| 摘要: |
| 语义Web模糊知识的表示和应用常常涉及模糊隶属度比较,但现有描述逻辑的模糊扩展缺乏描述模糊隶属度比较的能力.提出支持模糊隶属度比较和描述逻辑ALCN(attributive concept description language with complements and number restriction)概念构造子的扩展模糊描述逻辑FCALCN(fuzzy comparable ALCN).FCALCN引入新的原子概念形式以支持模糊隶属度比较.给出FCALCN的推理算法,证明了在空TBox约束下FCALCN的推理问题复杂性是多项式空间完全的.FCALCN能够表达语义Web上涉及模糊隶属度比较的复杂模糊知识并实现对它们的推理. |
| 关键词: 语义Web 知识表示 描述逻辑 模糊 比较 推理 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60425206, 60633010, 60403016, 60503033 (国家自然科学基金); the National Basic Research Program of China under Grant No.2002CB312000 (国家重点基础研究发展计划(973)); the National Research Foundation for the Doctoral Program of Higher Education of China under Grant No.20060286020 (高等学校博士学科点专项科研基金); the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2005060 (江苏省自然科学基金); the High Technology Research Project of Jiangsu Province of China under Grant No.BG2005032 (江苏省高技术研究项目) |
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| Extended Fuzzy Description Logics with Comparisons Between Fuzzy Membership Degrees |
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KANG Da-Zhou,XU Bao-Wen,LU Jian-Jiang,LI Yan-Hui
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| Abstract: |
| The representation and application of fuzzy knowledge on the semantic Web often relate to the comparisons between fuzzy membership degrees. However, the current fuzzy extensions of description logics do not support the expression of such comparisons. This paper proposes an extended fuzzy description logic that supports the comparisons of fuzzy membership degrees and the concept constructors from description logic ALCN (attributive concept description language with complements and number restriction), written FCALCN (fuzzy comparable ALCN). FCALCN introduces new forms of atom concepts in order to support the comparisons between fuzzy membership degrees. A reasoning algorithm for FCALCN is proposed, and the complexity of the reasoning problems of FCALCN with empty TBox is proved to be PSpace-complete. FCALCN can represent expressive fuzzy knowledge involving the comparisons of fuzzy membership degrees on the semantic Web and enable reasoning of them. |
| Key words: semantic Web knowledge representation description logic fuzzy comparison reasoning |
