<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This article summarizes some research results related to ontology enrichment specific to tourism domains from 2014 to 2017. Currently, some ontology enrichment approaches can use learning machinery such as support vector machine (SVM), Conditional Random Field (CRF) and kNN. Several studies have also been successful in evaluating ontology enrichment results with several parameters such as precision, recall and F-Measure. In addition, our research can enrich Dwipa Ontology II which has been successfully done by population to object / sample. The method used in this research is ontology enrichment method. This technique or method is used to show background knowledge (ontology) by adding new concepts and relationships through the extraction process. The enrichment process is done on semi-automated web document (corpus). The process using statistics and linguistics, by applying evaluation techniques by using reviewers in the field of tourism. The end result of Dwipa Ontology III (enriched ontology) contains 4 main classes, 15 subclasses and 199 samples / objects. The expansion of general concept / subclass knowledge under the attraction classes are: cultural parks, artists and monuments.</p>
</abstract>

This article summarizes some research results related to ontology enrichment specific to tourism domains from 2014 to 2017. Currently, some ontology enrichment approaches can use learning machinery such as support vector machine (SVM), Conditional Random Field (CRF) and kNN. Several studies have also been successful in evaluating ontology enrichment results with several parameters such as precision, recall and F-Measure. In addition, our research can enrich Dwipa Ontology II which has been successfully done by population to object / sample. The method used in this research is ontology enrichment method. This technique or method is used to show background knowledge (ontology) by adding new concepts and relationships through the extraction process. The enrichment process is done on semi-automated web document (corpus). The process using statistics and linguistics, by applying evaluation techniques by using reviewers in the field of tourism. The end result of Dwipa Ontology III (enriched ontology) contains 4 main classes, 15 subclasses and 199 samples / objects. The expansion of general concept / subclass knowledge under the attraction classes are: cultural parks, artists and monuments.

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This article summarizes some research results related to ontology enrichment specific to tourism domains from 2014 to 2017. Currently, some ontology enrichment...

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

Published Online: Dec 01, 2017

Page range: 1 - 17

Received: Aug 05, 2017

Accepted: Nov 12, 2017

DOI: https://doi.org/10.21307/ijssis-2018-024

Keywords
Ontology Enrichment, machine learning, statistics, linguistics, tourism domain, semi-automatic, Ontology Dwipa III

© 2017 Guson Prasamuarso Kuntarto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

Published Online: Dec 01, 2017

Page range: 1 - 17

Received: Aug 05, 2017

Accepted: Nov 12, 2017

DOI: https://doi.org/10.21307/ijssis-2018-024

KeywordsOntology Enrichment, machine learning, statistics, linguistics, tourism domain, semi-automatic, Ontology Dwipa III

© 2017 Guson Prasamuarso Kuntarto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Ontology Enrichment, machine learning, statistics, linguistics, tourism domain, semi-automatic, Ontology Dwipa III