A flexible model for retrieval of SGML documents

Myaeng, Sung Hyon; Jang, Don-Hyun; Kim, Mun-Seok; Zhoo, Zong-Cheol

doi:10.1145/290941.290980

Cited by 91 publications

(55 citation statements)

References 8 publications

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“…XML inverted list indices (e.g., [24,32,39]) typically store for each keyword in the document collection, the list of XML elements that directly contain the keyword. Figure 4 shows an example inverted list for our example document.…”

Section: Path-valuesmentioning

confidence: 99%

Efficient keyword search over virtual XML views

et al. 2009

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Emerging applications such as personalized portals, enterprise search and web integration systems often require keyword search over semi-structured views. However, traditional information retrieval techniques are likely to be expensive in this context because they rely on the assumption that the set of documents being searched is materialized. In this paper, we present a system architecture and algorithm that can efficiently evaluate keyword search queries over virtual (unmaterialized) XML views. An interesting aspect of our approach is that it exploits indices present on the base data and thereby avoids materializing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that by solely using indices, we can still score the results for queries over the virtual view, and the resulting scores and rank order are the same as if the view was materialized. Our performance evaluation using the INEX data set in the Quark [8] open-source XML database system indicates that the proposed approach is scalable and efficient.

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Section: Path-valuesmentioning

confidence: 99%

Efficient keyword search over virtual XML views

et al. 2009

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“…In this section we shall describe these two models and how they are used to retrieve document components from a document collection through probabilistic inference (see [2,3] for more details). Alternative probabilistic graphical models for structured information retrieval can also be found in the literature [6,7,9]. We assume a basic knowledge about graphical models.…”

Section: Probabilistic Graphical Models In the Garnata Systemmentioning

confidence: 99%

The Garnata Information Retrieval System at INEX’07

Campos

Fernández‐Luna

Huete

et al. 2008

Focused Access to XML Documents

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Abstract. This paper exposes the results of our participation at INEX'07 in the AdHoc track and the comparison of these results with respect to the ones obtained last year. Three runs were submitted to each of the Focused, Relevant In Context and Best In Context tasks, all of them obtained with Garnata, our Information Retrieval System for structured documents. As in the past year, we use a model based on Influence Diagrams, the CID model. The result of our participation has been better than the last year and we have reached an acceptable position in the ranking for the three tasks. In the paper we describe the model, the system and we show the differences between our systems at INEX'06 and INEX'07, which make possible to get a better performance.

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“…Ribeiro and Muntz (1996), Indrawan et al (1994) and de Campos et al (2003a proposed other approaches also based on belief networks which were conceived for flat document retrieval. An extension of the Inquery model, designed to incorporate structural and textual information, has been proposed by Myaeng et al (1998). In this approach, a document is represented by a tree.…”

Section: Related Workmentioning

confidence: 99%

“…Crestani et al (2003aCrestani et al ( , 2003b propose an extension of the BN model of de Campos et al (2003a) for structured retrieval. Here the information goes in the reverse way compared to Myaeng et al (1998), i.e. it goes from term nodes up to the document node.…”

Section: Related Workmentioning

confidence: 99%

“…Existing BN models for flat (Callan et al 1992) or structured (Myaeng et al 1998) documents use binary values (R, ¬R). This is too limitative for SIR since quantifying a doxel relevance is more complex than for whole documents and should somewhat be related to the two dimensional scale (specificity, exhaustivity) proposed for INEX.…”

Section: Bn Variables and Notationsmentioning

confidence: 99%

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A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection

Piwowarski

Gallinari

2005

Inf Retrieval

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Abstract. Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents-which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.

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A flexible model for retrieval of SGML documents

Cited by 91 publications

References 8 publications

Efficient keyword search over virtual XML views

Efficient keyword search over virtual XML views

The Garnata Information Retrieval System at INEX’07

A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection

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