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Automatic Extraction of Entity Alias from the Web

by Sumitra A. Jakhete, Shweta C. Dharmadhikari
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 8
Year of Publication: 2012
Authors: Sumitra A. Jakhete, Shweta C. Dharmadhikari
10.5120/ijais12-450567

Sumitra A. Jakhete, Shweta C. Dharmadhikari . Automatic Extraction of Entity Alias from the Web. International Journal of Applied Information Systems. 3, 8 ( August 2012), 5-9. DOI=10.5120/ijais12-450567

@article{ 10.5120/ijais12-450567,
author = { Sumitra A. Jakhete, Shweta C. Dharmadhikari },
title = { Automatic Extraction of Entity Alias from the Web },
journal = { International Journal of Applied Information Systems },
issue_date = { August 2012 },
volume = { 3 },
number = { 8 },
month = { August },
year = { 2012 },
issn = { 2249-0868 },
pages = { 5-9 },
numpages = {9},
url = { https://www.ijais.org/archives/volume3/number8/249-0567/ },
doi = { 10.5120/ijais12-450567 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:46:06.458511+05:30
%A Sumitra A. Jakhete
%A Shweta C. Dharmadhikari
%T Automatic Extraction of Entity Alias from the Web
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 3
%N 8
%P 5-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An individual is known by more than one name on the web. Identifying the correct alias for an entity is playing a crucial role in the field of information retrieval, relation extraction, sentiment analysis, and entity name disambiguation as well as in biomedical fields. Traditional system provides the solution on solving lexical ambiguity, but it lagged on the problem of referential ambiguity. Through this paper we emphasis on referential ambiguity to extract correct alias for a given name. Given a name alias dataset retrieves lexical pattern from a web search engine. With the help of Lexical-pattern and using second level depth extract candidate aliases. As to identify correct alias from a list of aliases we used similarity measures as well as graph mining measures such as degree distribution and clustering coefficient. We integrate different word sore and calculate the final weight of each candidate alias. There by our method providing more promising result in terms achieving a statistically significant mean reciprocal rank (MRR) of 0. 611 and improves the precision and minimize the recall that than the previous baseline method.

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Index Terms

Computer Science
Information Sciences

Keywords

Graph Mining Text Mining Web Mining Web Text Analysis

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