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

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

HOU Jiajia, LI Dongmei, QIU Chengjing, et al., “A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 460-466, 2016, doi: 10.1049/cje.2016.05.011

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HOU Jiajia, LI Dongmei, QIU Chengjing, et al., “A Semantic Retrieval Model Based on Domain Ontology of Orchard Disease and Pests,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 460-466, 2016, doi: 10.1049/cje.2016.05.011

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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

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References(18)

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