Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.
{"title":"MEC Network Resource Allocation Strategy Based on Improved PSO in 5G Communication Network","authors":"Yu Chen","doi":"10.4018/ijswis.328526","DOIUrl":"https://doi.org/10.4018/ijswis.328526","url":null,"abstract":"Relying on features such as high-speed, low latency, support for cutting-edge technology, internet of things, and multimodality, 5G networks will greatly contribute to the transformation of Web 3.0. In order to realize low-latency and high-speed information exchange in 5G communication networks, a method based on the allocation of network computing resource in view of edge computing model is proposed. The method first considers three computing modes: local device computing, local mobile edge computing (MEC) server computing, and adjacent MEC server computing. Then, a multi-scenario edge computing model is further constructed for optimizing energy consumption and delay. At the same time, the encoding-decoding mode is used to optimize PSO algorithm and combined with the improvement of fitness function, which can effectively support the communication network to achieve reasonable allocation of resources, ensuring efficiency of information exchange in the network. In the end, the results show that when the number of users is 500, the method can complete the task assignment within 44s.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"85 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88961737","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}
With the rapid development of computer and information technology, people can obtain and store data in a faster and cheaper way, which makes the amount of data and information grow exponentially. Based on data mining technology, this paper systematically analyzes and compares the basic situation and evolution of international trade networks and international human relations networks using the bilateral trade data and human relations data of countries and regions in the world. The research shows that the strength external entropy, strength internal entropy, and network weight entropy of the whole international trade network from 2008 to 2014 are generally lower than 0.75. The whole network still showed a downward trend from 2010 to 2015, but it began to rise steadily after 2016. The monopolistic behavior in international technology trade has a significant impact on the fundamental freedoms and rights of citizens.
{"title":"Analysis on the Legal System of International Technology Trade Management Based on Data Mining Analysis","authors":"Xiangbin Zuo, Yi Yang","doi":"10.4018/ijswis.328528","DOIUrl":"https://doi.org/10.4018/ijswis.328528","url":null,"abstract":"With the rapid development of computer and information technology, people can obtain and store data in a faster and cheaper way, which makes the amount of data and information grow exponentially. Based on data mining technology, this paper systematically analyzes and compares the basic situation and evolution of international trade networks and international human relations networks using the bilateral trade data and human relations data of countries and regions in the world. The research shows that the strength external entropy, strength internal entropy, and network weight entropy of the whole international trade network from 2008 to 2014 are generally lower than 0.75. The whole network still showed a downward trend from 2010 to 2015, but it began to rise steadily after 2016. The monopolistic behavior in international technology trade has a significant impact on the fundamental freedoms and rights of citizens.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"32 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85822802","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}
This paper explores the potential of utilizing semantic web technologies to improve class management in Chinese schools. By analyzing a comprehensive dataset obtained from the Scopus database, the study investigates publication trends, document types, keyword distributions, and author contributions in the field of semantic web technologies for class management. The findings reveal a growing interest in this research area and highlight the benefits of semantic web technologies in personalized learning, information retrieval, collaboration, and assessment. The paper discusses the practical implications, challenges, and considerations for implementing semantic web technologies in Chinese schools. It aims to provide valuable insights for educators, researchers, policymakers, and educational technology practitioners interested in enhancing class management practices through the innovative use of semantic web technologies.
{"title":"Enhancing Class Management in Chinese Schools Through Semantic Web Technologies","authors":"Jing Li, Akshat Gaurav, Kwok Tai Chui","doi":"10.4018/ijswis.328527","DOIUrl":"https://doi.org/10.4018/ijswis.328527","url":null,"abstract":"This paper explores the potential of utilizing semantic web technologies to improve class management in Chinese schools. By analyzing a comprehensive dataset obtained from the Scopus database, the study investigates publication trends, document types, keyword distributions, and author contributions in the field of semantic web technologies for class management. The findings reveal a growing interest in this research area and highlight the benefits of semantic web technologies in personalized learning, information retrieval, collaboration, and assessment. The paper discusses the practical implications, challenges, and considerations for implementing semantic web technologies in Chinese schools. It aims to provide valuable insights for educators, researchers, policymakers, and educational technology practitioners interested in enhancing class management practices through the innovative use of semantic web technologies.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"77 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82182502","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 danger of distributed denial of service (DDoS) attacks has grown in tandem with the proliferation of intelligent information systems. Because of the sheer volume of connected devices, constantly shifting network circumstances, and the need for instantaneous reaction, conventional DDoS detection methods are inadequate for the IoT. In this context, this study aims to survey the current state of the art in the topic by reading relevant articles found in the Scopus database, with a brief overview of the IoT and DDoS as this study examines neural networks and their applicability to DDoS detection. Finally, a decision tree-based model is developed for the detection of DDoS attacks. The analysis sheds light on the present trends and issues in this field and suggests avenues for further study.
