Aerial image target detection is a challenging task due to the complex backgrounds, dense target distribution, and large-scale differences often present in aerial images. Existing methods often struggle to effectively extract detailed features and address the issue of imbalanced positive and negative samples. To tackle these challenges, an aerial image target detection method (dense RFB-FE-CGAM) based on dense RFB-FE and channel-global attention mechanism (CGAM) was proposed. First, the authors design a shallow feature enhancement module using dense RFB feature multiplexing and expand convolution within an SSD network, improving detailed feature extraction. Second, they introduce CGAM, a global attention module, to enhance semantic feature extraction in backbone networks. Finally, they incorporate a focal loss function for joint training, addressing sample imbalance. In experiments, the method achieved an mAP of 0.755 on the DOTA dataset and recall/AP values of 0.889/0.906 on HRSC2016, confirming the effectiveness of dense RFB-FE-CGAM for aerial image target detection.
{"title":"A Semantic Feature Enhancement-Based Aerial Image Target Detection Method Using Dense RFB-FE","authors":"Xinyang Li, Jingguo Zhang","doi":"10.4018/ijswis.331083","DOIUrl":"https://doi.org/10.4018/ijswis.331083","url":null,"abstract":"Aerial image target detection is a challenging task due to the complex backgrounds, dense target distribution, and large-scale differences often present in aerial images. Existing methods often struggle to effectively extract detailed features and address the issue of imbalanced positive and negative samples. To tackle these challenges, an aerial image target detection method (dense RFB-FE-CGAM) based on dense RFB-FE and channel-global attention mechanism (CGAM) was proposed. First, the authors design a shallow feature enhancement module using dense RFB feature multiplexing and expand convolution within an SSD network, improving detailed feature extraction. Second, they introduce CGAM, a global attention module, to enhance semantic feature extraction in backbone networks. Finally, they incorporate a focal loss function for joint training, addressing sample imbalance. In experiments, the method achieved an mAP of 0.755 on the DOTA dataset and recall/AP values of 0.889/0.906 on HRSC2016, confirming the effectiveness of dense RFB-FE-CGAM for aerial image target detection.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to address the drawbacks of semantic ambiguity, inaccurate quantifiers, and low translation accuracy in traditional grammar-based translation methods, this paper proposes an artificial intelligence translation method based on semantic analysis for English fuzzy semantics. Firstly, a comprehensive analysis of English language semantics was carried out from different semantic levels such as language, knowledge, and pragmatics, and the key points of fuzzy semantics were identified. Then, key feature quantities for accurate translation of fuzzy semantics in English vocabulary and literature were constructed, and artificial intelligence methods were used to optimize fuzzy semantics. The experimental results show that the proposed method can avoid semantic understanding ambiguity and improve the accuracy of English language translation.
{"title":"Artificial Intelligence Method for Accurate Translation of Fuzzy Semantics in English Language and Literature","authors":"Ying Sun","doi":"10.4018/ijswis.331033","DOIUrl":"https://doi.org/10.4018/ijswis.331033","url":null,"abstract":"In order to address the drawbacks of semantic ambiguity, inaccurate quantifiers, and low translation accuracy in traditional grammar-based translation methods, this paper proposes an artificial intelligence translation method based on semantic analysis for English fuzzy semantics. Firstly, a comprehensive analysis of English language semantics was carried out from different semantic levels such as language, knowledge, and pragmatics, and the key points of fuzzy semantics were identified. Then, key feature quantities for accurate translation of fuzzy semantics in English vocabulary and literature were constructed, and artificial intelligence methods were used to optimize fuzzy semantics. The experimental results show that the proposed method can avoid semantic understanding ambiguity and improve the accuracy of English language translation.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135535985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Semantic web-based video surveillance systems can provide strong decision-making support for managers, and they have high requirements for real-time and precision of vehicle detection models in complex night scenes. To address this issue, a lightweight nighttime vehicle detection method (MC-YOLO) integrating MobileNetV2 and YOLOV3 is proposed. Firstly, in the preprocessing stage, image enhancement is performed on nighttime images to facilitate model feature extraction. Then, the lightweight network MobileNetV2 is used to extract feature by replacing the backbone network DarkNet53 in YOLOv3, thus accelerating the speed of target detection. Finally, after the convolution operation of the backbone network, a convolution block attention module is added to enhance the important feature information and suppress the secondary features, thereby improving the detection precision. The experimental results on the BDD100K dataset show that the proposed MC-YOLO model has a precision of up to 92.75%, which is superior to several other advanced comparative models.
