Earticle

초록

영어

Semantic similarity computation is of great importance in many applications such as natural language processing, knowledge acquisition and information retrieval. In recent years, many concept similarity measures have been developed for ontology and lexical taxonomy. Generally speaking, ontology concepts semantic similarity computation is tedious and time-consuming. This paper puts forward an optimization algorithm to simplify semantic similarity computation. The optimization algorithm utilizes hierarchical relationship between concepts to simplify similarity computation process. Simulation experiments showed the optimization algorithm could make similarity computation simple and convenient, and similarity computation speed was improved by one time. The more complexity an ontology structure, and the bigger the maximum depth of ontology, the more significantly the performance improved.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Methods
  3.1 Feasibility Analysis of Similarity Computation Optimization
  3.2 Algorithm Description and Complexity Analysis
  3.3 Further Discussions
 4. Results
 5. Conclusion
 Acknowledgement
 References

키워드

저자

• Zhixiao Wang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China, College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China ]
• Xiaofang Ding [ College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China ]
• Ying Huang [ College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605