Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte...Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.展开更多
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
Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks,and generating descriptive text based on these graphs places significant emphasis on content consistency.Howe...Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks,and generating descriptive text based on these graphs places significant emphasis on content consistency.However,knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes,making it challenging to ensure content coherence in generating text that spans multiple sentences.This lack of coherence can further compromise the overall consistency of the content within a paragraph.In this work,we present the generation of scientific abstracts by leveraging knowledge graphs,with a focus on enhancing both content consistency and coherence.In particular,we construct the ACL Abstract Graph Dataset(ACL-AGD)which pairs knowledge graphs with text,incorporating sentence labels to guide text structure and diverse expressions.We then implement a Siamese network to complement and concretize the entities and relations based on paragraph structure by accomplishing two tasks:graph-to-text generation and entity alignment.Extensive experiments demonstrate that the logical paragraphs generated by our method exhibit entities with a uniform position distribution and appropriate frequency.In terms of content,our method accurately represents the information encoded in the knowledge graph,prevents the generation of irrelevant content,and achieves coherent and non-redundant adjacent sentences,even with a shared knowledge graph.展开更多
Data curation is vital for selecting effective demonstration examples in graph-to-text generation.However,evaluating the quality ofKnowledgeGraphs(KGs)remains challenging.Prior research exhibits a narrowfocus on struc...Data curation is vital for selecting effective demonstration examples in graph-to-text generation.However,evaluating the quality ofKnowledgeGraphs(KGs)remains challenging.Prior research exhibits a narrowfocus on structural statistics,such as the shortest path length,while the correctness of graphs in representing the associated text is rarely explored.To address this gap,we introduce a dual-perspective evaluation framework for KG-text data,based on the computation of structural adequacy and semantic alignment.Froma structural perspective,we propose the Weighted Incremental EdgeMethod(WIEM)to quantify graph completeness by leveraging agreement between relation models to predict possible edges between entities.WIEM targets to find increments from models on“unseen links”,whose presence is inversely proportional to the structural adequacy of the original KG in representing the text.From a semantic perspective,we evaluate how well a KG aligns with the text in capturing the intended meaning.To do so,we instruct a large language model to convert KGs into natural language andmeasure the similarity between generated and reference texts.Based on these computations,we apply a Top-K union method,integrating the structural and semantic modules,to rank and select high-quality KGs.We evaluate our framework against various approaches for selecting few-shot examples in graph-to-text generation.Experiments on theAssociation for Computational LinguisticsAbstract Graph Dataset(ACL-AGD)and Automatic Content Extraction 05(ACE05)dataset demonstrate the effectiveness of our approach in distinguishing KG-text data of different qualities,evidenced by the largest performance gap between top-and bottom-ranked examples.We also find that the top examples selected through our dual-perspective framework consistently yield better performance than those selected by traditional measures.These results highlight the importance of data curation in improving graph-to-text generation.展开更多
In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,prov...In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.展开更多
Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,ca...Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.展开更多
This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foun...This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.展开更多
This paper outlines the basic concept of knowledge graph and its unique advantages, and explains in detail its approach to processing complex data structures through data integration, relationship discovery and semant...This paper outlines the basic concept of knowledge graph and its unique advantages, and explains in detail its approach to processing complex data structures through data integration, relationship discovery and semantic understanding. Knowledge graphs utilize a combination of technologies such as entities, attributes, relationships, and semantic annotations to demonstrate indispensable functionality in standardization processes, and especially excel in achieving semantic connectivity. This paper systematically analyzes the role of knowledge graph in each level using the standards hierarchical model as a framework. In Level 1, knowledge graph supports information extraction and preliminary tagging;in Level 2, it realizes structured and semantic processing of documents;in Level 3, it facilitates complex relationship modeling and executive integration;and it lays the foundation for advanced intelligent applications, autonomous standards governance and dynamic automatic updating in Level 4 and 5. This paper provides an in-depth discussion of its future directions and possible challenges, including key topics such as optimizing the scalability of knowledge graphs and facilitating cross-domain knowledge fusion. It shows that knowledge graphs provide powerful technical support for standards digitization and offer new possibilities for realizing smart manufacturing and cross-domain collaboration.展开更多
