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Author Vargas-Vera, M.
Title A Framework for Extraction of Relations from Text using Relational Learning and Similarity Measures Type
Year 2015 Publication Journal Of Universal Computer Science Abbreviated Journal J. Univers. Comput. Sci.
Volume 21 Issue 11 Pages 1482-1495
Keywords Semantic Learning; Relational Learning; Similarity Measures; Semantic Web
Abstract Named entity recognition (NER) has been studied largely in the Information Extraction community as it is one step in the construction of an Information Extraction System. However, to extract only names without contextual information is not sufficient if we want to be able to describe facts encountered in documents, in particular, academic documents. Then, there is a need for extracting relations between entities. This task is accomplished using relational learning algorithms embedded in an Information Extraction framework. In particular, we have extended two relational learning frameworks RAPIER and FOIL. Our proposed extended frameworks are equipped with DSSim (short for Dempster-Shafer Similarity) our similarity service. Both extended frameworks were tested using an electronic newsletter consisting of news articles describing activities or events happening in an academic institution as our main application is on education.
Address [Vargas-Vera, Maria] Adolfo Ibanez Univ, Vinia Del Mar, Chile, Email: mvargasvera@gmail.com
Corporate Author (down) Thesis
Publisher Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0948-695x ISBN Medium
Area Expedition Conference
Notes WOS:000368457300008 Approved
Call Number UAI @ eduardo.moreno @ Serial 578
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