Proximity-aware scoring for XML retrieval

Broschart, Andreas; Schenkel, Ralf

doi:10.1145/1390334.1390535

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…To our knowledge, there are only three proposals to this end. In the first one Broschart and Schenkel (2008) extend the proximity score initially introduced by Büttcher et al 2006: the structure is taken into account when computing the distance between the term occurrences by introducing virtual gaps at the border of elements in accordance with the element tags. Experiments performed at the document level (classical IR) showed that proximity scoring improves the precision and secondly that the structure gives an additional improvement, but, at the element level (focused IR), the effect of the structure is not positive.…”

Section: Proximity and Structurementioning

confidence: 99%

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Using proximity and tag weights for focused retrieval in structured documents

2014

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Focused information retrieval is concerned with the retrieval of small units of information. In this context, the structure of the documents as well as the proximity among query terms have been found useful for improving retrieval effectiveness. In this article, we propose an approach combining the proximity of the terms and the tags which mark these terms. Our approach is based on a Fetch and Browse method where the fetch step is performed with BM25 and the browse step with a structure enhanced proximity model. In this way, the ranking of a document depends not only upon the existence of the query terms within the document but also upon the tags which mark these terms. Thus, the document tends to be highly relevant when query terms are close together and are emphasized by tags. The evaluation of this model on a large XML structured collection provided by the INEX 2010 XML IR evaluation campaign shows that the use of term proximity and structure improves the retrieval effectiveness of BM25 in the context of focused information retrieval.

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Section: Proximity and Structurementioning

confidence: 99%

“…Only Broschart and Schenkel (2008) and Beigbeder (2010) proposed methods to achieve focused retrieval. Moreover, only that of Beigbeder proved its effectiveness in this context.…”

Section: Proximity and Structurementioning

confidence: 99%

Using proximity and tag weights for focused retrieval in structured documents

2014

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“…Broschart and Schenkel [ 5 ] presented the use of proximity-aware scoring functions that lead to significant effectiveness improvements for XML retrieval. This method introduces modified proximity scores that take the document structure as follows:

…”

Section: Data Model and Notionsmentioning

confidence: 99%

“…Several form of the document's structure based retrieval models have been developed, such as BM25F [ 2 ] ranking function that is composed of several document fields with potentially different degrees of importance; PRM-S [ 3 ] is based on probabilistic retrieval model; and FRM [ 4 ] is the relevance feedback function based on the language model. Broschart and Schenkel presented the proximity weighting to improve the search system [ 5 ]. On the other hand, it is based on user queries, such as QRX [ 6 ] which is based on tree matching model without knowing the exact structure of the data, using the similarity measure of the vector space model.…”

Section: Introductionmentioning

confidence: 99%

An Exponentiation Method for XML Element Retrieval

Wichaiwong

2014

The Scientific World Journal

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XML document is now widely used for modelling and storing structured documents. The structure is very rich and carries important information about contents and their relationships, for example, e-Commerce. XML data-centric collections require query terms allowing users to specify constraints on the document structure; mapping structure queries and assigning the weight are significant for the set of possibly relevant documents with respect to structural conditions. In this paper, we present an extension to the MEXIR search system that supports the combination of structural and content queries in the form of content-and-structure queries, which we call the Exponentiation function. It has been shown the structural information improve the effectiveness of the search system up to 52.60% over the baseline BM25 at MAP.

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“…[22] propose an improvement of this by using query log analysis. As there are no adequate logs for structured IR queries yet, we follow an approach similar to [5], where the gap between two terms separated by an XML tag, e.g. appearing in different XML elements, accounts for a certain amount of penalty points (equivalent with the same amount of consecutive terms).…”

Section: Formula 1 Global Collection Frequency Cf Of Xterm Tmentioning

confidence: 99%

Routing of structured queries in large-scale distributed systems

Winter

2008

Proceedings of the 2008 ACM Workshop on Large-Scale Distributed Systems for Information Retrieval

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In order to search XML-document collections, structural information -given by a user in the form of a structured query or provided by the self-describing structure of XML-documentshave been used in the past years to improve Information Retrieval (IR) quality in terms of recall and precision. However, all known approaches have only been used in classical client-/server (C/S) architectures. None have ever been applied to improve retrieval in large-scale distributed systems such as Peer-to-Peer (P2P) networks, where efficiency issues have to be dealt with carefully, e.g. in order to reduce communication overhead between distributed nodes. As P2P networks can be considered promising alternatives to C/S-systems for storing large amounts of information including XML-documents, possibilities for improving the retrieval in such networks should be investigated.In this paper, we concentrate on query routing in such a scenario and raise the question, how structured queries can be routed in a highly distributed environment so as to increase both efficiency and effectiveness. We provide an infrastructure for investigating this question and propose techniques for performing routing based on a mixture of document-, element-, collection-and peerevidence. We also report on preliminary evaluation results with the INEX collection.

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Proximity-aware scoring for XML retrieval

Cited by 8 publications

References 4 publications

Using proximity and tag weights for focused retrieval in structured documents

Using proximity and tag weights for focused retrieval in structured documents

An Exponentiation Method for XML Element Retrieval

Routing of structured queries in large-scale distributed systems

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