Fuzzy description logics are considered as the logical infrastructure of fuzzy knowledge representation on the semantic Web. To deal with fuzzy and dynamic knowledge on the semantic Web and its applications, a new fuz...Fuzzy description logics are considered as the logical infrastructure of fuzzy knowledge representation on the semantic Web. To deal with fuzzy and dynamic knowledge on the semantic Web and its applications, a new fuzzy extension of Attribute Language with Complement based on dynamic fuzzy logic called the dynamic fuzzy description logic （DFALC） is presented. The syntax and semantics of DFALC are formally defined, and the forms of axioms and assertions are specified. The DFALC provides more reasonable logic foundation for the semantic Web, and overcomes the insufficiency of using fuzzy description logic FALC to act as logical foundation for the semantic Web. The extended DFALC is more expressive than the existing fuzzy description logics and present more fuzzy information on the semantic Web.展开更多
Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web...Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty in the real world, it is essential to extend DLs to deal with these vague and imprecise information. We thus propose a new fuzzy DL f-DLR-Lite.n, which allows for the presence of n-ary relations and the occurrence of concept conjunction on the left land of inclusion axioms. We also suggest an improved fuzzy query language, which supports the presence of thresholds and user defined weights. We also show that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. DL f-DLR-Lite,n can make up for the disadvantages of knowledge representation and reasoning of classic DLs, and the enhanced query language expresses user intentions more precisely and reasonably.展开更多
The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to mode...The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language （UML） and building domain ontologies with fuzzy description logic （DL）. The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.展开更多
Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivatio...Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivation for this work. In this paper, we present a type-2 fuzzy attributive concept language with complements （ALC） and provide its knowledge representation and reasoning algorithms. We also propose type-2 fuzzy web ontology language （OWL） to build a fuzzy ontology based on type- 2 fuzzy ALC and analyze the soundness, completeness, and complexity of the reasoning algorithms. Compared to type-1 fuzzy ALC, type-2 fuzzy ALC can describe imprecise knowledge more meticulously by using the membership degree interval. We implement a semantic search engine based on type-2 fuzzy ALC and carry out experiments on real data to test its performance. The results show that the type-2 fuzzy ALC can improve the precision and increase the number of relevant hits for imprecise information searches.展开更多
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 restrict...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.展开更多
扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and...扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and transitive roles)的受限TBox(terminological box)描述术语公理,给出受限TBox约束下的EFALCR+推理算法,并对该算法进行优化,证明优化后的算法是正确完备的,时间复杂性不超过指数,最后证明受限TBox约束下的EFALCR+推理问题是指数时间完全问题.优化算法的最坏时间复杂性已达到该问题推理算法的复杂度下界,是实现术语公理约束下模糊知识库推理的有效算法.展开更多
基金the National Natural Science Foundation of China (60673092)Key Project of Ministry of Education of China (205059)+2 种基金the 2006 Jiangsu Sixth Talented-Personnel Research Program (06-E-037)The Project of Jiangsu Key Laboratory of Computer Information Processing Technologythe Higher Education Graduate Research Innovation Program of Jiangsu Province
文摘Fuzzy description logics are considered as the logical infrastructure of fuzzy knowledge representation on the semantic Web. To deal with fuzzy and dynamic knowledge on the semantic Web and its applications, a new fuzzy extension of Attribute Language with Complement based on dynamic fuzzy logic called the dynamic fuzzy description logic （DFALC） is presented. The syntax and semantics of DFALC are formally defined, and the forms of axioms and assertions are specified. The DFALC provides more reasonable logic foundation for the semantic Web, and overcomes the insufficiency of using fuzzy description logic FALC to act as logical foundation for the semantic Web. The extended DFALC is more expressive than the existing fuzzy description logics and present more fuzzy information on the semantic Web.
基金the Program for New Century Excellent Talents in University (NCET-05-0288)the Specialized Research Fund for the Doctoral Program of Higher Education of China (20050145024)
文摘Description logics （DLs） play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty in the real world, it is essential to extend DLs to deal with these vague and imprecise information. We thus propose a new fuzzy DL f-DLR-Lite.n, which allows for the presence of n-ary relations and the occurrence of concept conjunction on the left land of inclusion axioms. We also suggest an improved fuzzy query language, which supports the presence of thresholds and user defined weights. We also show that the query answering algorithm over the extended DL is still FOL reducible and shows polynomial data complexity. DL f-DLR-Lite,n can make up for the disadvantages of knowledge representation and reasoning of classic DLs, and the enhanced query language expresses user intentions more precisely and reasonably.