With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and powe...With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and power distribution systems.Supporting these applications,an important family of methods are based on graphs.For DT applications in modeling and managing smart cities,large-scale knowledge graphs(KGs)are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems.To this end,this paper develops a conceptual framework:Automated knowledge Graphs for Complex Systems(AutoGraCS).In contrast to existing KGs developed for DTs,AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems.The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling,Bayesian analysis,and adaptive decision supports.Besides,AutoGraCS provides flexibility in support of users’need to implement the ontology and rules when constructing the KG.With the user-defined ontology and rules,AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems.The bridge network in Miami-Dade County,FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network,traffic monitoring facilities,and flood water watch stations.展开更多
Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,th...Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,the design of the existing software testing courses fails to meet the demands for personalized learning.Knowledge graphs,with their rich semantics and good visualization effects,have a wide range of applications in the field of education.In response to the current problem of software testing courses which fails to meet the needs for personalized learning,this paper offers a learning path recommendation based on knowledge graphs to provide personalized learning paths for students.展开更多
Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power system...Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.展开更多
With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as it...With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.展开更多
In the current era of rapid development of AI and Big Data,utilizing these emerging technologies to empower learning in specialized higher education courses in the electrical engineering discipline has become a hot to...In the current era of rapid development of AI and Big Data,utilizing these emerging technologies to empower learning in specialized higher education courses in the electrical engineering discipline has become a hot topic among scholars.This paper constructs a ternary graph comprising knowledge,issue,and competency layers,based on knowledge graphs.Combining knowledge graphs with the instructional design of flipped classrooms and double closed-loop teaching designs,students’learning enthusiasm and efficiency can be fully unleashed.In the practical teaching of fundamentals of electrical engineering course,students’learning abilities,innovative thinking skills,and interpersonal coordination competencies significantly improved.展开更多
Artificial Intelligence(AI)has revolutionized education by enabling personalized learning experiences through adaptive platforms.However,traditional AI-driven systems primarily rely on correlation-based analytics,lim-...Artificial Intelligence(AI)has revolutionized education by enabling personalized learning experiences through adaptive platforms.However,traditional AI-driven systems primarily rely on correlation-based analytics,lim-iting their ability to uncover the causal mechanisms behind learning outcomes.This study explores the in-tegration of Knowledge Graphs(KGs)and Causal Inference(CI)as a novel approach to enhance AI-driven educational systems.KGs provide a structured representation of educational knowledge,facilitating intelligent content recommendations and adaptive learning pathways,while CI enables AI systems to move beyond pattern recognition to identify cause-and-effect relationships in student learning.By combining these methods,this research aims to optimize personalized learning path recommendations,improve educational decision-making,and ensure AI-driven interventions are both data-informed and causally validated.Case studies from real-world applications,including intelligent tutoring systems and MOOC platforms,illustrate the practical impact of this approach.The findings contribute to advancing AI-driven education by fostering a balance between knowledge modeling,adaptability,and empirical rigor.展开更多
Mangroves are crucial to the ecological security of the Earth and human well-being.Their management,conservation,and restoration are of great importance and necessitate the support of spatio-temporal information and m...Mangroves are crucial to the ecological security of the Earth and human well-being.Their management,conservation,and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology.Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves.However,this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data,facing the problem of“massive data,rare knowledge”.Knowledge graphs are capable of extracting,organizing,and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers.They provide a solution for the realization of intelligent knowledge services.Focusing on the urgent need for mangrove knowledge acquisition,formal representation,and intelligent services,this paper proposes a research prospect on mangrove knowledge graphs and knowledge services.We first analyze the similarities and differences between various domain-specific concepts of Tupu.On this basis,we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core.Then,we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services,including multiple research levels such as ubiquitous data sensing and aggregation,knowledge organization and graph construction,and intelligent mangrove knowledge services.Subsequently,the methods and workflow for constructing mangrove knowledge graphs are introduced.Finally,we discuss the challenges and possible future directions of mangrove knowledge services in the smart era,including the construction of a mangrove knowledge system that integrates the domain-specific characteristics and spatio-temporal features of mangroves,the exploration of knowledge extraction and fusion methods supported by large language models,and the development of intelligent knowledge applications for typical scenarios.展开更多
