Matching named entities with the aid of Wikipedia

Research output: Contribution to journalArticle

Standard

Matching named entities with the aid of Wikipedia. / Bawakid, Abdullah; Oussalah, Mourad; Afzal, Naveed; Shim, Seong O.; Ahsan, Syed.

In: Life Science Journal, Vol. 10, No. 2, 17.06.2013, p. 1414-1426.

Research output: Contribution to journalArticle

Harvard

Bawakid, A, Oussalah, M, Afzal, N, Shim, SO & Ahsan, S 2013, 'Matching named entities with the aid of Wikipedia', Life Science Journal, vol. 10, no. 2, pp. 1414-1426.

APA

Bawakid, A., Oussalah, M., Afzal, N., Shim, S. O., & Ahsan, S. (2013). Matching named entities with the aid of Wikipedia. Life Science Journal, 10(2), 1414-1426.

Vancouver

Bawakid A, Oussalah M, Afzal N, Shim SO, Ahsan S. Matching named entities with the aid of Wikipedia. Life Science Journal. 2013 Jun 17;10(2):1414-1426.

Author

Bawakid, Abdullah ; Oussalah, Mourad ; Afzal, Naveed ; Shim, Seong O. ; Ahsan, Syed. / Matching named entities with the aid of Wikipedia. In: Life Science Journal. 2013 ; Vol. 10, No. 2. pp. 1414-1426.

Bibtex

@article{36a63283ae8d4a129dc49327e5ebb85b,
title = "Matching named entities with the aid of Wikipedia",
abstract = "In this paper we propose a novel framework using features extracted from Wikipedia for the task of Matching Named Entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links is integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of Word Sense Disambiguation and Named Entities Matching. The system performance is compared against other learning-based state-of-the-art systems and its reported results are found to be competitive. We also present a method in this paper for Named Entities Matching that is context-independent and compare its results with other systems.",
keywords = "Features extraction, Links analysis, Matching named entities, Strong links, Wikipedia, WSD",
author = "Abdullah Bawakid and Mourad Oussalah and Naveed Afzal and Shim, {Seong O.} and Syed Ahsan",
year = "2013",
month = jun,
day = "17",
language = "English",
volume = "10",
pages = "1414--1426",
journal = "Life Science Journal",
issn = "1097-8135",
publisher = "Zhengzhou University",
number = "2",

}

RIS

TY - JOUR

T1 - Matching named entities with the aid of Wikipedia

AU - Bawakid, Abdullah

AU - Oussalah, Mourad

AU - Afzal, Naveed

AU - Shim, Seong O.

AU - Ahsan, Syed

PY - 2013/6/17

Y1 - 2013/6/17

N2 - In this paper we propose a novel framework using features extracted from Wikipedia for the task of Matching Named Entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links is integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of Word Sense Disambiguation and Named Entities Matching. The system performance is compared against other learning-based state-of-the-art systems and its reported results are found to be competitive. We also present a method in this paper for Named Entities Matching that is context-independent and compare its results with other systems.

AB - In this paper we propose a novel framework using features extracted from Wikipedia for the task of Matching Named Entities. We present how the employed features are extracted from Wikipedia and how its structure is utilized. We describe how a term-concepts table constructed from Wikipedia and the redirect links is integrated in the framework. In addition, the internal links within Wikipedia along with the categories structure are also used to compute the relatedness between concepts. We evaluate the built framework and report its performance in the applications of Word Sense Disambiguation and Named Entities Matching. The system performance is compared against other learning-based state-of-the-art systems and its reported results are found to be competitive. We also present a method in this paper for Named Entities Matching that is context-independent and compare its results with other systems.

KW - Features extraction

KW - Links analysis

KW - Matching named entities

KW - Strong links

KW - Wikipedia

KW - WSD

UR - http://www.scopus.com/inward/record.url?scp=84878863369&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84878863369

VL - 10

SP - 1414

EP - 1426

JO - Life Science Journal

JF - Life Science Journal

SN - 1097-8135

IS - 2

ER -