A framework for BM25F-based XML retrieval

Itakura, Kelly Y.; Clarke, Charles L. A.

doi:10.1145/1835449.1835644

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…Existing methods take advantage of the fact that entities have rich fielded information and propose a variety of fielded retrieval methods such as BM25F [22,13,26] and FSDM [32]. In FSDM, different fields of an entity are categorized into five final fields: names, attributes, categories, related entity names, and similar entity names.…”

Section: Related Workmentioning

confidence: 99%

Semantic Driven Fielded Entity Retrieval

Naseri¹,

Sarwar²,

Allan³

2019

Preprint

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A common approach for knowledge-base entity search is to consider an entity as a document with multiple fields. Models that focus on matching query terms in different fields are popular choices for searching such entity representations. An instance of such a model is FSDM (Fielded Sequential Dependence Model). We propose to integrate fieldlevel semantic features into FSDM. We use FSDM to retrieve a pool of documents, and then to use semantic field-level features to rerank those documents. We propose to represent queries as bags of terms as well as bags of entities, and eventually, use their dense vector representation to compute semantic features based on query document similarity. Our proposed re-ranking approach achieves significant improvement in entity retrieval on the DBpedia-Entity (v2) dataset over existing FSDM model. Specifically, for all queries we achieve 2.5% and 1.2% significant improvement in NDCG@10 and NDCG@100, respectively. * equal contribution Copyright c CIKM 2018 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

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Section: Related Workmentioning

confidence: 99%

Semantic Driven Fielded Entity Retrieval

Naseri¹,

Sarwar²,

Allan³

2019

Preprint

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“…With its well-defined standard, it is adopted for representing contents that requires both meanings and structures. As it has been well received by both research and commercial communities, development of methods like query languages (Chamberlin, 2002;Boag et al, 2007;Trotman, 2009;Carmel et al, 2003) query optimization (Petkova et al, 2009;Gan and Phang, 2014a), retrieval models (Itakura and Clarke, 2010;Li and van der Wiede, 2009), search engines (Taha and Elmasri, 2010;Theobald et al, 2008;Liu et al, 2007;Graupmann et al, 2004;Cohen et al, 2003) and schema definitions (Fallside and Walmsley, 2004) can be seen in many recent works.…”

Section: Motivationmentioning

confidence: 99%

Finding target and constraint concepts for XML query construction

Gan

Phang

2015

International Journal of Web Information Systems

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Purpose -This paper aims to focus on automatic selection of two important structural concepts required in an XML query, namely, target and constraint concepts, when given a keywords query. Due to the diversities of concepts used in XML resources, it is not easy to select a correct concept when constructing an XML query. Design/methodology/approach -In this paper, a Context-based Term Weighting model that performs term weighting based on part of documents. Each part represents a specific context, thus offering better capturing of concept and term relationship. For query time analysis, a Query Context Graph and two algorithms, namely, Select Target and Constraint (QC) and Select Target and Constraint (QCAS) are proposed to find the concepts for constructing XML query. Findings -Evaluations were performed using structured document for conference domain. For constraint concept selection, the approach CTXϩTW achieved better result than its baseline, NCTX, when search term has ambiguous meanings by using context-based scoring for the concepts. CTXϩTW also shows its stability on various scoring models like BM25, TFIEF and LM. For target concept selection, CTXϩTW outperforms the standard baseline, SLCA, whereas it also records higher coverage than FCA, when structural keywords are used in query. Originality/value -The idea behind this approach is to capture the concepts required for term interpretation based on parts of the collections rather than the entire collection. This allows better selection of concepts, especially when a structured XML document consists many different types of information.

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“…Stephen et al [ 2 ] and Robertson and Zaragoza [ 26 ] present BM25F as an extension of the baseline BM25 [ 27 ] scoring function that is adapted to score field documents. Using the BM25F scheme presented in [ 28 ], an element score is computed as follows:

where Score( e , q ) measures the relevance of element e to query q , tf e , f is a weighted normalized term frequency, K is a common tuning parameter for the BM25, and W t is the inverse document frequency weight of term t .…”

Section: Data Model and Notionsmentioning

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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A framework for BM25F-based XML retrieval

Cited by 8 publications

References 12 publications

Semantic Driven Fielded Entity Retrieval

Semantic Driven Fielded Entity Retrieval

Finding target and constraint concepts for XML query construction

An Exponentiation Method for XML Element Retrieval

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