Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

SOCIAL WEB (61 journals)

Showing 1 - 58 of 58 Journals sorted alphabetically
ACM Transactions on Social Computing     Hybrid Journal  
ACM Transactions on the Web (TWEB)     Hybrid Journal   (Followers: 3)
American Journal of Information Systems     Open Access   (Followers: 4)
Asiascape : Digital Asia     Hybrid Journal   (Followers: 1)
CCF Transactions on Networking     Hybrid Journal  
Communications in Mobile Computing     Open Access   (Followers: 14)
Computational Social Networks     Open Access   (Followers: 4)
Cyberpolitik Journal     Open Access  
Cyberpsychology, Behavior, and Social Networking     Hybrid Journal   (Followers: 16)
Data Science     Open Access   (Followers: 6)
Digital Library Perspectives     Hybrid Journal   (Followers: 40)
Discover Internet of Things     Open Access   (Followers: 2)
Informação & Informação     Open Access   (Followers: 2)
Information Technology and Libraries     Open Access   (Followers: 312)
Infrastructure Complexity     Open Access   (Followers: 5)
International Journal of Art, Culture and Design Technologies     Full-text available via subscription   (Followers: 10)
International Journal of Bullying Prevention     Hybrid Journal   (Followers: 1)
International Journal of Digital Humanities     Hybrid Journal   (Followers: 3)
International Journal of e-Collaboration     Full-text available via subscription  
International Journal of E-Entrepreneurship and Innovation     Full-text available via subscription   (Followers: 6)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Information Privacy, Security and Integrity     Hybrid Journal   (Followers: 25)
International Journal of Information Technology and Web Engineering     Hybrid Journal   (Followers: 2)
International Journal of Interactive Communication Systems and Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Interactive Mobile Technologies     Open Access   (Followers: 8)
International Journal of Internet and Distributed Systems     Open Access   (Followers: 2)
International Journal of Knowledge Society Research     Full-text available via subscription  
International Journal of Networking and Virtual Organisations     Hybrid Journal   (Followers: 11)
International Journal of Social and Humanistic Computing     Hybrid Journal  
International Journal of Social Computing and Cyber-Physical Systems     Hybrid Journal  
International Journal of Social Media and Interactive Learning Environments     Hybrid Journal   (Followers: 14)
International Journal of Social Network Mining     Hybrid Journal   (Followers: 3)
International Journal of Virtual Communities and Social Networking     Full-text available via subscription   (Followers: 1)
International Journal of Web Based Communities     Hybrid Journal  
International Journal of Web-Based Learning and Teaching Technologies     Hybrid Journal   (Followers: 20)
International Journal on Semantic Web and Information Systems     Hybrid Journal   (Followers: 4)
Internet Technology Letters     Hybrid Journal  
JLIS.it     Open Access   (Followers: 7)
Journal of Cyber Policy     Hybrid Journal   (Followers: 1)
Journal of Digital & Social Media Marketing     Full-text available via subscription   (Followers: 18)
Journal of Social Structure     Open Access   (Followers: 1)
Medicine 2.0     Open Access   (Followers: 2)
Observatorio (OBS*)     Open Access  
Online Social Networks and Media     Hybrid Journal   (Followers: 9)
Policy & Internet     Hybrid Journal   (Followers: 11)
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies     Hybrid Journal  
Redes. Revista Hispana para el Análisis de Redes Sociales     Open Access  
RESET     Open Access  
Scientific Phone Apps and Mobile Devices     Open Access  
Social Media + Society     Open Access   (Followers: 24)
Social Network Analysis and Mining     Hybrid Journal   (Followers: 4)
Social Networking     Open Access   (Followers: 3)
Social Networks     Hybrid Journal   (Followers: 20)
Social Science Computer Review     Hybrid Journal   (Followers: 13)
Synthesis Lectures on the Semantic Web: Theory and Technology     Full-text available via subscription  
Teknokultura. Revista de Cultura Digital y Movimientos Sociales     Open Access  
Terminal     Open Access  
Texto Digital     Open Access  
Similar Journals
Journal Cover
International Journal on Semantic Web and Information Systems
Journal Prestige (SJR): 0.414
Citation Impact (citeScore): 2
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1552-6283 - ISSN (Online) 1552-6291
Published by IGI Global Homepage  [147 journals]
  • Using an ontology-based neural network and DEA to discover deficiencies of
           hotel service

