The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a fe...The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a feasible and practical product configuration method is presented in masscustomization. The basic idea of the appoach is to integrate case-based reasoning (CBR) with a con-straint satisfaction problem(CSP). The similarity measure between a crisp and range is also given,which is common in case retrieves. Based on the configuration model, a product platform and customerneeds, case adaptation is carried out with the repair-based algorithm. Lastly, the methodology in theelevator configuration design domain is tested.展开更多
Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be-...Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be- come prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and fi- nally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via log- ical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph sys- tems and discuss the future research directions.展开更多
Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the stat...Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows： （1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.展开更多
By means of randomization, the concept of D-randomized truth degree of formulas in two-valued propositional logic is introduced, and it is proved that the set of values of D-randomized truth degree of formulas has no ...By means of randomization, the concept of D-randomized truth degree of formulas in two-valued propositional logic is introduced, and it is proved that the set of values of D-randomized truth degree of formulas has no isolated point in [0,1]. The concepts of D-logic pseudo-metric and D-logic metric space are also introduced and it is proved that there is no isolated point in the space. The new built D-randomized concepts are extensions of the corresponding concepts in quantified logic. Moreover, it is proved that the basic logic connectives are continuous operators in D-logic metric space. Lastly, three different types of approximate reasoning patterns are proposed.展开更多
Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact repr...Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.展开更多
基金This project is supported by National Natural Science Foundation of China(No.50275133) and China Hi-tech Program CIMS Topic (No.2003-China(No.50275133) and China Hi-tech Program CIMS Topic (No.2003-AA411320). Received July 22, 2003
文摘The increasing complexity and size of configuration knowledge bases requres the provisionof advanced methods supporting the development of the actual configuration process and design reuse.A new framework to find a feasible and practical product configuration method is presented in masscustomization. The basic idea of the appoach is to integrate case-based reasoning (CBR) with a con-straint satisfaction problem(CSP). The similarity measure between a crisp and range is also given,which is common in case retrieves. Based on the configuration model, a product platform and customerneeds, case adaptation is carried out with the repair-based algorithm. Lastly, the methodology in theelevator configuration design domain is tested.
文摘Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be- come prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and fi- nally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via log- ical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph sys- tems and discuss the future research directions.
基金supported by the National Natural Science Foundation of China(Nos.61371128,U1611461,61425025,and 61532005)
文摘Cross-media analysis and reasoning is an active research area in computer science, and a promising direction for artificial intelligence. However, to the best of our knowledge, no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances, challenges, and future directions for the field. To address these issues, we provide an overview as follows： （1） theory and model for cross-media uniform representation; （2） cross-media correlation understanding and deep mining; （3） cross-media knowledge graph construction and learning methodologies; （4） cross-media knowledge evolution and reasoning; （5） cross-media description and generation; （6） cross-media intelligent engines; and （7） cross-media intelligent applications. By presenting approaches, advances, and future directions in cross-media analysis and reasoning, our goal is not only to draw more attention to the state-of-the-art advances in the field, but also to provide technical insights by discussing the challenges and research directions in these areas.
文摘By means of randomization, the concept of D-randomized truth degree of formulas in two-valued propositional logic is introduced, and it is proved that the set of values of D-randomized truth degree of formulas has no isolated point in [0,1]. The concepts of D-logic pseudo-metric and D-logic metric space are also introduced and it is proved that there is no isolated point in the space. The new built D-randomized concepts are extensions of the corresponding concepts in quantified logic. Moreover, it is proved that the basic logic connectives are continuous operators in D-logic metric space. Lastly, three different types of approximate reasoning patterns are proposed.
基金supported by Guangdong Nuclear Power Group of China under Contract No. CNPRI-ST10P005the National Natural Science Foundation of China under Grant No. 60643006
文摘Developed from the dynamic causality diagram （DCD） model, a new approach for knowledge representation and reasoning named as dynamic uncertain causality graph （DUCG） is presented, which focuses on the compact representation of complex uncertain causalities and efficient probabilistie inference. It is pointed out that the existing models of compact representation and inference in Bayesian Network （BN） is applicable in single-valued cases, but may not be suitable to be applied in multi-valued cases. DUCG overcomes this problem and beyond. The main features of DUCG are： 1） compactly and graphically representing complex conditional probability distributions （CPDs）, regardless of whether the cases are single-valued or multi-valued; 2） able to perform exact reasoning in the case of the incomplete knowledge representation; 3） simplifying the graphical knowledge base conditional on observations before other calculations, so that the scale and complexity of problem can be reduced exponentially; 4） the efficient two-step inference algorithm consisting of （a） logic operation to find all possible hypotheses in concern for given observations and （b） the probability calculation for these hypotheses; and 5） much less relying on the parameter accuracy. An alarm system example is provided to illustrate the DUCG methodology.