{"title":"MC-YOLO-Based Lightweight Detection Method for Nighttime Vehicle Images in a Semantic Web-Based Video Surveillance System","authors":"Xiaofeng Wang, Xiao Hao, Kun Wang","doi":"10.4018/ijswis.330752","DOIUrl":"https://doi.org/10.4018/ijswis.330752","url":null,"abstract":"Semantic web-based video surveillance systems can provide strong decision-making support for managers, and they have high requirements for real-time and precision of vehicle detection models in complex night scenes. To address this issue, a lightweight nighttime vehicle detection method (MC-YOLO) integrating MobileNetV2 and YOLOV3 is proposed. Firstly, in the preprocessing stage, image enhancement is performed on nighttime images to facilitate model feature extraction. Then, the lightweight network MobileNetV2 is used to extract feature by replacing the backbone network DarkNet53 in YOLOv3, thus accelerating the speed of target detection. Finally, after the convolution operation of the backbone network, a convolution block attention module is added to enhance the important feature information and suppress the secondary features, thereby improving the detection precision. The experimental results on the BDD100K dataset show that the proposed MC-YOLO model has a precision of up to 92.75%, which is superior to several other advanced comparative models.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.
{"title":"Web Semantic-Based MOOP Algorithm for Facilitating Allocation Problems in the Supply Chain Domain","authors":"Chun-Yuan Lin, Mosiur Rahaman, Massoud Moslehpour, Sourasis Chattopadhyay, Varsha Arya","doi":"10.4018/ijswis.330250","DOIUrl":"https://doi.org/10.4018/ijswis.330250","url":null,"abstract":"The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135487458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In location-based social networks (LBSN), users can check-in at points of interest (POI) to record their trips. POI recommendation is an important service provided by LBSN; it can help users quickly find POI of interest, and also help POI providers more comprehensively understand user preferences and improve service quality. This paper proposes a POI recommendation algorithm that is based on attention mechanism. The sequence characteristics and short-term preferences of historical data are captured through the attention mechanism module and long short-term memory network (LSTM), and the POI location prediction is carried out in combination with the user embedding characteristics, and a better prediction accuracy is obtained. These results simulated show that the proposed method can realize the reliable analysis of complex data sets, and its precision index remains above 0.1 and recall index remains above 0.08, and it can also alleviate the cold start problem and better meet the personalized needs of users.
{"title":"A POI Recommendation Model for Intelligent Systems Using AT-LSTM in Location-Based Social Network Big Data","authors":"Yiqiang Lai, Xianfeng Zeng","doi":"10.4018/ijswis.330246","DOIUrl":"https://doi.org/10.4018/ijswis.330246","url":null,"abstract":"In location-based social networks (LBSN), users can check-in at points of interest (POI) to record their trips. POI recommendation is an important service provided by LBSN; it can help users quickly find POI of interest, and also help POI providers more comprehensively understand user preferences and improve service quality. This paper proposes a POI recommendation algorithm that is based on attention mechanism. The sequence characteristics and short-term preferences of historical data are captured through the attention mechanism module and long short-term memory network (LSTM), and the POI location prediction is carried out in combination with the user embedding characteristics, and a better prediction accuracy is obtained. These results simulated show that the proposed method can realize the reliable analysis of complex data sets, and its precision index remains above 0.1 and recall index remains above 0.08, and it can also alleviate the cold start problem and better meet the personalized needs of users.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liang Sun, Pengsheng Wang, Paiying Liu, Zhengang Nie
Unmanned motion platforms are being used in a wide range of industries. Unmanned motion platforms must have an autonomous and intelligent navigation procedure in order to carry out their system functions. Traditional inertial navigation and radio navigation have poor accuracy and autonomy when not dependent on satellite circumstances. The accuracy of image recognition algorithms must meet strict standards. This study and exploration of the high-precision scene image recognition system is based on convolutional neural network structure optimization. To demonstrate the viability of the approach, simulation experiments are carried out on the NUC dataset using the recognition technique based on a convolutional neural network that is proposed. The fundamental network architecture of a convolutional neural network is optimized using the L2 regularization technique. The experimental findings demonstrate that the NUC dataset now has better recognition accuracy. In terms of recognition accuracy, the suggested method satisfies the predetermined requirements.
