Difference between revisions of "Wikipedia Entry Augmentation by Sub-Merging Entities based on Multilingual Ontology"
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| − | '''Wikipedia Entry Augmentation by Sub-Merging Entities based on Multilingual Ontology''' - scientific work related to Wikipedia quality published in 2017, written by Md. Tasnim Manzur Ankon and Muhammad Masroor Ali. | + | '''Wikipedia Entry Augmentation by Sub-Merging Entities based on Multilingual Ontology''' - scientific work related to [[Wikipedia quality]] published in 2017, written by [[Md. Tasnim Manzur Ankon]] and [[Muhammad Masroor Ali]]. |
== Overview == | == Overview == | ||
| − | The amount of knowledge in the world wide web is increasing with the frequent addition of new features, like adaptation of multiple languages. Immense amount of content makes its utilization quite challenging for intelligent machines. Hence, structured formats or ontologies became significant. Ontologies require efficient extraction techniques, a topic that still needs much work. A way to improve the extraction technique is by concentrating multiple data sources into one single source. This paper introduces a way that can sub-merge knowledge repositories from multiple languages into a richer and easily accessible one. Using the knowledge base from English and French languages, machine-readable properties of individual entities are filtered through intelligent techniques and a precise knowledge source is generated. The results obtained by experimental implementation of the sub-merging technique are also presented in this paper to demonstrate the magnitude of enhancement. | + | The amount of knowledge in the world wide web is increasing with the frequent addition of new [[features]], like adaptation of [[multiple languages]]. Immense amount of content makes its utilization quite challenging for intelligent machines. Hence, structured formats or ontologies became significant. Ontologies require efficient extraction techniques, a topic that still needs much work. A way to improve the extraction technique is by concentrating multiple data sources into one single source. This paper introduces a way that can sub-merge knowledge repositories from multiple languages into a richer and easily accessible one. Using the knowledge base from English and French languages, machine-readable properties of individual entities are filtered through intelligent techniques and a precise knowledge source is generated. The results obtained by experimental implementation of the sub-merging technique are also presented in this paper to demonstrate the magnitude of enhancement. |
Revision as of 11:36, 14 June 2020
Wikipedia Entry Augmentation by Sub-Merging Entities based on Multilingual Ontology - scientific work related to Wikipedia quality published in 2017, written by Md. Tasnim Manzur Ankon and Muhammad Masroor Ali.
Overview
The amount of knowledge in the world wide web is increasing with the frequent addition of new features, like adaptation of multiple languages. Immense amount of content makes its utilization quite challenging for intelligent machines. Hence, structured formats or ontologies became significant. Ontologies require efficient extraction techniques, a topic that still needs much work. A way to improve the extraction technique is by concentrating multiple data sources into one single source. This paper introduces a way that can sub-merge knowledge repositories from multiple languages into a richer and easily accessible one. Using the knowledge base from English and French languages, machine-readable properties of individual entities are filtered through intelligent techniques and a precise knowledge source is generated. The results obtained by experimental implementation of the sub-merging technique are also presented in this paper to demonstrate the magnitude of enhancement.
