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{{Short description|Content-based retrieval of XML documents}}
'''XML retrieval''', or '''XML information retrieval''', is the content-based retrieval of documents structured with [[XML]] (eXtensible Markup Language). As such it is used for computing [[Relevance (information retrieval)|relevance]] of XML documents.<ref>{{Cite web |last=Lalmas |first=Mounia |date=2009 |title=XML Retrieval |url=https://fi.wikipedia.org/wiki/XML-tiedonhaku#L%C3%A4hteet |publisher=Morgan & Claypool}}</ref>
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Most XML retrieval approaches do so based on techniques from the [[information retrieval]] (IR) area, e.g. by computing the similarity between a query consisting of keywords (query terms) and the document. However, in XML-Retrieval the query can also contain [[Data structure|structural]] [[Hint (SQL)|hints]]. So-called "content and structure" (CAS) queries enable users to specify what structure the requested content can or must have.
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Taking advantage of the [[Self-documenting|self-describing]] structure of XML documents can improve the search for XML documents significantly. This includes the use of CAS queries, the weighting of different XML elements differently and the focused retrieval of subdocuments.
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Ranking in XML-Retrieval can incorporate both content relevance and structural similarity, which is the resemblance between the structure given in the query and the structure of the document. Also, the retrieval units resulting from an XML query may not always be entire documents, but can be any deeply nested XML elements, i.e. dynamic documents. The aim is to find the smallest retrieval unit that is highly relevant. Relevance can be defined according to the notion of specificity, which is the extent to which a retrieval unit focuses on the topic of request.<ref name="INEX2006">{{
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An overview of two potential approaches is available.<ref>{{
===Traditional XML query languages===
[[Query language]]s such as the [[W3C]] standard [[XQuery]]<ref>{{
===Databases===
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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>{{
== Data-centric XML datasets ==
For data-centric XML datasets, the unique and distinct keyword search method, namely, XDMA<ref>{{Cite journal|last1=Selvaganesan|first1=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|journal=International Journal of Software Engineering and Knowledge Engineering|language=en-US|volume=24|issue=4|pages=591–615|doi=10.1142/s0218194014500223|year=2014}}</ref> for XML databases is designed and developed based on dual indexing and mutual summation.
==See also==
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*[[Information retrieval applications]]
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{{DEFAULTSORT:Xml-Retrieval}}
[[Category:XML]]
[[Category:Information retrieval genres]]
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