{"title":"Image Processing Method of a Visual Communication System Based on Convolutional Neural Network","authors":"Liang Sun, Pengsheng Wang, Paiying Liu, Zhengang Nie","doi":"10.4018/ijswis.330022","DOIUrl":"https://doi.org/10.4018/ijswis.330022","url":null,"abstract":"Unmanned motion platforms are being used in a wide range of industries. Unmanned motion platforms must have an autonomous and intelligent navigation procedure in order to carry out their system functions. Traditional inertial navigation and radio navigation have poor accuracy and autonomy when not dependent on satellite circumstances. The accuracy of image recognition algorithms must meet strict standards. This study and exploration of the high-precision scene image recognition system is based on convolutional neural network structure optimization. To demonstrate the viability of the approach, simulation experiments are carried out on the NUC dataset using the recognition technique based on a convolutional neural network that is proposed. The fundamental network architecture of a convolutional neural network is optimized using the L2 regularization technique. The experimental findings demonstrate that the NUC dataset now has better recognition accuracy. In terms of recognition accuracy, the suggested method satisfies the predetermined requirements.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135982264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to enhance the utility of online educational digital resources, the authors propose a practical and efficient multi-strategy relation extraction (RE) model in online education scenarios. First, the effective relation discrimination model is used to make relation predictions for non-structured teaching resources and eliminate the noise data. Then, they extract relations from different path strategies using multiple low-computational resources and efficient relation extraction strategies and use their proposed multi-strategy weighting calculator to weigh the relation extraction strategies to derive the final target relations. To cope with the low-resource relation extraction scenario, the relation extraction results are complemented by using prompt learning with a big model paradigm. They also consider the model to serve the commercial scenario of online education, and they propose a global rate controller to adjust and adapt the rate and throughput requirements in different scenarios, so as to achieve the best balance of system stability, computation speed, and extraction performance.
{"title":"MusREL","authors":"Zhen Zhu, Huaiyuan Lin, Dongmei Gu, Liting Wang, Hong Wu, Yun Fang","doi":"10.4018/ijswis.329965","DOIUrl":"https://doi.org/10.4018/ijswis.329965","url":null,"abstract":"In order to enhance the utility of online educational digital resources, the authors propose a practical and efficient multi-strategy relation extraction (RE) model in online education scenarios. First, the effective relation discrimination model is used to make relation predictions for non-structured teaching resources and eliminate the noise data. Then, they extract relations from different path strategies using multiple low-computational resources and efficient relation extraction strategies and use their proposed multi-strategy weighting calculator to weigh the relation extraction strategies to derive the final target relations. To cope with the low-resource relation extraction scenario, the relation extraction results are complemented by using prompt learning with a big model paradigm. They also consider the model to serve the commercial scenario of online education, and they propose a global rate controller to adjust and adapt the rate and throughput requirements in different scenarios, so as to achieve the best balance of system stability, computation speed, and extraction performance.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"15 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89891962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the domain of target detection in mobile and embedded devices, neural network model inference speed is a crucial metric. This paper introduces YOLO-FLNet, a lightweight algorithm for detecting people in open scenes. The model utilizes the DFEM structure to capture and process high-frequency and low-frequency information in the feature map. Additionally, the VoV-DFEM structure, based on the concept of one-shot aggregation, enhances feature aggregation from different scales and frequencies in the backbone network. To validate its performance, experiments were conducted using publicly available datasets on a computer with dedicated GPUs. As a result, compared to YOLOv7-tiny, YOLO-FLNet achieved a 0.3% mAP@0.5 improvement, reduced parameter size by 52.9%, and increased inference speed by 30.2%. These characteristics make it valuable for person detection in engineering domains, providing theoretical guidance for lightweight models in edge computing.