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      Abstract: Companies can gain critical real-time insights into customer requirements and service evaluation by mining social media. To acquire the service performance and improve the service deficiencies for hotels, this research proposes a benchmark-based performance evaluation model for hotel service to enable hotel managers to assess the service performance. In the case of non-benchmark service hotels, the identification and improvement model for non-benchmark criteria can recognize and analyze the required quantities of performance improvements for non-benchmark criteria. For understanding the causes of service deficiencies, this research mines the online posts and creates a hierarchical ontology of service deficiencies for hotels. A hierarchical ontology-based neural network is proposed to automatically identify the causes of service deficiencies. This study employs an online forum as a case to achieve the identification accuracy of causes of service deficiencies of 92.68%. The analytical result can demonstrate the significant effectiveness and practical value of the proposed methodology.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.306748
      Issue No: Vol. 18, No. 1 (2022)
       
  • An agent-based social simulation for citizenship competences and conflict
           resolution styles

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      Abstract: The development of citizenship competences plays an important role in a complex system like the society. Thus, to analyze how such competences impact on other contexts is a great challenge because this kind of studies involve the work with people and the use of variables that depend on human behaviors. In this sense, many studies have highlighted the advantage of using simulation systems and tools. In particular, the agent-based social simulation field relies upon semantic web to manage knowledge representation in social scenarios. This study focuses on how citizenship competences impact on conflicts resolution. Moreover, a simulation model in which citizens interact to resolve conflicts by considering citizenship competences and conflict resolution styles is also introduced. It was developed in NetLogo together with an extension that connects it with the ontology of competences. Results show that the higher interactions of citizens-conflicts, the higher level of citizenship competences and, that the number of conflicts solved is higher when using citizenship competences.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.306749
      Issue No: Vol. 18, No. 1 (2022)
       
  • A path clustering driving travel route excavation

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      Abstract: The refueling trajectory of self-driving tourists is sparse, and it is difficult to restore the real travel route. A sparse trajectory clustering algorithm is proposed based on semantic representation to mine popular self-driving travel routes. Different from the traditional trajectory clustering algorithm based on trajectory point matching, the semantic relationship between different trajectory points is researched in this algorithm, and the low-dimensional vector representation of the trajectory is learned. First, the neural network language model is used to learn the distributed vector representation of the fueling station; then, the average of all the station vectors in each trajectory is taken as the vector representation of the trajectory. Finally, the classic k-means algorithm is used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm effectively mines two popular self-driving travel routes.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.306750
      Issue No: Vol. 18, No. 1 (2022)
       
  • Adaptive Ontology-based IoT resource provisioning in computing Systems

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      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 to 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 deals with the IoT services provided by the IoT service supplier to the user is that the best utilization with the parameters and ontology technique.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.306260
      Issue No: Vol. 18, No. 1 (2022)
       
  • Chaotic Whale Crow Optimization Algorithm for Secure Routing In Iot
           Environment

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      Abstract: This paper solves the Internet of Things (IoT) security issues by introducing a Chaotic Whale Crow (CWC) optimization, which is the integration of Chaotic Whale Optimization Algorithm (CWOA) in Crow Search Algorithm (CSA). The framework operates on two crucial aspects: one is to select the secure nodes, and the other is to implement secure routing using the selected trusted nodes. First, the selection of trusted nodes is performed based on trust factors like direct, indirect, forwarding rate, integrity, and availability factors. Then, the selected trusted nodes are adapted for trust-based secure routing, which is optimally performed using the proposed CWC, based on the fitness parameters trust and energy. Finally, the proposed CWC is evaluated, which revealed high performance with a minimal delay of 191.46ms, which shows 14.87%, 7.35%, 6.82%, 4.19%, and 5.74% improved performance comapred to existing LaSeR, PM Ipv6, secTrust-RPL RISA, and LSDAR techniques. Similarly, the proposed method obtained the maximal energy of 71.25J, and maximal throughput of 129.77kbps.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300824
      Issue No: Vol. 18, No. 1 (2022)
       
  • An Improved Structural-Based Ontology Matching Approach Using Similarity
           Spreading

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      Abstract: Increasing number of ontologies demand the interoperability between them in order to gain accurate information. the ontology heterogeneity also makes the interoperability process even more difficult. These scenarios let the development of effective and efficient ontology matching. The existing ontology matching systems are mainly focusing with subject derivatives of the concern domain. Since ontologies are represented as data model in structured format, In this paper, a new modified model of similarity spreading for ontology mapping is proposed. In this approach the mapping mainly involves with node clustering based on edge affinity and then the graph matching is achieved by applying coefficient similarity propagation. This process is carried out by iterative manner and at the end the similarity score is calculated for iteration. This model is evaluated in terms of precision, recall and f-measure parameters and found that it outperforms well than its similar kind of systems.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300825
      Issue No: Vol. 18, No. 1 (2022)
       