{"title":"Machine Learning-Based Distributed Denial of Services (DDoS) Attack Detection in Intelligent Information Systems","authors":"Wadee Alhalabi, Akshat Gaurav, Varsha Arya, I. Zamzami, Rania Anwar Aboalela","doi":"10.4018/ijswis.327280","DOIUrl":"https://doi.org/10.4018/ijswis.327280","url":null,"abstract":"The danger of distributed denial of service (DDoS) attacks has grown in tandem with the proliferation of intelligent information systems. Because of the sheer volume of connected devices, constantly shifting network circumstances, and the need for instantaneous reaction, conventional DDoS detection methods are inadequate for the IoT. In this context, this study aims to survey the current state of the art in the topic by reading relevant articles found in the Scopus database, with a brief overview of the IoT and DDoS as this study examines neural networks and their applicability to DDoS detection. Finally, a decision tree-based model is developed for the detection of DDoS attacks. The analysis sheds light on the present trends and issues in this field and suggests avenues for further study.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"86 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75894654","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 recent years, knowledge-aware recommendation systems have gained popularity as a solution to address the challenges of data sparsity and cold start in collaborative filtering. However, traditional knowledge graph convolutional networks impose significant computational burdens during training, demanding substantial resources and increasing the cost of recommendations. To address this issue, this article proposes a lightweight knowledge graph convolutional network for collaborative filtering (LKGCF). LKGCF eliminates the feature transformation and nonlinear activation components, by focusing on essential elements such as neighborhood aggregation and layer combination. LKGCF captures the user's long-distance personalized interests on the knowledge graph by sampling from neighborhood information and constructing a weighted sum of item embeddings. Experimental results demonstrate that the proposed model is easy to train and implement due to its coherence and simplicity. Furthermore, notable improvements in recommendation performance are observed compared to strong baselines.
{"title":"A Lightweight Method of Knowledge Graph Convolution Network for Collaborative Filtering","authors":"X. Zhang, Shaohua Kuang","doi":"10.4018/ijswis.327353","DOIUrl":"https://doi.org/10.4018/ijswis.327353","url":null,"abstract":"In recent years, knowledge-aware recommendation systems have gained popularity as a solution to address the challenges of data sparsity and cold start in collaborative filtering. However, traditional knowledge graph convolutional networks impose significant computational burdens during training, demanding substantial resources and increasing the cost of recommendations. To address this issue, this article proposes a lightweight knowledge graph convolutional network for collaborative filtering (LKGCF). LKGCF eliminates the feature transformation and nonlinear activation components, by focusing on essential elements such as neighborhood aggregation and layer combination. LKGCF captures the user's long-distance personalized interests on the knowledge graph by sampling from neighborhood information and constructing a weighted sum of item embeddings. Experimental results demonstrate that the proposed model is easy to train and implement due to its coherence and simplicity. Furthermore, notable improvements in recommendation performance are observed compared to strong baselines.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"16 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87364187","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}
Yu Nie, Na Huang, Junjie Peng, Guanghua Song, Yilai Zhang, Yongkang Peng, Chenglin Ni
There are problems of knowledge deficiency and effective unified expression of knowledge in the process of relevant knowledge data acquired by workers in the ceramic domain. In this study, the authors designed relevant experiments to construct ceramic field knowledge graphs to solve these problems. In the experiments of named entity recognition and relationship recognition, the authors compared the performance of several models in OwnThink and ceramics field datasets. The experimental results showed that the BiLSTM-CRF model is the best for named entity recognition and the TextCNN model is the best for relationship recognition in ceramics field datasets. Therefore, the first used the BiLSTM-CRF model to complete the naming entity recognition and then combined with the TextCNN model to complete the relationship recognition to construct the ceramic field knowledge graph. Then, they applied the constructed graph to the ceramic knowledge Q&A service to provide accurate data retrieval service for ceramic domain workers.
{"title":"Research on the Construction and Application of Knowledge Graph in the Ceramic Field Based on Natural Language Processing","authors":"Yu Nie, Na Huang, Junjie Peng, Guanghua Song, Yilai Zhang, Yongkang Peng, Chenglin Ni","doi":"10.4018/ijswis.327352","DOIUrl":"https://doi.org/10.4018/ijswis.327352","url":null,"abstract":"There are problems of knowledge deficiency and effective unified expression of knowledge in the process of relevant knowledge data acquired by workers in the ceramic domain. In this study, the authors designed relevant experiments to construct ceramic field knowledge graphs to solve these problems. In the experiments of named entity recognition and relationship recognition, the authors compared the performance of several models in OwnThink and ceramics field datasets. The experimental results showed that the BiLSTM-CRF model is the best for named entity recognition and the TextCNN model is the best for relationship recognition in ceramics field datasets. Therefore, the first used the BiLSTM-CRF model to complete the naming entity recognition and then combined with the TextCNN model to complete the relationship recognition to construct the ceramic field knowledge graph. Then, they applied the constructed graph to the ceramic knowledge Q&A service to provide accurate data retrieval service for ceramic domain workers.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"19 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85161314","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}
Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.
