Most of the questions from users lack the context needed to thoroughly understand the problemat hand,thus making the questions impossible to answer.Semantic Similarity Estimation is based on relating user’s questions...Most of the questions from users lack the context needed to thoroughly understand the problemat hand,thus making the questions impossible to answer.Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems(CSS)to provide answers without requesting the user’s context.It imposes constraints on the time needed to produce an answer for the user.The proposed model enables the use of contextual data associated with previous Conversational Searches(CS).While receiving a question in a new conversational search,the model determines the question that refers tomore pastCS.Themodel then infers past contextual data related to the given question and predicts an answer based on the context inferred without engaging in multi-turn interactions or requesting additional data from the user for context.This model shows the ability to use the limited information in user queries for best context inferences based on Closed-Domain-based CS and Bidirectional Encoder Representations from Transformers for textual representations.展开更多
Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is pre...Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.展开更多
Discovering genetic basis of diseases is an important goal and a challenging problem in bioinformatics research. Inspired by network-based global inference approach, Semi-global inference method is proposed to capture...Discovering genetic basis of diseases is an important goal and a challenging problem in bioinformatics research. Inspired by network-based global inference approach, Semi-global inference method is proposed to capture the complex associations between phenotypes and genes. The proposed method integrates phenotype similarities and protein-protein interactions, and it establishes the profile vectors of phenotypes and proteins. Then the relevance between each candidate gene and the target phenotype is evaluated. Candidate genes are then ranked according to relevance mark and genes that are potentially associated with target disease are identified based on this ranking. The model selects nodes in integrated phenotype-protein network for inference, by exploiting Phenotype Similarity Threshold (PST), which throws lights on selection of similar phenotypes for gene prediction problem. Different vector relevance metrics for computing the relevance marks of candidate genes are discussed. The performance of the model is evaluated on Online Mendelian Inheritance in Man (OMIM) data sets and experimental evaluation shows high performance of proposed Semi-global method outperforms existing global inference methods.展开更多
文摘Most of the questions from users lack the context needed to thoroughly understand the problemat hand,thus making the questions impossible to answer.Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems(CSS)to provide answers without requesting the user’s context.It imposes constraints on the time needed to produce an answer for the user.The proposed model enables the use of contextual data associated with previous Conversational Searches(CS).While receiving a question in a new conversational search,the model determines the question that refers tomore pastCS.Themodel then infers past contextual data related to the given question and predicts an answer based on the context inferred without engaging in multi-turn interactions or requesting additional data from the user for context.This model shows the ability to use the limited information in user queries for best context inferences based on Closed-Domain-based CS and Bidirectional Encoder Representations from Transformers for textual representations.
基金supported by 2013 Comprehensive Reform Pilot of Marine Engineering Specialty(No.ZG0434)
文摘Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.
文摘Discovering genetic basis of diseases is an important goal and a challenging problem in bioinformatics research. Inspired by network-based global inference approach, Semi-global inference method is proposed to capture the complex associations between phenotypes and genes. The proposed method integrates phenotype similarities and protein-protein interactions, and it establishes the profile vectors of phenotypes and proteins. Then the relevance between each candidate gene and the target phenotype is evaluated. Candidate genes are then ranked according to relevance mark and genes that are potentially associated with target disease are identified based on this ranking. The model selects nodes in integrated phenotype-protein network for inference, by exploiting Phenotype Similarity Threshold (PST), which throws lights on selection of similar phenotypes for gene prediction problem. Different vector relevance metrics for computing the relevance marks of candidate genes are discussed. The performance of the model is evaluated on Online Mendelian Inheritance in Man (OMIM) data sets and experimental evaluation shows high performance of proposed Semi-global method outperforms existing global inference methods.