The means of knowledge graph embedding is to transform entities and relations into low-dimensional vectors.When it is necessary to obtain the embedding results of two hetero-field knowledge graphs in a unified vector ...The means of knowledge graph embedding is to transform entities and relations into low-dimensional vectors.When it is necessary to obtain the embedding results of two hetero-field knowledge graphs in a unified vector space,there are only a few aligned entities between them,previous methods first need to merge the two graphs into a large graph,and then re-embed the entire large graph.This ignores the potential reuse of the original representation embeddings of two knowledge graphs and will lead to a lot of time consumption.To address this problem,this paper proposes a hetero-field knowledge graph embedding fusion model(BlockEF)based on aligned entities.According to the fact that the aligned entities of the two graphs should be located in the same position in the vector space,the transformation relationship between the embeddings of two graphs is firstly obtained,and then graph embedding is fine-tuned and optimized to achieve efficient fusion of hetero-field knowledge graph embeddings.The experimental results show that our method can significantly reduce the computational burden of heterofield knowledge graph embedding fusion and ensure the quality of embedding fusion.展开更多
With the continuous development of artificial intelligence and natural language processing technologies, traditional retrieval-augmented generation (RAG) techniques face numerous challenges in document answer precisio...With the continuous development of artificial intelligence and natural language processing technologies, traditional retrieval-augmented generation (RAG) techniques face numerous challenges in document answer precision and similarity measurement. This study, set against the backdrop of the shipping industry, combines top-down and bottom-up schema design strategies to achieve precise and flexible knowledge representation. The research adopts a semi-structured approach, innovatively constructing an adaptive schema generation mechanism based on reinforcement learning, which models the knowledge graph construction process as a Markov decision process. This method begins with general concepts, defining foundational industry concepts, and then delves into abstracting core concepts specific to the maritime domain through an adaptive pattern generation mechanism that dynamically adjusts the knowledge structure. Specifically, the study designs a four-layer knowledge construction framework, including the data layer, modeling layer, technology layer, and application layer. It draws on a mutual indexing strategy, integrating large language models and traditional information extraction techniques. By leveraging self-attention mechanisms and graph attention networks, it efficiently extracts semantic relationships. The introduction of logic-form-driven solvers and symbolic decomposition techniques for reasoning significantly enhances the model’s ability to understand complex semantic relationships. Additionally, the use of open information extraction and knowledge alignment techniques further improves the efficiency and accuracy of information retrieval. Experimental results demonstrate that the proposed method not only achieves significant performance improvements in knowledge graph retrieval within the shipping domain but also holds important theoretical innovation and practical application value.展开更多
A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolvin...A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.:62101087)the China Postdoctoral Science Foundation(Grant No.:2021MD703942)+2 种基金the Chongqing Postdoctoral Research Project Special Funding,China(Grant No.:2021XM2016)the Science Foundation of Chongqing Municipal Commission of Education,China(Grant No.:KJQN202100642)the Chongqing Natural Science Foundation,China(Grant No.:cstc2021jcyj-msxmX0834).
文摘Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.
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.
文摘Knowledge graphs convey precise semantic information that can be effectively interpreted by neural networks,and generating descriptive text based on these graphs places significant emphasis on content consistency.However,knowledge graphs are inadequate for providing additional linguistic features such as paragraph structure and expressive modes,making it challenging to ensure content coherence in generating text that spans multiple sentences.This lack of coherence can further compromise the overall consistency of the content within a paragraph.In this work,we present the generation of scientific abstracts by leveraging knowledge graphs,with a focus on enhancing both content consistency and coherence.In particular,we construct the ACL Abstract Graph Dataset(ACL-AGD)which pairs knowledge graphs with text,incorporating sentence labels to guide text structure and diverse expressions.We then implement a Siamese network to complement and concretize the entities and relations based on paragraph structure by accomplishing two tasks:graph-to-text generation and entity alignment.Extensive experiments demonstrate that the logical paragraphs generated by our method exhibit entities with a uniform position distribution and appropriate frequency.In terms of content,our method accurately represents the information encoded in the knowledge graph,prevents the generation of irrelevant content,and achieves coherent and non-redundant adjacent sentences,even with a shared knowledge graph.
