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===Information retrieval===
Classic information retrieval models such as the [[vector space model]] provide relevance ranking, but do not include document structure; only flat queries are supported. Also, they apply a static document concept, so retrieval units usually are entire documents.<ref name="Schlieder2002"/> They can be extended to consider structural information and dynamic document retrieval. Examples for approaches extending the vector space models are available: they use document [[subtree]]s (index terms plus structure) as dimensions of the vector space.<ref>{{Cite web|url=http://www.cobase.cs.ucla.edu/tech-docs/sliu/SIGIR04.pdf|title=Configurable Indexing and Ranking for XML Information Retrieval|last=Liu|first=Shaorong|author2=Zou, Qinghua |author3=Chu, Wesley W. |year=2004|work=SIGIR'04|publisher=ACM|accessdate=2009-02-10}}</ref>
== Data-Centric XML Datasets ==
For Data-centric XML Datasets, the unique and distinct keyword search method, namely, XDMA<ref>{{Cite journal|last=Selvaganesan|first=S.|last2=Haw|first2=Su-Cheng|last3=Soon|first3=Lay-Ki|title=XDMA: A Dual Indexing and Mutual Summation Based Keyword Search Algorithm for XML Databases|url=http://www.worldscientific.com/doi/pdf/10.1142/S0218194014500223|journal=International Journal of Software Engineering and Knowledge Engineering|language=en-US|volume=24|issue=04|pages=591–615|doi=10.1142/s0218194014500223}}</ref> for XML databases is designed and developed based dual indexing and mutual summation.
==See also==
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