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XAR: An Integrated Framework for Semantic Extraction and Annotation

XAR: An Integrated Framework for Semantic Extraction and Annotation

Naveen Ashish, Sharad Mehrotra
Copyright: © 2010 |Pages: 20
ISBN13: 9781605668949|ISBN10: 160566894X|EISBN13: 9781605668956
DOI: 10.4018/978-1-60566-894-9.ch011
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MLA

Ashish, Naveen, and Sharad Mehrotra. "XAR: An Integrated Framework for Semantic Extraction and Annotation." Cases on Semantic Interoperability for Information Systems Integration: Practices and Applications, edited by Yannis Kalfoglou, IGI Global, 2010, pp. 235-254. https://doi.org/10.4018/978-1-60566-894-9.ch011

APA

Ashish, N. & Mehrotra, S. (2010). XAR: An Integrated Framework for Semantic Extraction and Annotation. In Y. Kalfoglou (Ed.), Cases on Semantic Interoperability for Information Systems Integration: Practices and Applications (pp. 235-254). IGI Global. https://doi.org/10.4018/978-1-60566-894-9.ch011

Chicago

Ashish, Naveen, and Sharad Mehrotra. "XAR: An Integrated Framework for Semantic Extraction and Annotation." In Cases on Semantic Interoperability for Information Systems Integration: Practices and Applications, edited by Yannis Kalfoglou, 235-254. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-894-9.ch011

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Abstract

The authors present the XAR framework that allows for free text information extraction and semantic annotation. The language underpinning XAR, the authors argue, allows for the inclusion of probabilistic reasoning with the rule language, provides higher level predicates capturing text features and relationships, and defines and supports advanced features such as token consumption and stratified negotiation in the rule language and semantics. The XAR framework also allows the incorporation of semantic information as integrity constraints in the extraction and annotation process. The XAR framework aims to fill in a gap, the authors claim, in the Web based information extraction systems. XAR provides an extraction and annotation framework by permitting the integrated use of hand-crafted extraction rules, machine-learning based extractors, and semantic information about the particular domain of interest. The XAR system has been deployed in an emergency response scenario with civic agencies in North America and in a scenario with an IT department of a county level community clinic.

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