文摘Data curation is vital for selecting effective demonstration examples in graph-to-text generation.However,evaluating the quality ofKnowledgeGraphs(KGs)remains challenging.Prior research exhibits a narrowfocus on structural statistics,such as the shortest path length,while the correctness of graphs in representing the associated text is rarely explored.To address this gap,we introduce a dual-perspective evaluation framework for KG-text data,based on the computation of structural adequacy and semantic alignment.Froma structural perspective,we propose the Weighted Incremental EdgeMethod(WIEM)to quantify graph completeness by leveraging agreement between relation models to predict possible edges between entities.WIEM targets to find increments from models on“unseen links”,whose presence is inversely proportional to the structural adequacy of the original KG in representing the text.From a semantic perspective,we evaluate how well a KG aligns with the text in capturing the intended meaning.To do so,we instruct a large language model to convert KGs into natural language andmeasure the similarity between generated and reference texts.Based on these computations,we apply a Top-K union method,integrating the structural and semantic modules,to rank and select high-quality KGs.We evaluate our framework against various approaches for selecting few-shot examples in graph-to-text generation.Experiments on theAssociation for Computational LinguisticsAbstract Graph Dataset(ACL-AGD)and Automatic Content Extraction 05(ACE05)dataset demonstrate the effectiveness of our approach in distinguishing KG-text data of different qualities,evidenced by the largest performance gap between top-and bottom-ranked examples.We also find that the top examples selected through our dual-perspective framework consistently yield better performance than those selected by traditional measures.These results highlight the importance of data curation in improving graph-to-text generation.
基金University-level Scientific Research Project in Natural Sciences“Research on the Retrieval Method of Multimodal First-Class Course Teaching Content Based on Knowledge Graph Collaboration”(GKY-2024KYYBK-31)。
文摘In the context of digitalization,course resources exhibit multimodal characteristics,covering various forms such as text,images,and videos.Course knowledge and learning resources are becoming increasingly diverse,providing favorable conditions for students’in-depth and efficient learning.Against this backdrop,how to scientifically apply emerging technologies to automatically collect,process,and integrate digital learning resources such as voices,videos,and courseware texts,and better innovate the organization and presentation forms of course knowledge has become an important development direction for“artificial intelligence+education.”This article elaborates on the elements and characteristics of knowledge graphs,analyzes the construction steps of knowledge graphs,and explores the construction methods of multimodal course knowledge graphs from aspects such as dataset collection,course knowledge ontology identification,knowledge discovery,and association,providing references for the intelligent application of online open courses.
基金supported in part by the National Natural Science Foundation of China(No.62302507)and the funding of Harbin Institute of Technology(Shenzhen)(No.20210035).
文摘Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.
基金Educational and Teaching Reform Project of Beihua University:Research on the Construction of“Same Course with Different Structures”Course Resources Based on Knowledge Graphs。
文摘This paper explores the construction methods of“Same Course with Different Structures”curriculum resources based on knowledge graphs and their applications in the field of education.By reviewing the theoretical foundations of knowledge graph technology,the“Same Course with Different Structures”teaching model,and curriculum resource construction,and integrating existing literature,the paper analyzes the methods for constructing curriculum resources using knowledge graphs.The research finds that knowledge graphs can effectively integrate multi-source data,support personalized teaching and precision education,and provide both a scientific foundation and technical support for the development of curriculum resources within the“Same Course with Different Structures”framework.
文摘This paper outlines the basic concept of knowledge graph and its unique advantages, and explains in detail its approach to processing complex data structures through data integration, relationship discovery and semantic understanding. Knowledge graphs utilize a combination of technologies such as entities, attributes, relationships, and semantic annotations to demonstrate indispensable functionality in standardization processes, and especially excel in achieving semantic connectivity. This paper systematically analyzes the role of knowledge graph in each level using the standards hierarchical model as a framework. In Level 1, knowledge graph supports information extraction and preliminary tagging;in Level 2, it realizes structured and semantic processing of documents;in Level 3, it facilitates complex relationship modeling and executive integration;and it lays the foundation for advanced intelligent applications, autonomous standards governance and dynamic automatic updating in Level 4 and 5. This paper provides an in-depth discussion of its future directions and possible challenges, including key topics such as optimizing the scalability of knowledge graphs and facilitating cross-domain knowledge fusion. It shows that knowledge graphs provide powerful technical support for standards digitization and offer new possibilities for realizing smart manufacturing and cross-domain collaboration.
基金support received from US Department of Transportation Tier 1 University Transportation Center CREATE Award No.69A3552348330.
