Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in relat...Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.展开更多
Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly ...Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.展开更多
This paper critically re-examines the anthropocentric“Goldilocks Zone”paradigm that has dominated the search for extraterrestrial life.As of 2024,more than 5,500 exoplanets have been identified,yet only about 2%are ...This paper critically re-examines the anthropocentric“Goldilocks Zone”paradigm that has dominated the search for extraterrestrial life.As of 2024,more than 5,500 exoplanets have been identified,yet only about 2%are located within the traditionally defined habitable zone(National Aeronautics and Space Administration(NASA)Exoplanet Archive,2024;Kane et al.,2023).Moreover,the discovery of extremophiles-organisms thriving in high-radiation,extreme heat,or vacuum environments-demonstrates that the boundaries of life far exceed Earth-like conditions(Rothschild&Mancinelli,2001).展开更多
Metaverse is a new emerging concept building up a virtual environment for the user using Virtual Reality(VR)and blockchain technology but introduces privacy risks.Now,a series of challenges arise in Metaverse security...Metaverse is a new emerging concept building up a virtual environment for the user using Virtual Reality(VR)and blockchain technology but introduces privacy risks.Now,a series of challenges arise in Metaverse security,including massive data traffic breaches,large-scale user tracking,analysis activities,unreliable Artificial Intelligence(AI)analysis results,and social engineering security for people.In this work,we concentrate on Decentraland and Sandbox,two well-known Metaverse applications in Web 3.0.Our experiments analyze,for the first time,the personal privacy data exposed by Metaverse applications and services from a combined perspective of network traffic and privacy policy.We develop a lightweight traffic processing approach suitable for the Web 3.0 environment,which does not rely on complex decryption or reverse engineering techniques.We propose a smart contract interaction traffic analysis method capable of retrieving user interactions with Metaverse applications and blockchain smart contracts.This method provides a new approach to de-anonymizing users'identities through Metaverse applications.Our system,METAseen,analyzes and compares network traffic with the privacy policies of Metaverse applications to identify controversial data collection practices.The consistency check experiment reveals that the data types exposed by Metaverse applications include Personal Identifiable Information(PII),device information,and Metaverse-related data.By comparing the data flows observed in the network traffic with assertions made in the privacy regulations of the Metaverse service provider,we discovered that far more than 49%of the Metaverse data flows needed to be disclosed appropriately.展开更多
Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relati...Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.展开更多
A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These...A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.展开更多
This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to im...This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.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
Geographic Information System(GIS)layers contain both spatial precision and domain knowledge,making them valuable for mineral prospectivity analysis.This study proposes a task-oriented methodology to struct con-a mine...Geographic Information System(GIS)layers contain both spatial precision and domain knowledge,making them valuable for mineral prospectivity analysis.This study proposes a task-oriented methodology to struct con-a mineral prospecting knowledge graph directly from GIS maps.The framework integrates ontology construction,spatiotemporal semantic embedding,and triple confidence evaluation.Ontologies are built from GIS layers through terminology extraction and alignment with existing standards,while spatial and temporal semantics are encoded using GeoSPARQL and the Geological Time Ontology.Graph Convolutional Networks(GCN)combined with the TransE embedding model are then applied to assess triple plausibility.A case study in the Eastern Tianshan region of Xinjiang verifies the effectiveness of the proposed method through semantic evaluation and graph-theoretic analysis.Guided by GIS,ontology construction significantly enhances the semantic fidelity and structural robustness of the prospecting knowledge graph,providing relatively reliable support for subsequent reasoning and predictive studies.展开更多
Intensely using online social networks(OSNs)makes users concerned about privacy of data.Given the centralized nature of these platforms,and since each platform has a particular storage mechanism,authentication,and acc...Intensely using online social networks(OSNs)makes users concerned about privacy of data.Given the centralized nature of these platforms,and since each platform has a particular storage mechanism,authentication,and access control,their users do not have the control and the right over their data.Therefore,users cannot easily switch between similar platforms or transfer data from one platform to another.These issues imply,among other things,a threat to privacy since such users depend on the interests of the service provider responsible for administering OSNs.As a strategy for the decentralization of the OSNs and,consequently,as a solution to the privacy problems in these environments,the so-called decentralized online social networks(DOSNs)have emerged.Unlike OSNs,DOSNs are decentralized content management platforms because they do not use centralized service providers.Although DOSNs address some of the privacy issues encountered in OSNs,DOSNs also pose significant challenges to consider,for example,access control to user profile information with high granularity.This work proposes developing an ontological model and a service to support privacy in DOSNs.The model describes the main concepts of privacy access control in DOSNs and their relationships.In addition,the service will consume the model to apply access control according to the policies represented in the model.Our model was evaluated in two phases to verify its compliance with the proposed domain.Finally,we evaluated our service with a performance evaluation,and the results were satisfactory concerning the response time of access control requests.展开更多
Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive...Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.展开更多
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.展开更多
基金supported by the National Natural Science Foundation of China(61902095).
