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
In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS ...In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.展开更多
Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two o...Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two objects with one or multiple related attributes.The emerging Graph Neural Networks(GNN)can extract both object characteristics and relations from KGs.This paper presents how Machine Learning(ML)meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning.The paper also highlights important aspects of this area of research,discussing open issues such as the bias hidden in KGs at different levels of graph representation。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.
基金supported by the National Science Foundation of China Grant No.61762092“Dynamic multi-objective requirement optimization based on transfer learning”,No.61762089+2 种基金“The key research of high order tensor decomposition in distributed environment”the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province,Grant No.2017SE204,”Research on extracting software feature models using transfer learning”.
文摘In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.
文摘Remarkable progress in research has shown the efficiency of Knowledge Graphs(KGs)in extracting valuable external knowledge in various domains.A Knowledge Graph(KG)can illustrate high-order relations that connect two objects with one or multiple related attributes.The emerging Graph Neural Networks(GNN)can extract both object characteristics and relations from KGs.This paper presents how Machine Learning(ML)meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning.The paper also highlights important aspects of this area of research,discussing open issues such as the bias hidden in KGs at different levels of graph representation。
