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Semantic Trajectory Frequent Pattern Mining Model - The definitions and theorems 语义轨迹频繁模式挖掘模型-定义和定理
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.297031
{"title":"Semantic Trajectory Frequent Pattern Mining Model - The definitions and theorems","authors":"","doi":"10.4018/ijswis.297031","DOIUrl":"https://doi.org/10.4018/ijswis.297031","url":null,"abstract":"A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the Top-k candidate frequent location item sets, and then the spatio-temporal sequence intersection and the divide and conquer merge methods are used to convert the frequent iterative calculation of long itemsets into hierarchical sets' regular operations, the superset and subset of frequent sequences are found. This kind of semantic trajectory frequent pattern mining can actively identify and discover potential carpooling needs, and provide higher accuracy for location-based intelligent recommendations such as carpooling and HOV lane travel (High-Occupancy Vehicle Lane). Carpool matching and recommendation based on semantic trajectory in this paper is suitable for single carpooling and relay-ride carpooling. the results of simulation carpooling experiments prove the applicability and efficiency of the method.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"27 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74325881","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}
引用次数: 0
An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System 基于归一化互信息抗体特征选择和自适应量子人工免疫系统的入侵检测系统
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.308469
Zhang Ling, Zhang Jia Hao
{"title":"An Intrusion Detection System Based on Normalized Mutual Information Antibodies Feature Selection and Adaptive Quantum Artificial Immune System","authors":"Zhang Ling, Zhang Jia Hao","doi":"10.4018/ijswis.308469","DOIUrl":"https://doi.org/10.4018/ijswis.308469","url":null,"abstract":"The intrusion detection system (IDS) has lower speed, less adaptability and lower detection accuracy especially for small samples sets. This paper presents a detection model based on normalized mutual antibodies information feature selection and adaptive quantum artificial immune with cooperative evolution of multiple operators (NMAIFS MOP-AQAI). First, for a high intrusion speed, the NMAIFS is used to achieve an effective reduction for high-dimensional features. Then, the best feature vectors are sent to the MOP-AQAI classifier, in which, vaccination strategy, the quantum computing, and cooperative evolution of multiple operators are adopted to generate excellent detectors. Lastly, the data is fed into NMAIFS MOP-AQAI and ultimately generates accurate detection results. The experimental results on real abnormal data demonstrate that the NMAIFS MOP-AQAI has higher detection accuracy, lower false negative rate and a higher adaptive performance than the existing anomaly detection methods, especially for small samples sets.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"14 1","pages":"1-25"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89502083","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}
引用次数: 9
Spatial Patterns and Development Characteristics of China's Postgraduate Education 中国研究生教育的空间格局与发展特征
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.313190
P. Li, Haidong Zhong, J. Zhang
{"title":"Spatial Patterns and Development Characteristics of China's Postgraduate Education","authors":"P. Li, Haidong Zhong, J. Zhang","doi":"10.4018/ijswis.313190","DOIUrl":"https://doi.org/10.4018/ijswis.313190","url":null,"abstract":"Using four types of publicly available datasets and ArcGIS software, the authors identify the spatial characteristics of postgraduate education in China at three scales: comprehensive economic zone, provincial, and city. They also employ geographically weighted regression and ordinary least squares to study the factors influencing the spatial pattern of postgraduate education in Gin at the city scale. The findings show that the number of postgraduate education institutions increases as the longitude of a city increases, but the number decreases from coast to inland. Second, postgraduate education institutions tend to group together in provincial capitals and megacities. Finally, GDP, per capita GDP, population size, local income, and total retail sales of consumer goods significantly impact postgraduate education development. The study contributes to the literature and provides insights for practitioners in promoting urban planning and infrastructure development.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"25 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74871087","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}
引用次数: 6
Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media 基于语境Word2Vec模型的在线社交媒体词汇外汉语理解
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.309428
Jiakai Gu, Gen Li, Nam D. Vo, Jason J. Jung