  • Tiny-UKSIE-An Optimized Lightweight Semantic Inference Engine for
           Reasoning Uncertain Knowledge

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      Abstract: The application of semantic web technologies such as semantic inference to the field of the Internet of Things (IoT) can realize data semantic information enhancement and semantic knowledge discovery, which plays a key role in enhancing data value and application intelligence. However, Mainstream semantic inference engines cannot be applied to IoT computing devices with limited storage resources and weak computing power, and cannot reason about uncertain knowledge. To solve this problem, the authors propose a lightweight semantic inference engine, Tiny-UKSIE, based on the RETE algorithm. The genetic algorithm (GA) is adopted to optimize the Alpha network sequence, and the inference time can be reduced by 8.73% before and after optimization. Moreover, a four-tuple knowledge representation method with probability factors is proposed, and probabilistic inference rules are constructed to enable the inference engine to infer uncertain knowledge. Compared with mainstream inference engines, storage resource usage is reduced by up to 97.37%, and inference time is reduced by up to 24.55%.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300826
      Issue No: Vol. 18, No. 1 (2022)
       
  • A Differential Epidemic Model for Information, Misinformation and
           Disinformation in Online Social Networks

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      Abstract: These days the online social network has become a huge source of data. People are actively sharing information on these platforms. The data on online social networks can be misinformation, information, and disinformation. Because online social network has become an important part of our life, so the information on online social networks makes a great impact on us. Here a differential epidemic model for information, misinformation, and disinformation on online social networks is proposed. The expression for basic reproduction number has been developed. Again, the stability condition for the system at both infection-free and endemic equilibriums points has been discussed. The Numerical simulation has been performed to validate our theoretical results. Again, with the help of data available on twitter related to COVID-19 vaccination is used to perform the experiment. Finally, discuss about the control strategy to minimize the misinformation and disinformation related to vaccination.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300827
      Issue No: Vol. 18, No. 1 (2022)
       
  • AUV based Efficient Data Collection Scheme for Underwater Linear Sensor
           Networks

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      Abstract: The research on Underwater Wireless Sensor Networks (UWSNs) has grown considerably in recent years where the main focus remains to develop a reliable communication protocol to overcome its challenges between various underwater sensing devices. The main purpose of UWSNs is to provide a low cost and an unmanned data collection system for a range of applications such as offshore exploration, pollution monitoring, oil and gas pipeline monitoring, surveillance, etc. One of the common types of UWSN is Linear Sensor Network (LSN) which specially targets to monitor the underwater oil and gas pipelines. Under this application, in most of the previously proposed works, networks are deployed without considering the heterogeneity and capacity of the various sensor nodes. This negligence leads to the problem of inefficient data delivery from the sensor nodes deployed on the pipeline to the surface sinks. In addition, the existing path planning algorithms do not consider the network coverage of heterogeneous sensor nodes.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.299858
      Issue No: Vol. 18, No. 1 (2022)
       
  • Improved Semantic Representation Learning by Multiple Clustering for
           Image-Based 3D Model Retrieval

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      Abstract: Under the heavy management on the increasing 3D models, the topic of image-based 3D model retrieval which organizes unlabeled 3D models based on abundant knowledge learned from labeled 2D images has drawn attentions. However, prior methods are limited in aligning semantically at corresponding categories of two domains due to the lack of label information in 3D domain. To this end, this paper proposes an improved semantic representation learning by multiple clustering approach, which improves the reliability of pseudo labels for 3D models, so as to achieve class-level semantic alignment. Specifically, this paper first extracts features for 2D images and 3D models. Then it clusters combining the 3D features with the semantic information from multiple clustering on 3D model features to obtain more reliable target pseudo label. Extensive experiments have shown that the proposed method has achieved the gain of 3.0%-205.0% averagely for popular retrieval metrics on the benchmark of Monocular Image based 3D Object Retrieval (MI3DOR), and 1.3%-69.7% on another advanced benchmark MI3DOR-2.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297033
      Issue No: Vol. 18, No. 1 (2022)
       
  • A parallel fractional lion algorithm for data clustering based on
           MapReduce cluster framework