文摘The capability requirements of the command, control, communication, computing, intelligence, surveillance, reconnaissance （C41SR） systems are full of uncertain and vague information, which makes it difficult to model the C41SR architecture. The paper presents an approach to modeling the capability requirements with the fuzzy unified modeling language （UML） and building domain ontologies with fuzzy description logic （DL）. The UML modeling constructs are extended according to the meta model of Depart- ment of Defense Architecture Framework to improve their domain applicability, the fuzzy modeling mechanism is introduced to model the fuzzy efficiency features of capabilities, and the capability requirement models are converted into ontologies formalized in fuzzy DL so that the model consistency and reasonability can be checked with a DL reasoning system. Finally, a case study of C41SR capability requirements model checking is provided to demonstrate the availability and applicability of the method.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China （Grant Nos. 60873225, 60873083, and 70771043）, the National High Technology Research and Development Program of China （2007AA01Z403）, the Natural Science Foundation of Hubei Province （2009CDB298）, the Natural Science Foundation of Hubei Province for Distinguished Young Scholars （2008CDB351）, the Wuhan Youth Science and Technology Chenguang Program （200950431171）, the Open Foundation of State Key Laboratory of Software Engineering （SKLSE20080718）, the Innovation Fund of Huazhong University of Science and Technology （2010MS068, Q2009021）.
文摘Description logics （DLs） are widely employed in recent semantic web application systems. However, classical description logics are limited when dealing with imprecise concepts and roles, thus providing the motivation for this work. In this paper, we present a type-2 fuzzy attributive concept language with complements （ALC） and provide its knowledge representation and reasoning algorithms. We also propose type-2 fuzzy web ontology language （OWL） to build a fuzzy ontology based on type- 2 fuzzy ALC and analyze the soundness, completeness, and complexity of the reasoning algorithms. Compared to type-1 fuzzy ALC, type-2 fuzzy ALC can describe imprecise knowledge more meticulously by using the membership degree interval. We implement a semantic search engine based on type-2 fuzzy ALC and carry out experiments on real data to test its performance. The results show that the type-2 fuzzy ALC can improve the precision and increase the number of relevant hits for imprecise information searches.
基金the National Natural Science Foundation of China under Grant No.60663001 60573010( 国家自然科学基金)the Young Science Foundation of Guangxi Province of China under Grant No.GUIKEQING-0640030(广西青年科学基金).
文摘语义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.
基金The National Natural Science Foundation of China(No.60373066,60425206,90412003),the National Basic Research Pro-gram of China (973Program)(No.2002CB312000),the Innovation Plan for Jiangsu High School Graduate Student, the High TechnologyResearch Project of Jiangsu Province (No.BG2005032), and the Weap-onry Equipment Foundation of PLA Equipment Ministry ( No.51406020105JB8103).
文摘扩展模糊描述逻辑是对描述逻辑的一种模糊扩展,支持对复杂模糊知识的表示和推理,但该逻辑缺乏支持术语公理约束的推理算法.提出扩展模糊描述逻辑EFALCR+(extended fuzzy attributive concept description language with complements and transitive roles)的受限TBox(terminological box)描述术语公理,给出受限TBox约束下的EFALCR+推理算法,并对该算法进行优化,证明优化后的算法是正确完备的,时间复杂性不超过指数,最后证明受限TBox约束下的EFALCR+推理问题是指数时间完全问题.优化算法的最坏时间复杂性已达到该问题推理算法的复杂度下界,是实现术语公理约束下模糊知识库推理的有效算法.
基金Supported by the National Natural Science Foundation of China under Grant Nos.60373066, 60425206, 90412003 (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 Jiangsu High-Tech Research Project of China under Grant No.20020286004 (高等学校博士学科点专项科研基金)
基金Supported by the National Natural Science Foundation of China under Grant(60663001)the Natural Science Foundation of GuangdongProvince of China under Grant(10151063101000031)~~