{"title":"Contextual Word2Vec Model for Understanding Chinese Out of Vocabularies on Online Social Media","authors":"Jiakai Gu, Gen Li, Nam D. Vo, Jason J. Jung","doi":"10.4018/ijswis.309428","DOIUrl":"https://doi.org/10.4018/ijswis.309428","url":null,"abstract":"In this chapter, the authors propose to use contextual Word2Vec model for understanding OOV (out of vocabulary). The OOV is extracted by using left-right entropy and point information entropy. They choose to use Word2Vec to construct the word vector space and CBOW (continuous bag of words) to obtain the contextual information of the words. If there is a word that has similar contextual information to the OOV, the word can be used to understand the OOV. They chose the Weibo corpus as the dataset for the experiments. The results show that the proposed model achieves 97.10% accuracy, which is better than Skip-Gram by 8.53%.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"36 1","pages":"1-14"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83955888","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}
引用次数: 5
Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching 基于自编码器的深度嵌入学习大规模本体匹配
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.297042
Meriem Ali Khoudja, Messaouda Fareh, Hafida Bouarfa
{"title":"Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology Matching","authors":"Meriem Ali Khoudja, Messaouda Fareh, Hafida Bouarfa","doi":"10.4018/ijswis.297042","DOIUrl":"https://doi.org/10.4018/ijswis.297042","url":null,"abstract":"Ontology matching is an efficient method to establish interoperability among heterogeneous ontologies. Large-scale ontology matching still remains a big challenge for its long time and large memory space consumption. The actual solution to this problem is ontology partitioning which is also challenging. This paper presents DeepOM, an ontology matching system to deal with this large-scale heterogeneity problem without partitioning using deep learning techniques. It consists on creating semantic embeddings for concepts of input ontologies using a reference ontology, and use them to train an auto-encoder in order to learn more accurate and less dimensional representations for concepts. The experimental results of its evaluation on large ontologies, and its comparison with different ontology matching systems which have participated to the same test challenge, are very encouraging with a precision score of 0.99. They demonstrate the higher efficiency of the proposed system to increase the performance of the large-scale ontology matching task.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"155 1","pages":"1-18"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86297724","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}
引用次数: 2
Integration and Open Access System Based on Semantic Technologies 基于语义技术的集成与开放存取系统
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.309422
A. F. García, Maria Isabel Manzano García, Roberto Berjón Gallinas, Montserrat Mateos Sánchez, M. E. B. Gutiérrez
{"title":"Integration and Open Access System Based on Semantic Technologies","authors":"A. F. García, Maria Isabel Manzano García, Roberto Berjón Gallinas, Montserrat Mateos Sánchez, M. E. B. Gutiérrez","doi":"10.4018/ijswis.309422","DOIUrl":"https://doi.org/10.4018/ijswis.309422","url":null,"abstract":"The aim of this work is the development of an information system that, by integrating data from different sources and applying semantic technologies, makes it possible to publish and share with society the scientific production generated in the university environment, promoting its dissemination and thus contributing to the knowledge society, among others. In practice, this is the implementation of a CRIS (current research information system). This CRIS presents advanced features. On one hand it applies semantic technologies, providing a query service through a SPARQL Point, besides the reuse of shared data by exporting them in different formats. In this sense, it is also based on a European ontology or semantic standard such as CERIF, which facilitates its portability. On the other hand, CRIS also presents an alternative to the lack of a single data system by allowing data from different sources to be integrated and managed.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"78 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78141683","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}
引用次数: 0
Adaptive Ontology-Based IoT Resource Provisioning in Computing Systems 计算系统中基于自适应本体的物联网资源分配
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.306260
Ashish Tiwari, R. Garg
{"title":"Adaptive Ontology-Based IoT Resource Provisioning in Computing Systems","authors":"Ashish Tiwari, R. Garg","doi":"10.4018/ijswis.306260","DOIUrl":"https://doi.org/10.4018/ijswis.306260","url":null,"abstract":"The eagle expresses of cloud computing plays a pivotal role in the development of technology. The aim is to solve in such a way that it will provide an optimized solution. The key role of allocating these efficient resources and making the algorithms for its time and cost optimization. The approach of the research is based on the rough set theory RST. RST is a great method for making a large difference in qualitative analysis situations. It's a technique to find knowledge discovery and handle the problems such as inductive reasoning, automatic classification, pattern recognition, learning algorithms, and data reduction. The rough set theory is the new method in cloud service selection so that the best services provide for cloud users and efficient service improvement for cloud providers. The simulation of the work is finished at intervals with the merchandise utilized for the formation of the philosophy framework. The simulation shows the IoT services provided by the IoT service supplier to the user are the best utilization with the parameters and ontology technique.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"32 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74628101","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}
