Abstract
In this work we deal with the problem of TBox learning from incomplete semantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to obtain. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies - Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+ and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.
| Original language | English |
|---|---|
| Pages (from-to) | 30-40 |
| Number of pages | 11 |
| Journal | Knowledge-Based Systems |
| Volume | 75 |
| Issue number | C |
| Early online date | 18 Nov 2014 |
| DOIs | |
| Publication status | Published - 1 Feb 2015 |
Fingerprint
Keywords
- Evaluation framework
- Ontology learning
- Probabilistic description logics
- Semantic web
- TBox learning
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Management Information Systems
- Information Systems and Management
Cite this
TBox learning from incomplete data by inference in BelNet+. / Zhu, Man; Gao, Zhiqiang; Pan, Jeff Z.; Zhao, Yuting; Xu, Ying; Quan, Zhibin.
In: Knowledge-Based Systems, Vol. 75, No. C, 01.02.2015, p. 30-40.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - TBox learning from incomplete data by inference in BelNet+
AU - Zhu, Man
AU - Gao, Zhiqiang
AU - Pan, Jeff Z.
AU - Zhao, Yuting
AU - Xu, Ying
AU - Quan, Zhibin
N1 - Acknowledgements This work is partially funded by the National Science Foundation of China under Grant 61170165, the EU IAPP K-Drive project (286348) and the EPSRC WhatIf project (EP/J014354/1). The authors would like to thank Campbell Wilson for proof-reading the document.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - In this work we deal with the problem of TBox learning from incomplete semantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to obtain. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies - Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+ and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.
AB - In this work we deal with the problem of TBox learning from incomplete semantic web data. TBox, or conceptual schema, is the backbone of a Description Logic (DL) ontology, but is always difficult to obtain. Existing approaches either fail in getting correct results under incompleteness or learn results that are not enough to resolve the incompleteness. We propose to transform TBox learning in DL into inference in the extension of Bayesian Description Logic Network (abbreviated as BelNet+), whereby the structure in the data is leveraged when evaluating the relationships between two concepts. BelNet+, integrating the probabilistic inference capability of Bayesian Networks with the logical formalism of DL ontologies - Description Logics, supports promising inference. In this paper, we firstly explain the details of BelNet+ and introduce a TBox learning approach based on BelNet+. In order to overcome the drawbacks of current evaluation metrics, we then propose a novel evaluation framework conforming to the Open World Assumption (OWA) generally made in the semantic web. Finally the results from empirical studies on comparisons with the state-of-the-art TBox learners verify the effectiveness of our approach.
KW - Evaluation framework
KW - Ontology learning
KW - Probabilistic description logics
KW - Semantic web
KW - TBox learning
UR - http://www.scopus.com/inward/record.url?scp=84920538184&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2014.11.004
DO - 10.1016/j.knosys.2014.11.004
M3 - Article
AN - SCOPUS:84920538184
VL - 75
SP - 30
EP - 40
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
SN - 0950-7051
IS - C
ER -