Knowledge graphs (KGs) offer a structured, machine-readable format for organizing complex information. In heterogeneous catalysis, where data on catalytic materials, reaction conditions, mechanisms, and synthesis rout...Knowledge graphs (KGs) offer a structured, machine-readable format for organizing complex information. In heterogeneous catalysis, where data on catalytic materials, reaction conditions, mechanisms, and synthesis routes are dispersed across diverse sources, KGs provide a semantic framework that supports data integration under the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This review aims to survey recent developments in catalysis KGs, describe the main techniques for graph construction, and highlight how artificial intelligence, particularly large language models (LLMs), enhances graph generation and query. We conducted a systematic analysis of the literature, focusing on ontology-guided text mining pipelines, graph population methods, and maintenance strategies. Our review identifies key trends: ontology-based approaches enable the automated extraction of domain knowledge, LLM-driven retrieval-augmented generation supports natural-language queries, and scalable graph architectures range from a few thousand to over a million triples. We discuss state-of-the-art applications, such as catalyst recommendation systems and reaction mechanism discovery tools, and examine the major challenges, including data heterogeneity, ontology alignment, and long-term graph curation. We conclude that KGs, when combined with AI methods, hold significant promise for accelerating catalyst discovery and knowledge management, but progress depends on establishing community standards for ontology development and maintenance. This review provides a roadmap for researchers seeking to leverage KGs to advance heterogeneous catalysis research.展开更多
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
As an advanced data science technology,the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine(TCM).This not only contributes to a deeper com...As an advanced data science technology,the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine(TCM).This not only contributes to a deeper comprehension of traditional Chinese medical theories but also provides robust support for the intelligent decision systems and medical applications of TCM.Against this backdrop,this paper aims to systematically review the current status and development trends of TCM knowledge graphs,offering theoretical and technical foundations to facilitate the inheritance,innovation,and integrated development of TCM.Firstly,we introduce the relevant concepts and research status of TCM knowledge graphs.Secondly,we conduct an in-depth analysis of the challenges and trends faced by key technologies in TCM knowledge graph construction,such as knowledge representation,extraction,fusion,and reasoning,and classifies typical knowledge graphs in various subfields of TCM.Next,we comprehensively outline the current medical applications of TCM knowledge graphs in areas such as information retrieval,diagnosis,question answering,recommendation,and knowledge mining.Finally,the current research status and future directions of TCM knowledge graphs are concluded and discussed.We believe this paper contributes to a deeper understanding of the research dynamics in TCM knowledge graphs and provides essential references for scholars in related fields.展开更多
钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井...钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。展开更多
随着遥感、物联网、人工智能、大数据、云计算以及近年来迅速发展的大语言模型(large language models,简称LLMs)等高新技术持续取得突破,地震与地质灾害研究正加速从传统依赖单一数据源与经验规则的范式,迈向多源信息融合与智能驱动的...随着遥感、物联网、人工智能、大数据、云计算以及近年来迅速发展的大语言模型(large language models,简称LLMs)等高新技术持续取得突破,地震与地质灾害研究正加速从传统依赖单一数据源与经验规则的范式,迈向多源信息融合与智能驱动的风险识别和决策支持体系。基于“高新技术在地震与地质灾害领域的应用研究”专栏,系统梳理了当前在物理仿真模拟、深度学习识别、遥感集成分析、智能预警技术与知识图谱构建等关键方向的研究进展,概括展示了高新技术在灾害风险监测、致灾机制解析与应急响应支撑中的典型应用与发展趋势。在此基础上,进一步总结了多模态数据集成、灾害链建模、模型泛化能力与场景适应性等方面面临的技术瓶颈,探讨了大语言模型在地震与地质灾害领域中的潜在价值,包括知识抽取、因果推理与多场景风险研判等方面的前沿探索。展开更多
基金support from the Full Bridge Fellowship for enabling the research stay at Virginia Tech.H.Xin acknowledge the financial support from the US Department of Energy,Office of Basic Energy Sciences under contract no.DE-SC0023323from the National Science Foundation through the grant 2245402 from CBET Catalysis and CDS&E programs.
文摘Knowledge graphs (KGs) offer a structured, machine-readable format for organizing complex information. In heterogeneous catalysis, where data on catalytic materials, reaction conditions, mechanisms, and synthesis routes are dispersed across diverse sources, KGs provide a semantic framework that supports data integration under the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This review aims to survey recent developments in catalysis KGs, describe the main techniques for graph construction, and highlight how artificial intelligence, particularly large language models (LLMs), enhances graph generation and query. We conducted a systematic analysis of the literature, focusing on ontology-guided text mining pipelines, graph population methods, and maintenance strategies. Our review identifies key trends: ontology-based approaches enable the automated extraction of domain knowledge, LLM-driven retrieval-augmented generation supports natural-language queries, and scalable graph architectures range from a few thousand to over a million triples. We discuss state-of-the-art applications, such as catalyst recommendation systems and reaction mechanism discovery tools, and examine the major challenges, including data heterogeneity, ontology alignment, and long-term graph curation. We conclude that KGs, when combined with AI methods, hold significant promise for accelerating catalyst discovery and knowledge management, but progress depends on establishing community standards for ontology development and maintenance. This review provides a roadmap for researchers seeking to leverage KGs to advance heterogeneous catalysis research.
基金supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
基金supported by the research“Evidence Study Based on Multimodal Knowledge Graph Reasoning of the Idea of Treating Pre-disease in TCM(2023016)”the National Natural Science Foundation of China(No.82374621).
文摘As an advanced data science technology,the knowledge graph systematically integrates and displays the knowledge framework within the field of traditional Chinese medicine(TCM).This not only contributes to a deeper comprehension of traditional Chinese medical theories but also provides robust support for the intelligent decision systems and medical applications of TCM.Against this backdrop,this paper aims to systematically review the current status and development trends of TCM knowledge graphs,offering theoretical and technical foundations to facilitate the inheritance,innovation,and integrated development of TCM.Firstly,we introduce the relevant concepts and research status of TCM knowledge graphs.Secondly,we conduct an in-depth analysis of the challenges and trends faced by key technologies in TCM knowledge graph construction,such as knowledge representation,extraction,fusion,and reasoning,and classifies typical knowledge graphs in various subfields of TCM.Next,we comprehensively outline the current medical applications of TCM knowledge graphs in areas such as information retrieval,diagnosis,question answering,recommendation,and knowledge mining.Finally,the current research status and future directions of TCM knowledge graphs are concluded and discussed.We believe this paper contributes to a deeper understanding of the research dynamics in TCM knowledge graphs and provides essential references for scholars in related fields.
文摘钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。
文摘随着遥感、物联网、人工智能、大数据、云计算以及近年来迅速发展的大语言模型(large language models,简称LLMs)等高新技术持续取得突破,地震与地质灾害研究正加速从传统依赖单一数据源与经验规则的范式,迈向多源信息融合与智能驱动的风险识别和决策支持体系。基于“高新技术在地震与地质灾害领域的应用研究”专栏,系统梳理了当前在物理仿真模拟、深度学习识别、遥感集成分析、智能预警技术与知识图谱构建等关键方向的研究进展,概括展示了高新技术在灾害风险监测、致灾机制解析与应急响应支撑中的典型应用与发展趋势。在此基础上,进一步总结了多模态数据集成、灾害链建模、模型泛化能力与场景适应性等方面面临的技术瓶颈,探讨了大语言模型在地震与地质灾害领域中的潜在价值,包括知识抽取、因果推理与多场景风险研判等方面的前沿探索。
