Abstract
Today, the web is the huge data repository which contains excessively growing with uncountable size of data. From the view point of data, Semantic Web is the advanced version of World Wide Web, which aims machine understandable web based on the structured data. For the advent of Semantic Web, its data has been rapidly increased with various areas. In this paper, we proposed novel decision tree algorithm, which called Semantic Decision Tree, to learning the covered knowledge beyond the Semantic Web based ontology. For this purpose, we newly defined six different refinements based on the description logic constructors. Refinements are replaced the features of traditional decision tree algorithms, and these refinements are automatically searched by our proposed decision tree algorithm based on the structure information of ontology. Additional information from the ontology is also used to enhance the quality of decision tree results. Finally, we test our algorithm by solving the famous rule induction problems, and we can get perfect answers with useful decision tree results. In addition, we expect that our proposed algorithm has strong advantage to learn decision tree algorithm on complex and huge size of ontology.
| Original language | English |
|---|---|
| Title of host publication | Semantic Technology - Third Joint International Conference, JIST 2013, Revised Selected Papers |
| Publisher | Springer Verlag |
| Pages | 414-428 |
| Number of pages | 15 |
| ISBN (Print) | 9783319068251 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 3rd Joint International Semantic Technology Conference, JIST 2013 - Seoul, Korea, Republic of Duration: 2013 Nov 28 → 2013 Nov 30 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8388 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 3rd Joint International Semantic Technology Conference, JIST 2013 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 13/11/28 → 13/11/30 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Concept learning algorithm for semantic web based on the automatically searched refinement condition. / Jeon, Dongkyu; Kim, Wooju.
Semantic Technology - Third Joint International Conference, JIST 2013, Revised Selected Papers. Springer Verlag, 2014. p. 414-428 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8388 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Concept learning algorithm for semantic web based on the automatically searched refinement condition
AU - Jeon, Dongkyu
AU - Kim, Wooju
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Today, the web is the huge data repository which contains excessively growing with uncountable size of data. From the view point of data, Semantic Web is the advanced version of World Wide Web, which aims machine understandable web based on the structured data. For the advent of Semantic Web, its data has been rapidly increased with various areas. In this paper, we proposed novel decision tree algorithm, which called Semantic Decision Tree, to learning the covered knowledge beyond the Semantic Web based ontology. For this purpose, we newly defined six different refinements based on the description logic constructors. Refinements are replaced the features of traditional decision tree algorithms, and these refinements are automatically searched by our proposed decision tree algorithm based on the structure information of ontology. Additional information from the ontology is also used to enhance the quality of decision tree results. Finally, we test our algorithm by solving the famous rule induction problems, and we can get perfect answers with useful decision tree results. In addition, we expect that our proposed algorithm has strong advantage to learn decision tree algorithm on complex and huge size of ontology.
AB - Today, the web is the huge data repository which contains excessively growing with uncountable size of data. From the view point of data, Semantic Web is the advanced version of World Wide Web, which aims machine understandable web based on the structured data. For the advent of Semantic Web, its data has been rapidly increased with various areas. In this paper, we proposed novel decision tree algorithm, which called Semantic Decision Tree, to learning the covered knowledge beyond the Semantic Web based ontology. For this purpose, we newly defined six different refinements based on the description logic constructors. Refinements are replaced the features of traditional decision tree algorithms, and these refinements are automatically searched by our proposed decision tree algorithm based on the structure information of ontology. Additional information from the ontology is also used to enhance the quality of decision tree results. Finally, we test our algorithm by solving the famous rule induction problems, and we can get perfect answers with useful decision tree results. In addition, we expect that our proposed algorithm has strong advantage to learn decision tree algorithm on complex and huge size of ontology.
UR - http://www.scopus.com/inward/record.url?scp=84902601874&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902601874&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-06826-8_30
DO - 10.1007/978-3-319-06826-8_30
M3 - Conference contribution
AN - SCOPUS:84902601874
SN - 9783319068251
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 414
EP - 428
BT - Semantic Technology - Third Joint International Conference, JIST 2013, Revised Selected Papers
PB - Springer Verlag
ER -