Lexical Disambiguation (CKBD)

A tool to identify and resolve semantic conflicts using Context Knowledge

Said Al Tahat, Kamsuriah Ahmad

Research output: Contribution to journalArticle

Abstract

The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the realworld semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are "presented"; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.

Original languageEnglish
Pages (from-to)213-219
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Semantics
Names
Labels
Systems Integration
Confusion
Aptitude
Vocabulary
Conflict (Psychology)
Language

Keywords

  • Natural language processing
  • Schema integration
  • Schema matching
  • Semantic ambiguity
  • Word sense disambiguation

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

@article{fa4cabe6f68a41e28d228d126279c28e,
title = "Lexical Disambiguation (CKBD): A tool to identify and resolve semantic conflicts using Context Knowledge",
abstract = "The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the realworld semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are {"}presented{"}; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.",
keywords = "Natural language processing, Schema integration, Schema matching, Semantic ambiguity, Word sense disambiguation",
author = "{Al Tahat}, Said and Kamsuriah Ahmad",
year = "2019",
month = "1",
day = "1",
doi = "10.18517/ijaseit.9.1.6387",
language = "English",
volume = "9",
pages = "213--219",
journal = "International Journal on Advanced Science, Engineering and Information Technology",
issn = "2088-5334",
publisher = "INSIGHT - Indonesian Society for Knowledge and Human Development",
number = "1",

}

TY - JOUR

T1 - Lexical Disambiguation (CKBD)

T2 - A tool to identify and resolve semantic conflicts using Context Knowledge

AU - Al Tahat, Said

AU - Ahmad, Kamsuriah

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the realworld semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are "presented"; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.

AB - The schema matching process is a fundamental step in a schema integration system, and its quality impacts the overall performance of the system. Recently, a large number of schema matching approaches have been developed. Until today, the performance of schema matching is inherently uncertain and requires improvement. The most difficult task is inferring the realworld semantics of data from the information provided by schema labels in their representations. Usually, schemas with identical semantics are represented by different vocabularies and only their own designers can completely understand. A schema may contain synonyms and homonyms words. Therefore, it is necessary to understand how the schema elements are "presented"; it is often hard to get aware meaning associated with elements names, due to the semantic ambiguity of human language. Semantic ambiguity problem means the capability of being understood in two or more possible senses. Having more than one meaning for an individual schema element would cause confusion in interpretation of schema name. This may affect negatively on the matching result. Therefore, this paper aims to resolve this problem of semantic ambiguity and represent the intended meaning of the schema labels name, by introducing the CKBD (Context Knowledge-Based Disambiguation) approach. The CKBD is obtained by integrating two pieces of context knowledge: semantic domain and more frequency used into a disambiguation processor. Finally, the CKBD is implemented and is tested in a real dataset. The result is deeply grounded in the ability to detect schema name intended meaning.

KW - Natural language processing

KW - Schema integration

KW - Schema matching

KW - Semantic ambiguity

KW - Word sense disambiguation

UR - http://www.scopus.com/inward/record.url?scp=85062412124&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062412124&partnerID=8YFLogxK

U2 - 10.18517/ijaseit.9.1.6387

DO - 10.18517/ijaseit.9.1.6387

M3 - Article

VL - 9

SP - 213

EP - 219

JO - International Journal on Advanced Science, Engineering and Information Technology

JF - International Journal on Advanced Science, Engineering and Information Technology

SN - 2088-5334

IS - 1

ER -