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IJMLC 2011 Vol.1(2): 113-119 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.17

From Text to Knowledge: Semantic Entity Extraction using YAGO Ontology

Farhad Abedini, Fariborz Mahmoudi, and Amir Hossein Jadidinejad

Abstract—Semantic entities are the entities that their concepts are available in a knowledgebase. Here, a new system will be introduced to extract semantic entities from texts. For this aim a new disambiguation method is suggested to match each of ambiguous entity with one of semantic entities in the knowledgebase. The YAGO ontology is used in this method as state of the art of knowledgebase in this field. Since entities in YAGO are meaningful, so in this method, semantic entities are obtained. Comparing the results with the literatures shows that the results of this new approach can be sufficiently reliable.

Index Terms—Disambiguation, Information Extraction, Semantic Entity Extraction, YAGO Ontology.

Farhad Abedini is with the Electrical and Computer Engineering Department, and member of Young Researchers Club, Islamic Azad University, Roudsar and Amlash branch, Roudsar, Iran. Phone: +98-01426215051, e-mail: abedini.ac@gmail.com. Fariborz Mahmoudi is with the Electrical and Computer Engineering Department, Islamic Azad University, Qazvin Branch, Qazvin, Iran. Phone: +9802813665275-3665276-, e-mail: mahmoudi@qiau.ac.ir. Amir Hossein Jadidinejad is with the Electrical and Computer Engineering Department, Islamic Azad University, Qazvin Branch, Qazvin, Iran. Phone: +9802813665275-3665276, e-mail: amir@jadidi.info.

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Cite: Farhad Abedini, Fariborz Mahmoudi, and Amir Hossein Jadidinejad, "From Text to Knowledge: Semantic Entity Extraction using YAGO Ontology," International Journal of Machine Learning and Computing vol. 1, no. 2, pp. 113-119, 2011.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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