A New Framework for Personal Name Disambiguation

Lilia Georgieva; Sirisuda Buatongkue

doi:10.1007/978-3-030-01174-1_76

A New Framework for Personal Name Disambiguation

Lilia Georgieva, Sirisuda Buatongkue

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

In this paper we study the problem of personal name disambiguation (NED). We develop a framework to address the three challenges in personal name disambiguation: (i) identification of referential ambiguity, (ii) identification of lexical ambiguity, and (iii) predicting the NIL value, that is the value when a named entity cannot be mapped to a knowledge base. Our framework includes extractor, searcher and disambiguator. Experimental results evaluated on real-world data sets, show that our framework and algorithm provide accuracy in personal name linking up to 92%, which is higher than the accuracy of previously developed algorithms.

Original language	English
Title of host publication	Intelligent Computing
Subtitle of host publication	SAI 2018
Publisher	Springer
Pages	995-1009
Number of pages	15
ISBN (Electronic)	9783030011741
ISBN (Print)	9783030011734
DOIs	https://doi.org/10.1007/978-3-030-01174-1_76
Publication status	Published - 2 Nov 2018
Event	Computing Conference 2018 - London, United Kingdom Duration: 10 Jul 2018 → 12 Jul 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	858
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computing Conference 2018
Country/Territory	United Kingdom
City	London
Period	10/07/18 → 12/07/18

Keywords

personal name disambiguation
data cleaning
lexical ambiguity

ASJC Scopus subject areas

General Computer Science

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10.1007/978-3-030-01174-1_76

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This is a post-peer-review, pre-copyedit version of an article published in Advances in Intelligent Systems and Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-01174-1_76
Accepted author manuscript, 530 KB

Lilia Georgieva
- L.Georgievahw.acuk
- School of Mathematical & Computer Sciences - Assistant Professor
- School of Mathematical & Computer Sciences, Computer Science - Assistant Professor
Person: Academic (Research & Teaching)

Cite this

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title = "A New Framework for Personal Name Disambiguation",

abstract = "In this paper we study the problem of personal name disambiguation (NED). We develop a framework to address the three challenges in personal name disambiguation: (i) identification of referential ambiguity, (ii) identification of lexical ambiguity, and (iii) predicting the NIL value, that is the value when a named entity cannot be mapped to a knowledge base. Our framework includes extractor, searcher and disambiguator. Experimental results evaluated on real-world data sets, show that our framework and algorithm provide accuracy in personal name linking up to 92%, which is higher than the accuracy of previously developed algorithms.",

keywords = "personal name disambiguation, data cleaning, lexical ambiguity",

author = "Lilia Georgieva and Sirisuda Buatongkue",

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language = "English",

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series = "Advances in Intelligent Systems and Computing",

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AU - Buatongkue, Sirisuda

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N2 - In this paper we study the problem of personal name disambiguation (NED). We develop a framework to address the three challenges in personal name disambiguation: (i) identification of referential ambiguity, (ii) identification of lexical ambiguity, and (iii) predicting the NIL value, that is the value when a named entity cannot be mapped to a knowledge base. Our framework includes extractor, searcher and disambiguator. Experimental results evaluated on real-world data sets, show that our framework and algorithm provide accuracy in personal name linking up to 92%, which is higher than the accuracy of previously developed algorithms.

AB - In this paper we study the problem of personal name disambiguation (NED). We develop a framework to address the three challenges in personal name disambiguation: (i) identification of referential ambiguity, (ii) identification of lexical ambiguity, and (iii) predicting the NIL value, that is the value when a named entity cannot be mapped to a knowledge base. Our framework includes extractor, searcher and disambiguator. Experimental results evaluated on real-world data sets, show that our framework and algorithm provide accuracy in personal name linking up to 92%, which is higher than the accuracy of previously developed algorithms.

KW - personal name disambiguation

KW - data cleaning

KW - lexical ambiguity

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DO - 10.1007/978-3-030-01174-1_76

M3 - Conference contribution

SN - 9783030011734

T3 - Advances in Intelligent Systems and Computing

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EP - 1009

BT - Intelligent Computing

PB - Springer

T2 - Computing Conference 2018

Y2 - 10 July 2018 through 12 July 2018

ER -

A New Framework for Personal Name Disambiguation

Abstract

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Lilia Georgieva

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