{"title":"Constructing a Knowledge Graph for the Chinese Subject Based on Collective Intelligence","authors":"Guozhu Ding, Peiying Yi, Xinru Feng","doi":"10.4018/ijswis.327355","DOIUrl":"https://doi.org/10.4018/ijswis.327355","url":null,"abstract":"Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"40 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79234533","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}
Effective extraction of patent technology points in new energy fields is profitable, which motivates technological innovation and facilitates patent transformation and application. However, since patent data exists the ununiform distribution of technology points information, long length of term, and long sentences, technology point extraction faces the dilemmas of poor readability and logic confusion. To mitigate these problems, the article proposes a method to generate patent technology points called IGPTP—a two-stage strategy, which fuses the advantage of extractive and generative ways. IGPTP utilizes the RoBERTa+CNN model to obtain the key sentences of text and takes the output as input of UNILM (unified pre-trained language model). Simultaneously, it takes a multi-strategies integration technique to enhance the quality of patent technology points by combining the copy mechanism and external knowledge guidance model. Substantial experimental results manifest that IGPTP outperforms the current mainstream models, which can generate more coherent and richer text.
{"title":"Research on the Generation of Patented Technology Points in New Energy Based on Deep Learning","authors":"Haixiang Yang, Xindong You, Xueqiang Lv, Ge Xu","doi":"10.4018/ijswis.327354","DOIUrl":"https://doi.org/10.4018/ijswis.327354","url":null,"abstract":"Effective extraction of patent technology points in new energy fields is profitable, which motivates technological innovation and facilitates patent transformation and application. However, since patent data exists the ununiform distribution of technology points information, long length of term, and long sentences, technology point extraction faces the dilemmas of poor readability and logic confusion. To mitigate these problems, the article proposes a method to generate patent technology points called IGPTP—a two-stage strategy, which fuses the advantage of extractive and generative ways. IGPTP utilizes the RoBERTa+CNN model to obtain the key sentences of text and takes the output as input of UNILM (unified pre-trained language model). Simultaneously, it takes a multi-strategies integration technique to enhance the quality of patent technology points by combining the copy mechanism and external knowledge guidance model. Substantial experimental results manifest that IGPTP outperforms the current mainstream models, which can generate more coherent and richer text.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"19 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84436582","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 emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.
{"title":"A Machine Learning Technique for Detection of Social Media Fake News","authors":"M. Arowolo, S. Misra, R. Ogundokun","doi":"10.4018/ijswis.326120","DOIUrl":"https://doi.org/10.4018/ijswis.326120","url":null,"abstract":"The emergence of the Internet and the growing development of online platforms (like Facebook and Instagram) opened the way for disseminating information that hasn't been experienced in the history of mankind earlier. Consumers generate and share more information and a massive amount of data than ever with the growing utilization of social media sites, many of which are deceptive with little relevance to reality. A daunting task is the automated classification of a text article as misleading or misinformation. To see the latest news alerts, individuals often utilize e-newspapers, Twitter, Instagram, Youtube, and many more. Fake news created on social media can lead to uncertainty amongst individuals and psychiatric illness. We may detect that news obtained based on machine learning techniques is either true or false. This study proposes a machine learning technique to detect fake news by carrying out filtration on social media data, classifying the preprocessed data using a machine learning algorithm, evaluating the developed system, and evaluating the results.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"62 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84766725","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}
After years of development, Hainan Province has established a significant sports tourism industry. This paper explores how digitalization is driving the transformation of traditional sports tourism through the utilization of the DRAT model to reconstruct the value of both consumers and businesses. The analysis shows that the digitalization of sports tourism has deconstructed the traditional industry using innovative technologies such as technological virtualization and data platform construction. The effective development and utilization of highly developed information technology in modern society are essential for ensuring the smooth and healthy growth of the sports tourism industry. The traditional sports tourism industry has been digitally deconstructed, leading to the formation of new production, management, and business models. The status of consumers continues to improve during this process, and companies are seeking to collaborate with consumers to create new value positioning.
{"title":"Analysis on the Development Strategy of Hainan's Sports Tourism Informatization in the Digital Era","authors":"Y. Zhang","doi":"10.4018/ijswis.325788","DOIUrl":"https://doi.org/10.4018/ijswis.325788","url":null,"abstract":"After years of development, Hainan Province has established a significant sports tourism industry. This paper explores how digitalization is driving the transformation of traditional sports tourism through the utilization of the DRAT model to reconstruct the value of both consumers and businesses. The analysis shows that the digitalization of sports tourism has deconstructed the traditional industry using innovative technologies such as technological virtualization and data platform construction. The effective development and utilization of highly developed information technology in modern society are essential for ensuring the smooth and healthy growth of the sports tourism industry. The traditional sports tourism industry has been digitally deconstructed, leading to the formation of new production, management, and business models. The status of consumers continues to improve during this process, and companies are seeking to collaborate with consumers to create new value positioning.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"15 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82076454","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：