Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurr...Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.展开更多
Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The ...Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.展开更多
采用面向对象方法进行桥梁知识数据组织,建立了包含几何信息和非几何信息的桥梁案例数据,并通过系统建模平台,建立了和中小跨径桥梁通用图库中项目图纸信息一一对应的可供推理的案例数据库。案例数据采用SQL Server 2005数据库技术进行...采用面向对象方法进行桥梁知识数据组织,建立了包含几何信息和非几何信息的桥梁案例数据,并通过系统建模平台,建立了和中小跨径桥梁通用图库中项目图纸信息一一对应的可供推理的案例数据库。案例数据采用SQL Server 2005数据库技术进行结构化存储。研究了适合桥梁结构设计的构件相似性定义,建立了获取桥梁结构设计相似案例方法。采用人工神经元网络技术,挖掘相似案例中蕴涵的知识,实现对最相似案例的修改,形成符合新的设计要求的桥梁设计案例。实现桥梁数据的参数和知识驱动,形成了基于挖掘以往设计案例中包含的知识的桥梁结构设计方法,为桥梁结构设计企业重复利用以往设计案例提供了有效的解决方法和新的思路。展开更多
基金the National Natural Science Founda-tion of China(62062062)hosted by Gulila Altenbek.
文摘Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A2C1011198).
文摘Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia ensues.It is a global health problem.99%of clinical trials have failed to limit the progression of this disease.The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms.Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction.In this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive scores.Establishing an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very useful.Thus,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning models.These models are random forest(RF),JRip,and J48,which have been used along with the voting ensemble.ADNI dataset was used for this study.The proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference rules.Besides,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.
文摘采用面向对象方法进行桥梁知识数据组织,建立了包含几何信息和非几何信息的桥梁案例数据,并通过系统建模平台,建立了和中小跨径桥梁通用图库中项目图纸信息一一对应的可供推理的案例数据库。案例数据采用SQL Server 2005数据库技术进行结构化存储。研究了适合桥梁结构设计的构件相似性定义,建立了获取桥梁结构设计相似案例方法。采用人工神经元网络技术,挖掘相似案例中蕴涵的知识,实现对最相似案例的修改,形成符合新的设计要求的桥梁设计案例。实现桥梁数据的参数和知识驱动,形成了基于挖掘以往设计案例中包含的知识的桥梁结构设计方法,为桥梁结构设计企业重复利用以往设计案例提供了有效的解决方法和新的思路。
