Earticle

초록

영어

In the collaborative evolution of biomedical ontology, participants are a sizeable, balanced mix of scholars and physicians, all of whom are experienced in the biomedical domain and who represent diverse viewpoints, experiences, and backgrounds. In the field of collaborative evolution of biomedical ontology on large-scale ontology, there exists inevitable conflicts, which may cause the inconsistent ontology. In this paper, a new method to detect conflicts in ontology evolution is presented, which classifies conflicts as three groups: internal inconsistencies conflicts in change sequence, direct conflicts between the sequences and Inconsistent conflict between the sequences. For different conflict, high effective detecting algorithms are presented with evaluation. Before the conflicts detecting, semantic extended rules are employed to depict the evolution requirements of the participants. In particular, we discuss the situation where maximum consistent changing subsequence is needed if there are inconsistent conflicts between changing subsequences. We also show how detecting algorithms could be taken in the collaborative evolution of medicine ontology. As a result, internal and mutual inconsistencies can be detected from change sequences. And if there are conflicts between the sequences, the algorithm will provide the maximum consistent changing subsequence as the evolution basis. The designed experiments verify our approach and achieve the expected results.

목차

Abstract
 1. Introduction
 2. Formal Description of Ontology Change
 3. Collaborative Evolution Conflicts Detection
 4. Similarity Calculation
 5. Time Complexity Analyzing
 6. Experimental Design
 7. Summary and Future Works
 Acknowledgements
 References

키워드

저자

• Song Yingjie [ Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), 264005 Yantai, China, School of Computer Science and Technology, Shandong Institute of Business and Technology , 264005 Yantai, China, Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, 643000 Zigong, China ]
• Zhang Bin [ Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), 264005 Yantai, China, School of Computer Science and Technology, Shandong Institute of Business and Technology , 264005 Yantai, China ]
• Mao Yanyan [ Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), 264005 Yantai, China, School of Computer Science and Technology, Shandong Institute of Business and Technology , 264005 Yantai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605