Difference between revisions of "Auto-Construction of Domain Ontology based on Wikipedia and Scientific Papers"
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| + | | title = Auto-Construction of Domain Ontology based on Wikipedia and Scientific Papers | ||
| + | | date = 2016 | ||
| + | | authors = [[Lan Huang]]<br />[[Chongliang Sun]]<br />[[Yang Chi]]<br />[[Hao Xu]] | ||
| + | | link = http://www.dpi-journals.com/index.php/JRST/article/view/2944 | ||
| + | }} | ||
'''Auto-Construction of Domain Ontology based on Wikipedia and Scientific Papers''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Lan Huang]], [[Chongliang Sun]], [[Yang Chi]] and [[Hao Xu]]. | '''Auto-Construction of Domain Ontology based on Wikipedia and Scientific Papers''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Lan Huang]], [[Chongliang Sun]], [[Yang Chi]] and [[Hao Xu]]. | ||
== Overview == | == Overview == | ||
Ontology plays an important role in [[information retrieval]], common sense reasoning, knowledge bases construction and so on, while the conventional manual [[ontology]] construction with numerous human cost is hard to maintain. Meanwhile, though many general ontology databases have existed, the domain ontology is rare and proved has lots of challenges to construct. In this paper, an auto-construction of domain ontology tree with terms as its nodes and relationships as its sides is implemented. Reinforcement learning is used to get the depth of ontology and k-means hierarchical clustering to get the related relationships in ontology. Authors experiment shows that the methodology constructing domain ontology automatically for different agents holds better efficiency and accuracy. | Ontology plays an important role in [[information retrieval]], common sense reasoning, knowledge bases construction and so on, while the conventional manual [[ontology]] construction with numerous human cost is hard to maintain. Meanwhile, though many general ontology databases have existed, the domain ontology is rare and proved has lots of challenges to construct. In this paper, an auto-construction of domain ontology tree with terms as its nodes and relationships as its sides is implemented. Reinforcement learning is used to get the depth of ontology and k-means hierarchical clustering to get the related relationships in ontology. Authors experiment shows that the methodology constructing domain ontology automatically for different agents holds better efficiency and accuracy. | ||
Revision as of 08:54, 2 May 2020
Authors | Lan Huang Chongliang Sun Yang Chi Hao Xu |
|---|---|
Publication date | 2016 |
Links | Original |
Auto-Construction of Domain Ontology based on Wikipedia and Scientific Papers - scientific work related to Wikipedia quality published in 2016, written by Lan Huang, Chongliang Sun, Yang Chi and Hao Xu.
Overview
Ontology plays an important role in information retrieval, common sense reasoning, knowledge bases construction and so on, while the conventional manual ontology construction with numerous human cost is hard to maintain. Meanwhile, though many general ontology databases have existed, the domain ontology is rare and proved has lots of challenges to construct. In this paper, an auto-construction of domain ontology tree with terms as its nodes and relationships as its sides is implemented. Reinforcement learning is used to get the depth of ontology and k-means hierarchical clustering to get the related relationships in ontology. Authors experiment shows that the methodology constructing domain ontology automatically for different agents holds better efficiency and accuracy.
