To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of to...To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.展开更多
For an extract description of threads information in question and answer （QnA） web forums, it is proposed to construct a QnA knowledge presentation model in the English language, and then an entire solution for the ...For an extract description of threads information in question and answer （QnA） web forums, it is proposed to construct a QnA knowledge presentation model in the English language, and then an entire solution for the QnA knowledge system is presented, including data gathering, platform building and applications design. With pre-defined dictionary and grammatical analysis, the model draws semantic information, grammatical information and knowledge confidence into IR methods, in the form of statement sets and term sets with semantic links. Theoretical analysis shows that the statement model can provide an exact presentation for QnA knowledge, breaking through any limits from original QnA patterns and being adaptable to various query demands; the semantic links between terms can assist the statement model, in terms of deducing new from existing knowledge. The model makes use of both information retrieval （IR） and natural language processing （NLP） features, strengthening the knowledge presentation ability. Many knowledge-based applications built upon this model can be improved, providing better performance.展开更多
Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic netw...Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.展开更多
Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has becom...Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.展开更多
基金The National High Technology Research and Devel-opment Program of China (863Program) (No2003AA1Z2560,2002AA414060)the Key Science and Technology Program of Shaanxi Province (No2006K04-G10)
文摘To deal with a lack of semantic interoperability of traditional knowledge retrieval approaches, a semantic-based networked manufacturing （NM） knowledge retrieval architecture is proposed, which offers a series of tools for supporting the sharing of knowledge and promoting NM collaboration. A 5-tuple based semantic information retrieval model is proposed, which includes the interoperation on the semantic layer, and a test process is given for this model. The recall ratio and the precision ratio of manufacturing knowledge retrieval are proved to be greatly improved by evaluation. Thus, a practical and reliable approach based on the semantic web is provided for solving the correlated concrete problems in regional networked manufacturing.
基金Microsoft Research Asia Internet Services in Aca-demic Research Fund (NoFY07-RES-OPP-116)Tianjin Technological Development Program Project (No06YFGZGX05900)
文摘For an extract description of threads information in question and answer （QnA） web forums, it is proposed to construct a QnA knowledge presentation model in the English language, and then an entire solution for the QnA knowledge system is presented, including data gathering, platform building and applications design. With pre-defined dictionary and grammatical analysis, the model draws semantic information, grammatical information and knowledge confidence into IR methods, in the form of statement sets and term sets with semantic links. Theoretical analysis shows that the statement model can provide an exact presentation for QnA knowledge, breaking through any limits from original QnA patterns and being adaptable to various query demands; the semantic links between terms can assist the statement model, in terms of deducing new from existing knowledge. The model makes use of both information retrieval （IR） and natural language processing （NLP） features, strengthening the knowledge presentation ability. Many knowledge-based applications built upon this model can be improved, providing better performance.
文摘Abstract： It was discussed that the way to reflect the internal relations between judgment and identification, the two most fundamental ways of thinking or cognition operations, during the course of the semantic network knowledge representation processing. A new extended Petri net is defined based on qualitative mapping, which strengths the expressive ability of the feature of thinking and the mode of action of brain. A model of semantic network knowledge representation based on new Petri net is given. Semantic network knowledge has a more efficient representation and reasoning mechanism. This model not only can reflect the characteristics of associative memory in semantic network knowledge representation, but also can use Petri net to express the criterion changes and its change law of recognition judgment, especially the cognitive operation of thinking based on extraction and integration of sensory characteristics to well express the thinking transition course from quantitative change to qualitative change of human cognition.
基金supported by the National Basic Research Program(973)of China(No.2015CB352302)the National Natural Science Foundation of China(Nos.61625107,U1611461,U1509206,and 61402403)+2 种基金the Key Program of Zhejiang Province,China(No.2015C01027)the Chinese Knowledge Center for Engineering Sciences and Technologythe Fundamental Research Funds for the Central Universities,China
文摘Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language.How to efficiently identify the exact answer with respect to a given question has become an active line of research.Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer.Most of these models suffer when a question contains very little content that is indicative of the answer.In this paper,we devise an architecture named the temporality-enhanced knowledge memory network(TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl.Unlike most of the existing approaches,our model encodes not only the content of questions and answers,but also the temporal cues in a sequence of ordered sentences which gradually remark the answer.Moreover,our model collaboratively uses external knowledge for a better understanding of a given question.The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods.
