TopX: efficient and versatile top-k query processing for semistructured data

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TopX: efficient and versatile top-k query processing for semistructured data
Authors:	Martin Theobald, Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Gerhard Weikum [edit item]
Citation:	The VLDB Journal 17 (1): 81-115. 2008.
Publication type:	Journal article
Peer-reviewed:	Yes
Database(s):
DOI:	10.1007/s00778-007-0072-z.
Google Scholar cites:	Citations
Link(s):	Paper link
Added by Wikilit team:	Added on initial load
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TopX: efficient and versatile top-k query processing for semistructured data is a publication by Martin Theobald, Holger Bast, Debapriyo Majumdar, Ralf Schenkel, Gerhard Weikum.

Abstract

Recent {IR} extensions to {XML} query languages such as Xpath 1.0 {Full-Text} or the {NEXI} query language of the {INEX} benchmark series reflect the emerging interest in {IR-style} ranked retrieval over semistructured data. {TopX} is a top-k retrieval engine for text and semistructured data. It terminates query execution as soon as it can safely determine the k top-ranked result elements according to a monotonic score aggregation function with respect to a multidimensional query. It efficiently supports vague search on both content- and structure-oriented query conditions for dynamic query relaxation with controllable influence on the result ranking. The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for ranked {XML} retrieval with {XPath} {Full-Text} functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the {TopX} system, with performance experiments on large-scale corpora like {TREC} Terabyte and {INEX} Wikipedia.

Research questions

"The main contributions of this paper unfold into four main points: (1) fully implemented models and algorithms for rankedXMLretrieval with XPath Full-Text functionality, (2) efficient and effective top-k query processing for semistructured data, (3) support for integrating thesauri and ontologies with statistically quantified relationships among concepts, leveraged for word-sense disambiguation and query expansion, and (4) a comprehensive description of the TopX system, with performance experiments on large-scale corpora like TREC Terabyte and INEX Wikipedia"

Research details

Topics:	Query processing [edit item]
Domains:	Computer science [edit item]
Theory type:	Analysis, Design and action [edit item]
Wikipedia coverage:	Other [edit item]
Theories:	"Undetermined" [edit item]
Research design:	Experiment [edit item]
Data source:	[edit item]
Collected data time dimension:	Cross-sectional [edit item]
Unit of analysis:	Article [edit item]
Wikipedia data extraction:	Clone [edit item]
Wikipedia page type:	Article [edit item]
Wikipedia language:	Not specified [edit item]

Conclusion

"TopX is an efficient and effective search engine for nonschematic XML documents, with the full functionality of XPath Full-Text and supporting also the entire range of text, semistructured, and structured data. It achieves effective ranked retrieval by means of an XML-specific extension of the probabilistic-IR BM25 relevance scoring model, and also leverages thesauri and ontologies for word-sense disambiguation and robust query expansion. It achieves scalability and efficient top-k query processing by means of extended threshold algorithms with specific priority-queue management, judicious scheduling of random accesses to index entries, and probabilistic score predictions for early pruning of top-k candidates."

Comments

"TopX is an efficient and effective search engine for nonschematic XML documents, with the full functionality of XPath Full-Text and supporting also the entire range of text, semistructured, and structured data."