文摘With the ability to harness the power of big data,the digital twin(DT)technology has been increasingly applied to the modeling and management of structures and infrastructure systems,such as buildings,bridges,and power distribution systems.Supporting these applications,an important family of methods are based on graphs.For DT applications in modeling and managing smart cities,large-scale knowledge graphs(KGs)are necessary to represent the complex interdependencies and model the urban infrastructure as a system of systems.To this end,this paper develops a conceptual framework:Automated knowledge Graphs for Complex Systems(AutoGraCS).In contrast to existing KGs developed for DTs,AutoGraCS can support KGs to account for interdependencies and statistical correlations across complex systems.The established KGs from AutoGraCS can then be easily turned into Bayesian networks for probabilistic modeling,Bayesian analysis,and adaptive decision supports.Besides,AutoGraCS provides flexibility in support of users’need to implement the ontology and rules when constructing the KG.With the user-defined ontology and rules,AutoGraCS can automatically generate a KG to represent a complex system consisting of multiple systems.The bridge network in Miami-Dade County,FL is used as an illustrative example to generate a KG that integrates multiple layers of data from the bridge network,traffic monitoring facilities,and flood water watch stations.
基金supported by the Special Funds for Basic Research of Central Universities(D5000220240)the Special Funds for Education and Teaching Reform in 2023(06410-23GZ230102)。
文摘Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,the design of the existing software testing courses fails to meet the demands for personalized learning.Knowledge graphs,with their rich semantics and good visualization effects,have a wide range of applications in the field of education.In response to the current problem of software testing courses which fails to meet the needs for personalized learning,this paper offers a learning path recommendation based on knowledge graphs to provide personalized learning paths for students.
基金supported by the National Key R&D Program of China(2018AAA0101502)the Science and Technology Project of SGCC(State Grid Corporation of China):Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control。
文摘Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.
基金The National Key R&D Program of China(2018AAA0102100)Hunan Provincial Department of Education Outstanding Youth Project(22B0385)+2 种基金Open Fund of the Domestic First-class Discipline Construction Project of Chinese Medicine of Hunan University of Chinese Medicine(2018ZYX17)Electronic Science and Technology Discipline Open Fund Project of School of Information Science and Engineering,Hunan University of Chinese Medicine(2018-2)Hunan University of Chinese Medicine Graduate Innovation Project(2022CX122)。
文摘With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing exponentially.Consequently,there is much attention on the collection of useful knowledge as well as its effective organization and expression.Knowledge graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of research.This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge reasoning.Secondly,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are introduced.Then,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out.Finally,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.
基金supported by the 2024 Provincial Teaching Reform Research Program of Hubei Undergraduate Colleges and Universities(Artificial Intelligence–AI Teaching Assistant–Knowledge Graph Empowerment in New Engineering Education Design and Innovation:A Case Study of the Fundamentals of Electrical Engineering Course)China’s Ministry of Education Equipment Renewal Project for the Digital and Intelligent Education and Teaching Platform(Grant No.2406-000000-05-03-583551).
文摘In the current era of rapid development of AI and Big Data,utilizing these emerging technologies to empower learning in specialized higher education courses in the electrical engineering discipline has become a hot topic among scholars.This paper constructs a ternary graph comprising knowledge,issue,and competency layers,based on knowledge graphs.Combining knowledge graphs with the instructional design of flipped classrooms and double closed-loop teaching designs,students’learning enthusiasm and efficiency can be fully unleashed.In the practical teaching of fundamentals of electrical engineering course,students’learning abilities,innovative thinking skills,and interpersonal coordination competencies significantly improved.
文摘Artificial Intelligence(AI)has revolutionized education by enabling personalized learning experiences through adaptive platforms.However,traditional AI-driven systems primarily rely on correlation-based analytics,lim-iting their ability to uncover the causal mechanisms behind learning outcomes.This study explores the in-tegration of Knowledge Graphs(KGs)and Causal Inference(CI)as a novel approach to enhance AI-driven educational systems.KGs provide a structured representation of educational knowledge,facilitating intelligent content recommendations and adaptive learning pathways,while CI enables AI systems to move beyond pattern recognition to identify cause-and-effect relationships in student learning.By combining these methods,this research aims to optimize personalized learning path recommendations,improve educational decision-making,and ensure AI-driven interventions are both data-informed and causally validated.Case studies from real-world applications,including intelligent tutoring systems and MOOC platforms,illustrate the practical impact of this approach.The findings contribute to advancing AI-driven education by fostering a balance between knowledge modeling,adaptability,and empirical rigor.