引用次数: 13
Mc-DNN: Fake News Detection Using Multi-Channel Deep Neural Networks Mc-DNN:使用多通道深度神经网络检测假新闻
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.295553
Jitendra V. Tembhurne, Md. Moin Almin, Tausif Diwan
{"title":"Mc-DNN: Fake News Detection Using Multi-Channel Deep Neural Networks","authors":"Jitendra V. Tembhurne, Md. Moin Almin, Tausif Diwan","doi":"10.4018/ijswis.295553","DOIUrl":"https://doi.org/10.4018/ijswis.295553","url":null,"abstract":"With the advancement of technology, social media has become a major source of digital news due to its global exposure. This has led to an increase in spreading fake news and misinformation online. Humans cannot differentiate fake news from real news because they can be easily influenced. A lot of research work has been conducted for detecting fake news using Artificial Intelligence and Machine Learning. A large number of deep learning models and their architectural variants have been investigated and many websites are utilizing these models directly or indirectly to detect fake news. However, state-of-the-arts demonstrate the limited accuracy in distinguishing fake news from the original news. We propose a multi-channel deep learning model namely Mc-DNN, leveraging and processing the news headlines and news articles along different channels for differentiating fake or real news. We achieve the highest accuracy of 99.23% on ISOT Fake News Dataset and 94.68% on Fake News Data for Mc-DNN. Thus, we highly recommend the use of Mc-DNN for fake news detection.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"48 1","pages":"1-20"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91271192","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}
引用次数: 25
Understanding Universal Adversarial Attack and Defense on Graph 理解图上的通用对抗性攻击和防御
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.308812
Tianfeng Wang, Zhisong Pan, Guyu Hu, Yexin Duan, Yu Pan
{"title":"Understanding Universal Adversarial Attack and Defense on Graph","authors":"Tianfeng Wang, Zhisong Pan, Guyu Hu, Yexin Duan, Yu Pan","doi":"10.4018/ijswis.308812","DOIUrl":"https://doi.org/10.4018/ijswis.308812","url":null,"abstract":"Compared with traditional machine learning model, graph neural networks (GNNs) have distinct advantages in processing unstructured data. However, the vulnerability of GNNs cannot be ignored. Graph universal adversarial attack is a special type of attack on graph which can attack any targeted victim by flipping edges connected to anchor nodes. In this paper, we propose the forward-derivative-based graph universal adversarial attack (FDGUA). Firstly, we point out that one node as training data is sufficient to generate an effective continuous attack vector. Then we discretize the continuous attack vector based on forward derivative. FDGUA can achieve impressive attack performance that three anchor nodes can result in attack success rate higher than 80% for the dataset Cora. Moreover, we propose the first graph universal adversarial training (GUAT) to defend against universal adversarial attack. Experiments show that GUAT can effectively improve the robustness of the GNNs without degrading the accuracy of the model.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"30 1","pages":"1-21"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85298552","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}
引用次数: 2
False Alert Detection Based on Deep Learning and Machine Learning 基于深度学习和机器学习的假警报检测
IF 3.2 4区 计算机科学
International Journal on Semantic Web and Information Systems Pub Date : 2022-01-01 DOI: 10.4018/ijswis.297035
Shudong Li, Danyi Qin, Xiaobo Wu, Juan Li, Baohui Li, Weihong Han
{"title":"False Alert Detection Based on Deep Learning and Machine Learning","authors":"Shudong Li, Danyi Qin, Xiaobo Wu, Juan Li, Baohui Li, Weihong Han","doi":"10.4018/ijswis.297035","DOIUrl":"https://doi.org/10.4018/ijswis.297035","url":null,"abstract":"Among the large number of network attack alerts generated every day, actual security incidents are usually overwhelmed by a large number of redundant alerts. Therefore, how to remove these redundant alerts in real time and improve the quality of alerts is an urgent problem to be solved in large-scale network security protection. This paper uses the method of combining machine learning and deep learning to improve the effect of false alarm detection and then more accurately identify real alarms, that is, in the process of training the model, the features of a hidden layer output of the DNN model are used as input to train the machine learning model. In order to verify the proposed method, we use the marked alert data to do classification experiments, and finally use the accuracy recall rate, precision, and F1 value to evaluate the model. Good results have been obtained.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"23 1","pages":"1-21"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89234086","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}
引用次数: 19
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