Keyword proximity search on XML graphs

Hristidis, Vagelis; Papakonstantinou, Yannis; Balmin, Andrey

doi:10.1109/icde.2003.1260806

Cited by 181 publications

(192 citation statements)

References 8 publications

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“…Keyword search over XML databases has also attracted interest recently [7,12,9]. Florescu et al [7] extend XML query languages to enable keyword search at the granularity of XML elements, which helps novice users formulate queries.…”

Section: Related Workmentioning

confidence: 99%

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Efficient IR-Style Keyword Search over Relational Databases

Hristidis

Gravano

Papakonstantinou

2003

Proceedings 2003 VLDB Conference

476

698

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Applications in which plain text coexists with structured data are pervasive. Commercial relational database management systems (RDBMSs) generally provide querying capabilities for text attributes that incorporate state-of-the-art information retrieval (IR) relevance ranking strategies, but this search functionality requires that queries specify the exact column or columns against which a given list of keywords is to be matched. This requirement can be cumbersome and inflexible from a user perspective: good answers to a keyword query might need to be "assembled" -in perhaps unforeseen ways-by joining tuples from multiple relations. This observation has motivated recent research on free-form keyword search over RDBMSs. In this paper, we adapt IR-style document-relevance ranking strategies to the problem of processing free-form keyword queries over RDBMSs. Our query model can handle queries with both AND and OR semantics, and exploits the sophisticated single-column text-search functionality often available in commercial RDBMSs. We develop query-processing strategies that build on a crucial characteristic of IR-style keyword search: only the few most relevant matches -according to some definition of "relevance"-are generally of interest. Consequently, rather than computing all matches for a keyword query, which leads to inefficient executions, our techniques focus on the top-k matches for the query, for moderate values of k. A thorough experimental evaluation over real data shows the performance advantages of our approach. *

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Section: Related Workmentioning

confidence: 99%

“…This work does not consider keyword proximity. Hristidis et al [12] view an XML database as a graph of "minimal" XML segments and find connections between them that contain all the query keywords. They focus on the presentation of the results and use view materialization techniques to provide fast response times.…”

Section: Related Workmentioning

confidence: 99%

Efficient IR-Style Keyword Search over Relational Databases

Hristidis

Gravano

Papakonstantinou

2003

Proceedings 2003 VLDB Conference

476

698

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show abstract

“…There are many techniques for ranking entities on the Web, for example PageRank 32] and HITS [33], on XML documents [34], and on the Semantic Web [35]. However, these techniques cannot be used directly to rank our search results because of the different problem nature.…”

Section: Discussionmentioning

confidence: 99%

Semantics‐Based Resource Discovery in Large‐Scale Grids

Sarbazi-Azad¹,

Zomaya²

2013

Large Scale Network‐Centric Distributed Systems

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“…Lore [18] uses Dataguides to walk users through XML schemas for composition of XML queries. Other authors have considered keyword-based search over XML data [26,22,8]. CLIDE [31] offers an interactive interface for users to pose queries in a data-integration environment.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Heterogeneous Data

Cammarano

Dong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

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Abstract-Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.

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Keyword proximity search on XML graphs

Cited by 181 publications

References 8 publications

Efficient IR-Style Keyword Search over Relational Databases

Efficient IR-Style Keyword Search over Relational Databases

Semantics‐Based Resource Discovery in Large‐Scale Grids

Visualization of Heterogeneous Data

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