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Keyword search for smallest lowest common ancestors (SLCAs) is an important approach to identify interesting data nodes in XML documents. With the rapid growth of XML data in Internet, how to effectively process massive XML data becomes an interesting topic. As an open-source cloud computing platform developed in
Keyword search query processing is considered as the most promising way of information retrieval over XML data in present days as it relieves user from understanding complex schemas of XML document and writing difficult queries using XPath and XQuery. Till date various query processing techniques have been proposed to
of ontology-aware keyword search of XML documents with a particular focus on EMR XML documents. Our current prototypes and experiments use the health level seven (HL7) clinical document architecture (CDA) Release 2.0 standard of EMR representation and the systematized nomenclature of human and veterinary medicine
In last years, XML has become the standard for information representing, exchanging, and publishing. So, querying XML data is a well-explored topic in both database (DB) and information retrieval (IR) fields, which shows proximity search is well suited to XML documents, which are often modelled as labelled trees. Based on the observations that there are some semantic restrictions in userpsilas query,...
A labeled text corpus made up of Turkish papers' titles, abstracts and keywords is collected. The corpus includes 35 number of different disciplines, and 200 documents per subject. This study presents the text corpus' collection and content. The classification performance of Term Frequcney — Inverse Document
To get semantic related searching results based on simple keywords, XML search engine not only need to search the matched nodes but also need to check whether those matched nodes are semantic related nodes in XML tree. Since the judgment on the semantic related nodes might cost much time, we first use mining
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