A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests

HOU Jiajia; LI Dongmei; QIU Chengjing; HAN Hui

doi:10.1049/cje.2016.05.011

Volume 25 Issue 3

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2016 > 25(3): 460-466

HOU Jiajia, LI Dongmei, QIU Chengjing, HAN Hui. A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests[J]. Chinese Journal of Electronics, 2016, 25(3): 460-466. doi: 10.1049/cje.2016.05.011

Citation:

HOU Jiajia, LI Dongmei, QIU Chengjing, HAN Hui. A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests[J]. Chinese Journal of Electronics, 2016, 25(3): 460-466. doi: 10.1049/cje.2016.05.011

Citation:

PDF( 367 KB)

A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests

doi: 10.1049/cje.2016.05.011

1.
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China;
2.
School of Information, Renmin University of China, Beijing 100872, China;
3.
School of Computer Science and Information Technology, Northeast Normal University, Changchun 130000, China

Funds: This work is supported by the Fundamental Research Funds for the Central Universities (No.TD2014-02, No.YX2014-19).

More Information

Corresponding author: LI Dongmei received the Ph.D. degree from Beijing Jiaotong University in 2014. She is an associate professor in School of Information and technology at Beijing Forestry University. Her research interests include artificial intelligent, knowledge engineering and semantic Web. (Email: lidongmei@bjfu.edu.cn)
Received Date: 2014-12-17
Rev Recd Date: 2015-01-28
Publish Date: 2016-05-10

Abstract

Abstract

Ontology-based semantic retrieval can improve the efficiency of information retrieval. This paper proposes a semantic retrieval model based on domain ontology of orchard disease and pests. According to Forestry Thesaurus, we semi-automatically construct a domain ontology and repair ontology inconsistency to ensure the accuracy and uniqueness of the domain knowledge. A concept similarity algorithm is proposed and applied to calculate sentence similarity. We present a synthetic sentence similarity algorithm, which is a combination of the traditional sentence similarity algorithm and the weighted sentence similarity algorithm. Compared with other related methods through experiments, our retrieval model has higher accuracy in semantic retrieval.
- Orchard pests and diseases,
- Thesaurus,
- Ontology,
- Inconsistency detection,
- Concept similarity,
- Sentence similarity

FullText(HTML)

References(18)

References

J. Wu, Z.H. Wu, Y. Li and S.G. Deng, "Web service discovery based on ontology and similarity of words", Chinese Journal of Computers, Vol.28, No.4, pp.595-602, 2005.

D.X. Cao, Z.J. Li and R. Karthik, "Ontology-based customer preference modeling for concept generation", Advanced Engineering Informatics, Vol.25, No.2, pp.162-176, 2011.

S. Zammali, K. Arour and A. Bouzeghoub, "Using ontologies to build testbed for peer-to-peer information retrieval systems", Proc. of the 27th IEEE International Conference on Advanced Information Networking and Applications (AINA), Barcelona, Spain, pp.1033-1040, 2013.

S.T. Cheng, C.L. Chou and G.J. Horng, "The adaptive ontology-based personalized recommender system", Wireless Personal Communications, Vol.72, No.4, pp.1801-1826, 2011.

D.M. Li, J.J. Hou, et al., "A computation study on semantics based weighted sentence similarity", Journal of Chemical and Pharmaceutical Research, Vol.5, No.8, pp.225-231, 2013.

C. Willis and R.M. Losee, "A random walk on an ontology: Using thesaurus structure for automatic subject indexing", Journal of the American Society for Information Science and Technology, Vol.64, No.7, pp.1330-1344, 2013.

J.F. Du, G.L. Qi, J.Z. Pan, et al., "A decomposition-based approach to OWL DL ontology diagnosis", Proc. of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Florida, USA, pp.659-664, 2011.

Z.S. Huang and F.V. Harmelen, "Using semantic distances for reasoning with inconsistent ontologies", Proc. of the 7th International Semantic Web Conference, Karlsruhe, Germany, pp.178-194, 2008.

D.M. Li, Y.F. Lin, H.K. Huang and X. Tian, "Measuring ontology inconsistency based on Dempster-Shafer theory", Journal of Computer Research and Development, Vol.50, No.3, pp.559- 567, 2013. (in Chinese)

D.M. Li, Y.F. Lin, H.K. Huang, S.D. Hao and J.X. Wang, "Dempster-Shafer inconsistency values", Chinese Journal of Electronics, Vol.23, No.2, pp.227-231, 2014.

J. Wang, E.H. Chen, D.M. Shi and Z.Y. Zhang, "Semantic similarity-based information retrieval method", PR & AI, Vol.19, No.6, pp.696-701, 2006. (in Chinese)

Y.H. Yang, J.P. Du and B.W. He, "A novel ontology-based semantic retrieval model for food safety domain", Chinese Journal of Electronics, Vol.22, No,2, pp.247-252, 2013.

M. Strube and S.P. Ponzetto, "WikiRelate! Computing semantic relatedness using Wikipedia", Proc. of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, USA, pp.1419-1424, 2006.

E. Gabrilovich and S. Markovitch, "Computing semantic relatedness using Wikipedia-based explicit semantic analysis", IJCAI, Vol.7, No.1, pp.1606-1611, 2007.

M.V. Assem, M.R. Menken, G. Schreiber, J. Wielemaker and B. Wielinga, "A method for converting thesauri to RDF/OWL", Proc. of the Third International Semantic Web Conference, Hiroshima, Japan, pp.17-31, 2004.

J.X. Huang, J.A. Shin and K.S. Choi, "Enriching core ontology with domain thesaurus through concept and relation classification", Proc. of the sixth International Semantic Web Conference, Busan, South-Korea, pp.1-10, 2007.

D. Sánchez, M. Batet, D. Isern, et al., "Ontology-based semantic similarity: A new feature-based approach", Expert Systems with Applications, Vol.39, No.9, pp.7718-7728, 2012.

Q. Liu and S.J. Li, "Word similarity computing based on Hownet", Proc. of the 3rd Chinese Lexical Semantic Workshop, Taipei, 2003. (in Chinese)

Relative Articles

Supplements(0)

Cited By

Proportional views