文摘Biomedical big data,characterized by its massive scale,multi-dimensionality,and heterogeneity,offers novel perspectives for disease research,elucidates biological principles,and simultaneously prompts changes in related research methodologies.Biomedical ontology,as a shared formal conceptual system,not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research.In this review,we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties,highlighting how technological advancements are enabling the more comprehensive use of ontology information.Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list.Deep learning,on the other hand,represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction.With the continuous evolution of big data technologies,the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
基金partially funded by the JSPS KAKENHI Grant Number JP22K18004.
文摘Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.
文摘This paper critically re-examines the anthropocentric“Goldilocks Zone”paradigm that has dominated the search for extraterrestrial life.As of 2024,more than 5,500 exoplanets have been identified,yet only about 2%are located within the traditionally defined habitable zone(National Aeronautics and Space Administration(NASA)Exoplanet Archive,2024;Kane et al.,2023).Moreover,the discovery of extremophiles-organisms thriving in high-radiation,extreme heat,or vacuum environments-demonstrates that the boundaries of life far exceed Earth-like conditions(Rothschild&Mancinelli,2001).
基金supported by the National Key R&D Program of China (2021YFB2700200)the National Natural Science Foundation of China (U21B2021,61932014,61972018,62202027)+2 种基金Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)Beijing Natural Science Foundation (M23016)Yunnan Key Laboratory of Blockchain Application Technology Open Project (202105AG070005,YNB202206)。
文摘Metaverse is a new emerging concept building up a virtual environment for the user using Virtual Reality(VR)and blockchain technology but introduces privacy risks.Now,a series of challenges arise in Metaverse security,including massive data traffic breaches,large-scale user tracking,analysis activities,unreliable Artificial Intelligence(AI)analysis results,and social engineering security for people.In this work,we concentrate on Decentraland and Sandbox,two well-known Metaverse applications in Web 3.0.Our experiments analyze,for the first time,the personal privacy data exposed by Metaverse applications and services from a combined perspective of network traffic and privacy policy.We develop a lightweight traffic processing approach suitable for the Web 3.0 environment,which does not rely on complex decryption or reverse engineering techniques.We propose a smart contract interaction traffic analysis method capable of retrieving user interactions with Metaverse applications and blockchain smart contracts.This method provides a new approach to de-anonymizing users'identities through Metaverse applications.Our system,METAseen,analyzes and compares network traffic with the privacy policies of Metaverse applications to identify controversial data collection practices.The consistency check experiment reveals that the data types exposed by Metaverse applications include Personal Identifiable Information(PII),device information,and Metaverse-related data.By comparing the data flows observed in the network traffic with assertions made in the privacy regulations of the Metaverse service provider,we discovered that far more than 49%of the Metaverse data flows needed to be disclosed appropriately.
基金Supported by National Natural Science Foundation of China(Grant No.52375479)。
文摘Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents.This information encompasses various levels of elements and complex semantic relationships.However,there is currently a scarcity of effective modeling techniques to express these documents'inherent assembly process knowledge.This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products(APKG-CP)utilizing text mining techniques to tackle the challenges of high costs,low efficiency,and difficulty reusing process knowledge.Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels.The Bert-BiLSTM-CRF model integrates BERT(bidirectional encoder representations from transformers),BiLSTM(bidirectional long short-term memory),and CRF(conditional random field)to extract knowledge entities and relationships in assembly process documents automatically.Furthermore,the knowledge fusion method automatically instantiates the assembly process knowledge graph.The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example.Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.
文摘A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational,maintenance,and feedback phases following railway incidents or accidents.These approaches exploit railway safety data once the transport system has received authorization for commissioning.However,railway standards and regulations require the development of a safety management system(SMS)from the specification and design phases of the railway system.This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to improving the development of the SMS.Unlike some learning methods,the proposed approach,which is dedicated in particular to safety assessment bodies,is based on semi-supervised learning carried out in close collaboration with safety experts who contributed to the development of a database of potential accident scenarios(learning example database)relating to the risk of rail collision.The proposed decision support is based on the use of an expert system whose knowledge base is automatically generated by inductive learning in the form of an association rule(rule base)and whose main objective is to suggest to the safety expert possible hazards not considered during the development of the SMS to complete the initial hazard register.
基金supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091)Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).
文摘This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
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 Natural Science Foundation of Jilin Province(20220101114JC).