基金supported by the National Natural Science Foundation of China(Grant No.42301536)the National Key Research and Development Program of China(Grant No.2022YFF0711602)the GDAS’Project of Science and Technology Development(Grant Nos.2022GDASZH-2022010202,2022GDASZH2022020402-01&2022GDASZH-2022010111)。
文摘Mangroves are crucial to the ecological security of the Earth and human well-being.Their management,conservation,and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and ecology.Traditional knowledge services such as plant atlas provide illustrated textual knowledge of mangroves.However,this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data,facing the problem of“massive data,rare knowledge”.Knowledge graphs are capable of extracting,organizing,and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by computers.They provide a solution for the realization of intelligent knowledge services.Focusing on the urgent need for mangrove knowledge acquisition,formal representation,and intelligent services,this paper proposes a research prospect on mangrove knowledge graphs and knowledge services.We first analyze the similarities and differences between various domain-specific concepts of Tupu.On this basis,we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the core.Then,we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services,including multiple research levels such as ubiquitous data sensing and aggregation,knowledge organization and graph construction,and intelligent mangrove knowledge services.Subsequently,the methods and workflow for constructing mangrove knowledge graphs are introduced.Finally,we discuss the challenges and possible future directions of mangrove knowledge services in the smart era,including the construction of a mangrove knowledge system that integrates the domain-specific characteristics and spatio-temporal features of mangroves,the exploration of knowledge extraction and fusion methods supported by large language models,and the development of intelligent knowledge applications for typical scenarios.
基金supported by the Deep-time Digital Earth (DDE) Big Science Programin part by the National Natural Science Foundation of China (NSFC) (No. 61972365)in part by the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing (No. KLIGIP-2021B01)
文摘The means of knowledge graph embedding is to transform entities and relations into low-dimensional vectors.When it is necessary to obtain the embedding results of two hetero-field knowledge graphs in a unified vector space,there are only a few aligned entities between them,previous methods first need to merge the two graphs into a large graph,and then re-embed the entire large graph.This ignores the potential reuse of the original representation embeddings of two knowledge graphs and will lead to a lot of time consumption.To address this problem,this paper proposes a hetero-field knowledge graph embedding fusion model(BlockEF)based on aligned entities.According to the fact that the aligned entities of the two graphs should be located in the same position in the vector space,the transformation relationship between the embeddings of two graphs is firstly obtained,and then graph embedding is fine-tuned and optimized to achieve efficient fusion of hetero-field knowledge graph embeddings.The experimental results show that our method can significantly reduce the computational burden of heterofield knowledge graph embedding fusion and ensure the quality of embedding fusion.
文摘With the continuous development of artificial intelligence and natural language processing technologies, traditional retrieval-augmented generation (RAG) techniques face numerous challenges in document answer precision and similarity measurement. This study, set against the backdrop of the shipping industry, combines top-down and bottom-up schema design strategies to achieve precise and flexible knowledge representation. The research adopts a semi-structured approach, innovatively constructing an adaptive schema generation mechanism based on reinforcement learning, which models the knowledge graph construction process as a Markov decision process. This method begins with general concepts, defining foundational industry concepts, and then delves into abstracting core concepts specific to the maritime domain through an adaptive pattern generation mechanism that dynamically adjusts the knowledge structure. Specifically, the study designs a four-layer knowledge construction framework, including the data layer, modeling layer, technology layer, and application layer. It draws on a mutual indexing strategy, integrating large language models and traditional information extraction techniques. By leveraging self-attention mechanisms and graph attention networks, it efficiently extracts semantic relationships. The introduction of logic-form-driven solvers and symbolic decomposition techniques for reasoning significantly enhances the model’s ability to understand complex semantic relationships. Additionally, the use of open information extraction and knowledge alignment techniques further improves the efficiency and accuracy of information retrieval. Experimental results demonstrate that the proposed method not only achieves significant performance improvements in knowledge graph retrieval within the shipping domain but also holds important theoretical innovation and practical application value.
基金supported in part by the National Key R&D Program of China(No.2021ZD0113305)the National Natural Science Foundation of China(Grant Nos.61960206008,62002292,42050105,62020106005,62061146002,61960206002)+1 种基金the National Science Fund for Distinguished Young Scholars(No.61725205)Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University.
文摘A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
