matching For Precision Enhancementmentioning

confidence: 99%

Semantic relations in information science

Khoo

2006

Annual Review Info Sci & Tec

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Abstract. This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies are provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering and automatic text summarization applications.

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Section: Relation Matching For Precision Enhancementmentioning

confidence: 99%

Semantic relations in information science

Khoo

2006

Annual Review Info Sci & Tec

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“…Cette intégration se fait souvent par l'ajout de descripteurs syntaxiques dans des langages à base de mots-clefs (Strzalkowski et al, 1998, Zhai et al, 1997, Gaussier et al, 2000. Plus rarement, des modèles plus expressifs utilisent directement les structures syntaxiques (Matsumura et al, 2000, Metzler et al, 1989, Smeaton, 1999, Gao et al, 2004 et effectuent une correspondance sur ces structures.…”

Section: à Travers L'état De L'artunclassified

L'expressivité des modèles de recherche d'informations précises. Le support de vocabulaires et son application à la recherche d'information médicale

Maisonnasse¹,

Berrut²,

Chevallet³

2009

Document numérique

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Nous proposons dans cet article de modéliser l'expressivité des systèmes de recherche d'information (SRI). En effet peu de cadres de modélisation sont disponibles pour spécifier les SRIs. Nous proposons un tel cadre sur lequel nous portons un intérêt particulier à la modélisation de l'expressivité. Le niveau d'expressivité est important dans les SRIs, et positionner un système au bon niveau permet d'obtenir de meilleurs résultats. Le cadre de modélisation que nous proposons permet ainsi de choisir l'expressivité d'un modèle et de comparer des modèles sur leur niveau d'expressivité. Nous montrons en dernier lieu les possibilités offertes par ce cadre pour sélectionner le niveau d'expressivité sur une tâche de recherche d'information médicale, où les utilisateurs expriment des besoins complexes. ABSTRACT. This article proposes to model the expressiveness of information retrieval systems. Indeed few modeling framework are available to model retrieval systems. We propose here such a framework, where we focus on the modeling of expressiveness. Indeed the level of expressivity is important in information retrieval and positioning system at the right level allows to achieve better results. The modelling framework that we propose allows to compare or to choose the expressiveness of one or more models. We show the possibilities provided by this framework to select the level of expressiveness on a medical information retrieval task, where users express complex needs. MOTS-CLÉS : recherche d'information, expressivité, modèle

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“…Relation-preserving matching that is crucial for analogical reasoning (Gentner et al 1986) can be achieved easily by ignoring concept labels in the process of forming association graph nodes. Likewise, by ignoring relation labels and considering concept labels and other structural information, the algorithm can perform syntactic structure matching (Metzler and Hass 1989) that takes into account simple labels in concept nodes and their dependencies (i.e. directionality) but ignores the kind of relationships between them.…”

Section: Four Types Of Cg Matchingmentioning

confidence: 99%

Conceptual graph matching: a flexible algorithm and experiments

Myaeng¹

1992

Journal of Experimental & Theoretical Artificial Intelligen

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A bstract. Graph matching is recognized as a central problem across a variety of application areas, and application-specific matchers have been developed with different simplifying assumptions to reduce the computational complexity. Graph matching is viewed as a form of plausible reasoning when conceptual information contained in graphs are considered, and thus requires an underlying algorithm flexible and general enough to accommodate application-specific matching heuristics and schemes that determine the degree of plausibility. This paper presents such an algorithm, based on the notion of association graphs, developed for matching Sowa's conceptual graphs (CG). While the general subgraph isomorphism problem is known to be NP-complete, matching graphs containing conceptual information appears to be computationally tractable. Following the detailed description of the algorithm, some experimental results are shown to discuss the time complexity of the algorithm and its practicality.

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The constituent object parser: syntactic structure matching for information retrieval

Cited by 32 publications

References 6 publications

Semantic relations in information science

Semantic relations in information science

L'expressivité des modèles de recherche d'informations précises. Le support de vocabulaires et son application à la recherche d'information médicale

Conceptual graph matching: a flexible algorithm and experiments

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