OntoPop: An Ontology Population System for the Semantic Web

Theerayut THONGKRAU; Pattarachai LALITROJWONG

doi:10.1587/transinf.E95.D.921

OntoPop: An Ontology Population System for the Semantic Web

Theerayut THONGKRAU, Pattarachai LALITROJWONG

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The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

Publication: IEICE TRANSACTIONS on Information Vol.E95-D No.4 pp.921-931

Publication Date: 2012/04/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E95.D.921

Type of Manuscript: Special Section PAPER (Special Section on Knowledge-Based Software Engineering)

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Theerayut THONGKRAU, Pattarachai LALITROJWONG, "OntoPop: An Ontology Population System for the Semantic Web" in IEICE TRANSACTIONS on Information, vol. E95-D, no. 4, pp. 921-931, April 2012, doi: 10.1587/transinf.E95.D.921.
Abstract: The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.921/_p

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@ARTICLE{e95-d_4_921,
author={Theerayut THONGKRAU, Pattarachai LALITROJWONG, },
journal={IEICE TRANSACTIONS on Information},
title={OntoPop: An Ontology Population System for the Semantic Web},
year={2012},
volume={E95-D},
number={4},
pages={921-931},
abstract={The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.},
keywords={},
doi={10.1587/transinf.E95.D.921},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - OntoPop: An Ontology Population System for the Semantic Web
T2 - IEICE TRANSACTIONS on Information
SP - 921
EP - 931
AU - Theerayut THONGKRAU
AU - Pattarachai LALITROJWONG
PY - 2012
DO - 10.1587/transinf.E95.D.921
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2012
AB - The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.
ER -