文摘Geographic Information System(GIS)layers contain both spatial precision and domain knowledge,making them valuable for mineral prospectivity analysis.This study proposes a task-oriented methodology to struct con-a mineral prospecting knowledge graph directly from GIS maps.The framework integrates ontology construction,spatiotemporal semantic embedding,and triple confidence evaluation.Ontologies are built from GIS layers through terminology extraction and alignment with existing standards,while spatial and temporal semantics are encoded using GeoSPARQL and the Geological Time Ontology.Graph Convolutional Networks(GCN)combined with the TransE embedding model are then applied to assess triple plausibility.A case study in the Eastern Tianshan region of Xinjiang verifies the effectiveness of the proposed method through semantic evaluation and graph-theoretic analysis.Guided by GIS,ontology construction significantly enhances the semantic fidelity and structural robustness of the prospecting knowledge graph,providing relatively reliable support for subsequent reasoning and predictive studies.
基金Fundação de AmparoàPesquisa do Estado da Bahia(FAPESB),Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)organizations for supporting the Graduate Program in Computer Science at the Federal University of Bahia.
文摘Intensely using online social networks(OSNs)makes users concerned about privacy of data.Given the centralized nature of these platforms,and since each platform has a particular storage mechanism,authentication,and access control,their users do not have the control and the right over their data.Therefore,users cannot easily switch between similar platforms or transfer data from one platform to another.These issues imply,among other things,a threat to privacy since such users depend on the interests of the service provider responsible for administering OSNs.As a strategy for the decentralization of the OSNs and,consequently,as a solution to the privacy problems in these environments,the so-called decentralized online social networks(DOSNs)have emerged.Unlike OSNs,DOSNs are decentralized content management platforms because they do not use centralized service providers.Although DOSNs address some of the privacy issues encountered in OSNs,DOSNs also pose significant challenges to consider,for example,access control to user profile information with high granularity.This work proposes developing an ontological model and a service to support privacy in DOSNs.The model describes the main concepts of privacy access control in DOSNs and their relationships.In addition,the service will consume the model to apply access control according to the policies represented in the model.Our model was evaluated in two phases to verify its compliance with the proposed domain.Finally,we evaluated our service with a performance evaluation,and the results were satisfactory concerning the response time of access control requests.
文摘Background:With growing interest in space exploration,understanding microgravity’s impact on human health is essential.This study aims to investigate gene expression changes and migration and invasion potential infive thyroid-related cell lines cultured under simulated microgravity.Methods:Five thyroid-related cell lines—normal thyrocytes(Nthy-ori 3-1),papillary thyroid cancer(PTC)cells(SNU-790,TPC-1),poorly differentiated thyroid cancer cell(BCPAP),and anaplastic thyroid cancer cell(SNU-80)—were cultured under simulated microgravity(10-3 g)using a clinostat.Differentially expressed genes(DEGs)were analyzed using cDNA microarray,followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.Results:DEG analysis under simulated microgravity revealed distinct gene expression profiles by gravity condition,with 2980 DEGs in SNU-790,1033 in BCPAP,562 in TPC-1,477 in Nthy-ori 3-1,and 246 in SNU-80,as confirmed by hierarchical clustering.In PTC cell lines(SNU-790,TPC-1),G2–M phase–related genes were upregulated.In non-PTC cell lines(BCPAP,SNU-80),genes associated with innate immune response,Toll-like receptor signaling,were upregulated,whereas Hypoxia-Inducible Factor 1-alpha(HIF-1α)signaling-related genes were downregulated.Additionally,under simulated microgravity,significant migration was observed in SNU-790(3×104 cells)and BCPAP(2×104 and 3×104),while significant invasion occurred in SNU-790,Nthy-ori 3-1,and BCPAP at a seeding density of 2×104.Other conditions showed no significant differences.Conclusion:This study comprehensively evaluates the effects of simulated microgravity using a diverse panel of thyroid-related cell lines.Thesefindings provide valuable insight into how microgravity could influence cancer biology,emphasizing the importance of further research on cancer behavior in space environments and its implications for human health during long-term space missions.
基金supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
文摘With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task.At present,the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently.Therefore,we propose an Ontology Matching Method Based on the Gated Graph Attention Model(OM-GGAT).Firstly,the semantic knowledge related to concepts in the ontology is encoded into vectors using the OWL2Vec^(*)method,and the relevant path information from the root node to the concept is embedded to understand better the true meaning of the concept itself and the relationship between concepts.Secondly,the ontology is transformed into the corresponding graph structure according to the semantic relation.Then,when extracting the features of the ontology graph nodes,different attention weights are assigned to each adjacent node of the central concept with the help of the attention mechanism idea.Finally,gated networks are designed to further fuse semantic and structural embedding representations efficiently.To verify the effectiveness of the proposed method,comparative experiments on matching tasks were carried out on public datasets.The results show that the OM-GGAT model can effectively improve the efficiency of ontology matching.
