IEICE TRANSACTIONS on Information
A Knowledge Representation Based User-Driven Ontology Summarization Method
Summary :
As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.9 pp.1870-1873
- Publication Date
- 2019/09/01
- Publicized
- 2019/05/30
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8069
- Type of Manuscript
- LETTER
- Category
- Data Engineering, Web Information Systems
Authors
Yuehang DING
Information Engineering University
Hongtao YU
Information Engineering University
Jianpeng ZHANG
Information Engineering University
Huanruo LI
Information Engineering University
Yunjie GU
Information Engineering University
Keyword
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Yuehang DING, Hongtao YU, Jianpeng ZHANG, Huanruo LI, Yunjie GU, "A Knowledge Representation Based User-Driven Ontology Summarization Method" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1870-1873, September 2019, doi: 10.1587/transinf.2019EDL8069.
Abstract: As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8069/_p
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@ARTICLE{e102-d_9_1870,
author={Yuehang DING, Hongtao YU, Jianpeng ZHANG, Huanruo LI, Yunjie GU, },
journal={IEICE TRANSACTIONS on Information},
title={A Knowledge Representation Based User-Driven Ontology Summarization Method},
year={2019},
volume={E102-D},
number={9},
pages={1870-1873},
abstract={As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.},
keywords={},
doi={10.1587/transinf.2019EDL8069},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - A Knowledge Representation Based User-Driven Ontology Summarization Method
T2 - IEICE TRANSACTIONS on Information
SP - 1870
EP - 1873
AU - Yuehang DING
AU - Hongtao YU
AU - Jianpeng ZHANG
AU - Huanruo LI
AU - Yunjie GU
PY - 2019
DO - 10.1587/transinf.2019EDL8069
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.
ER -
English