{"title":"A Lightweight Real-Time System for Object Detection in Enterprise Information Systems for Frequency-Based Feature Separation","authors":"YiHeng Wu, JianXin Chen","doi":"10.4018/ijswis.330015","DOIUrl":"https://doi.org/10.4018/ijswis.330015","url":null,"abstract":"In the domain of target detection in mobile and embedded devices, neural network model inference speed is a crucial metric. This paper introduces YOLO-FLNet, a lightweight algorithm for detecting people in open scenes. The model utilizes the DFEM structure to capture and process high-frequency and low-frequency information in the feature map. Additionally, the VoV-DFEM structure, based on the concept of one-shot aggregation, enhances feature aggregation from different scales and frequencies in the backbone network. To validate its performance, experiments were conducted using publicly available datasets on a computer with dedicated GPUs. As a result, compared to YOLOv7-tiny, YOLO-FLNet achieved a 0.3% mAP@0.5 improvement, reduced parameter size by 52.9%, and increased inference speed by 30.2%. These characteristics make it valuable for person detection in engineering domains, providing theoretical guidance for lightweight models in edge computing.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"69 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84354666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qiuying Li, Xiao-Dan Li, Kwok Tai Chui, Varsha Arya
This paper delves into the dynamic intersection of athletic psychology and emerging technologies, aiming to understand their interplay and implications for sports performance. The study examines the latest research and literature in this field, encompassing the use of social media, digital devices, and virtual reality as technological advancements. It explores the impact of these technologies on athlete psychology, mental resilience, motivation, and goal setting. By analyzing country-specific scientific production, author contributions, and keyword trends, the paper provides insights into the global landscape of research in athletic psychology and emerging technologies. The findings contribute to a better understanding of the evolving relationship between technology and athlete psychology, offering potential avenues for optimizing performance, mental well-being, and training strategies in the realm of sports.
{"title":"Exploring the Intersection of Athletic Psychology and Emerging Technologies","authors":"Qiuying Li, Xiao-Dan Li, Kwok Tai Chui, Varsha Arya","doi":"10.4018/ijswis.329168","DOIUrl":"https://doi.org/10.4018/ijswis.329168","url":null,"abstract":"This paper delves into the dynamic intersection of athletic psychology and emerging technologies, aiming to understand their interplay and implications for sports performance. The study examines the latest research and literature in this field, encompassing the use of social media, digital devices, and virtual reality as technological advancements. It explores the impact of these technologies on athlete psychology, mental resilience, motivation, and goal setting. By analyzing country-specific scientific production, author contributions, and keyword trends, the paper provides insights into the global landscape of research in athletic psychology and emerging technologies. The findings contribute to a better understanding of the evolving relationship between technology and athlete psychology, offering potential avenues for optimizing performance, mental well-being, and training strategies in the realm of sports.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"77 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82577817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The topic about consensus target track seeking for high-order nonlinear multi-agent systems (MASs) with unmodeled dynamics, dynamic disturbances, and dead-zone input is considered in the paper. Using the strong nonlinear map characteristic of radial basis function neural networks (RBFNNs), the complex functions arising from recursive procedure are simplified. Also, inspired by input-to-state practical stability (ISpS), the authors construct a dynamical signal in order to counteract the impact of unmodeled dynamics and dynamic disturbances. The bounded inequality expression has been applied to tackle the unknown input of dead zone. Based on this, consensus control protocol suitable for nonlinear constraints has been constructed by using the recursive backstepping technique and adaptive neural network method. Theoretical analysis indicates not only the uniform boundary of all signals in the closed-loop under the neuro-based consensus controller, but uniform ultimate convergence of consensus tracking errors. The final simulations also confirmed the correctness of the theoretical analysis.
{"title":"Neuro-Based Consensus Seeking for Nonlinear Uncertainty Multi-Agent Systems Constrained by Dead-Zone Input","authors":"Zhenhua Qin, Rongjun Gai","doi":"10.4018/ijswis.328767","DOIUrl":"https://doi.org/10.4018/ijswis.328767","url":null,"abstract":"The topic about consensus target track seeking for high-order nonlinear multi-agent systems (MASs) with unmodeled dynamics, dynamic disturbances, and dead-zone input is considered in the paper. Using the strong nonlinear map characteristic of radial basis function neural networks (RBFNNs), the complex functions arising from recursive procedure are simplified. Also, inspired by input-to-state practical stability (ISpS), the authors construct a dynamical signal in order to counteract the impact of unmodeled dynamics and dynamic disturbances. The bounded inequality expression has been applied to tackle the unknown input of dead zone. Based on this, consensus control protocol suitable for nonlinear constraints has been constructed by using the recursive backstepping technique and adaptive neural network method. Theoretical analysis indicates not only the uniform boundary of all signals in the closed-loop under the neuro-based consensus controller, but uniform ultimate convergence of consensus tracking errors. The final simulations also confirmed the correctness of the theoretical analysis.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"20 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83336325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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