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
In this paper we investigate cycle base structures of a (weighted) graph and show that much information of short cycles is contained in an MCB (i.e., minimum cycle base). After setting up a Hall type theorem for base-...In this paper we investigate cycle base structures of a (weighted) graph and show that much information of short cycles is contained in an MCB (i.e., minimum cycle base). After setting up a Hall type theorem for base-transformation, we give a sufficient and necessary condition for a cycle base to be an MCB. Furthermore, we show that the structure of MCB in a (weighted)graph is unique. The property is also true for those having a longest length (although much work has been down in evaluating MCB, little is known for those having a longest length). We use those methods to find out some unknown properties for short cycles sharing particular properties in (unweighted) graphs. As applications, we determine the structures of short cycles in an embedded graph and show that there exist polynomially bounded algorithms in finding a shortest contractible cycle and a shortest two-sided cycle provided such cycles exist. Those answer an open problem of B. Mohar and C. Thomassen.展开更多
The stability of underground entry-type excavations(UETEs)is of paramount importance for ensuring the safety of mining operations.As more engineering cases are accumulated,machine learning(ML)has demonstrated great po...The stability of underground entry-type excavations(UETEs)is of paramount importance for ensuring the safety of mining operations.As more engineering cases are accumulated,machine learning(ML)has demonstrated great potential for the stability evaluation of UETEs.In this study,a hybrid stacking ensemble method aggregating support vector machine(SVM),k-nearest neighbor(KNN),decision tree(DT),random forest(RF),multilayer perceptron neural network(MLPNN)and extreme gradient boosting(XGBoost)algorithms was proposed to assess the stability of UETEs.Firstly,a total of 399 historical cases with two indicators were collected from seven mines.Subsequently,to pursue better evaluation performance,the hyperparameters of base learners(SVM,KNN,DT,RF,MLPNN and XGBoost)and meta learner(MLPNN)were tuned by combining a five-fold cross validation(CV)and simulated annealing(SA)approach.Based on the optimal hyperparameters configuration,the stacking ensemble models were constructed using the training set(75%of the data).Finally,the performance of the proposed approach was evaluated by two global metrics(accuracy and Cohen’s Kappa)and three within-class metrics(macro average of the precision,recall and F1-score)on the test set(25%of the data).In addition,the evaluation results were compared with six base learners optimized by SA.The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy,Kappa coefficient,macro average of the precision,recall and F1-score were 0.92,0.851,0.885,0.88 and 0.883,respectively.The rock mass rating(RMR)had the most important influence on evaluation results.Moreover,the critical span graph(CSG)was updated based on the proposed model,representing a significant improvement compared with the previous studies.This study can provide valuable guidance for stability analysis and risk management of UETEs.However,it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.展开更多
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(Grant No.10271048)the first author also acknowledges the financial support of Shanghai Priority Academic Disciplinethe fund of the Science and Technology Commission of Shanghai Municipality(Grant No.04JC14031).
文摘In this paper we investigate cycle base structures of a (weighted) graph and show that much information of short cycles is contained in an MCB (i.e., minimum cycle base). After setting up a Hall type theorem for base-transformation, we give a sufficient and necessary condition for a cycle base to be an MCB. Furthermore, we show that the structure of MCB in a (weighted)graph is unique. The property is also true for those having a longest length (although much work has been down in evaluating MCB, little is known for those having a longest length). We use those methods to find out some unknown properties for short cycles sharing particular properties in (unweighted) graphs. As applications, we determine the structures of short cycles in an embedded graph and show that there exist polynomially bounded algorithms in finding a shortest contractible cycle and a shortest two-sided cycle provided such cycles exist. Those answer an open problem of B. Mohar and C. Thomassen.
基金supported by the National Natural Science Foundation of China(Grant No.52204117)the Natural Science Foundation of Hunan Province,China(Grant No.2022JJ40601).
文摘The stability of underground entry-type excavations(UETEs)is of paramount importance for ensuring the safety of mining operations.As more engineering cases are accumulated,machine learning(ML)has demonstrated great potential for the stability evaluation of UETEs.In this study,a hybrid stacking ensemble method aggregating support vector machine(SVM),k-nearest neighbor(KNN),decision tree(DT),random forest(RF),multilayer perceptron neural network(MLPNN)and extreme gradient boosting(XGBoost)algorithms was proposed to assess the stability of UETEs.Firstly,a total of 399 historical cases with two indicators were collected from seven mines.Subsequently,to pursue better evaluation performance,the hyperparameters of base learners(SVM,KNN,DT,RF,MLPNN and XGBoost)and meta learner(MLPNN)were tuned by combining a five-fold cross validation(CV)and simulated annealing(SA)approach.Based on the optimal hyperparameters configuration,the stacking ensemble models were constructed using the training set(75%of the data).Finally,the performance of the proposed approach was evaluated by two global metrics(accuracy and Cohen’s Kappa)and three within-class metrics(macro average of the precision,recall and F1-score)on the test set(25%of the data).In addition,the evaluation results were compared with six base learners optimized by SA.The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy,Kappa coefficient,macro average of the precision,recall and F1-score were 0.92,0.851,0.885,0.88 and 0.883,respectively.The rock mass rating(RMR)had the most important influence on evaluation results.Moreover,the critical span graph(CSG)was updated based on the proposed model,representing a significant improvement compared with the previous studies.This study can provide valuable guidance for stability analysis and risk management of UETEs.However,it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.