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      Abstract: This work introduces a parallel clustering algorithm by modifying the existing Fractional Lion Algorithm (FLA). The proposed work replaces the conventional Euclidean distance measure with the Bhattacharya distance measure to newly propose the improved FLA (IMR-FLA). The proposed IMR-FLA is implemented in both the mapper and the reducer in the MapReduce framework to achieve the parallel clustering. The experimentation of the proposed IMR-FLA is done by using six standard databases, namely Pima Indian diabetes dataset, Heart disease dataset, Hepatitis dataset, localization dataset, breast cancer dataset, and skin segmentation dataset, from the UCI repository. The proposed IMR-FLA has the overall improved Jaccard coefficient value of 0.9357, 0.6572, 0.7462, 0.5944, 0.9418, and 0.8680, for each dataset. Similarly, the proposed IMR-FLA algorithm has outclassed other classifiers' performance with the clustering accuracy value of 0.9674, 0.9471, 0.9677, 0.777, 0.9023, and 0.9585, respectively, for the experimental databases.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297034
      Issue No: Vol. 18, No. 1 (2022)
       
  • Modified Transformer architecture to Explain Black box models in Narrative
           form

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      Abstract: The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to be interpreted by a non-expert user. Here, the use of Natural Language Generation (NLG) based techniques can help to represent explanations in human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to unavailability of a relevant annotated dataset for development and testing, we also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both, manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297040
      Issue No: Vol. 18, No. 1 (2022)
       
  • Virtual Reality Simulator Enhances Ergonomics Skills for Neurosurgeons

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      Abstract: This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The project main objective is to investigate the ability virtual reality to enhance the training.An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is two fold. From one side investigates the new requirements for the enhancement of Neurosurgenos’ training and adoption on Virtual Reality Simulator. From the other side contributes to the body of knowledge related to the required Ergonomics skills.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297041
      Issue No: Vol. 18, No. 1 (2022)
       
  • An Ontology-based Automation System

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      Abstract: This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus fertilization ontology, which covers knowledge about citrus fertilizers and fertilization application. Specially, our approach can provide not only the yearly fertilization quantities of required pure nitrogen, phosphorus, and potassium according to their disease symptoms, but also the suitable fertilizing recommendations for the citrus orchards with different soil properties. The current version of the ontology (ver. 2.9.10) contains 103 classes, 34 properties, 800 instances, which are defined by 3056 RDF triples and is evaluated by using 90 competency questions. Furthermore, we run experiments with our proposal targeting at four citrus orchards in Chongqing, and compare its outputs with the reference values advised by the agri-professionals of citrus planting.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 0-0
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295946
      Issue No: Vol. 18, No. 1 (2022)
       
  • Longitudinal Study of a Website for Assessing American Presidential
           Candidates and Decision Making of Potential Election Irregularities
           Detection

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      Authors: Piper; Justin, Rodger, James A.
      Pages: 1 - 20
      Abstract: We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-20
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.305802
      Issue No: Vol. 18, No. 1 (2022)
       
  • Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to
           Extract Causal Factors

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      Authors: Akilandeswari J; , Jothi G, , Dhanasekaran K, , Kousalya K, , Sathiyamoorthi V,
      Pages: 1 - 27
      Abstract: Social media especially Twitter has become ubiquitous among people where they express their opinions on various domains. This paper presents a Hybrid Firefly – Ontology-based Clustering (FF-OC) algorithm which attempts to extract factors impacting a major public issue that is trending. In this research work, the issue of food price rise and disease which was trending during the time of the investigation is considered. The novelty of the algorithm lies in the fact that it clusters the association rules without any prior knowledge. The findings from the experimentation suggest different factors impacting the rise of price in food items and diseases such as diabetes, flu, zika virus. The empirical results show the significant improvement when compared with Artificial Bees Colony, Cuckoo Search Algorithm, Particle Swarm Optimization, and Ant Colony Optimization based clustering algorithms. The proposed method gives an improvement of 81% in terms of DB index, 79% in terms of silhouette index, 85% in terms of C index when compared to other algorithms.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-27
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295550
      Issue No: Vol. 18, No. 1 (2022)
       
  • Cat-Squirrel Optimization Algorithm for VM Migration in a Cloud Computing
           Platform

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      Authors: C; Ashok Kumar, P, Sivakumar
      Pages: 1 - 23
      Abstract: This paper introduces an approach for the VM migration based on optimization algorithm, named CS in cloud. The provider to be selected is carried out with the usage of multiple constraints, like delay, bandwidth, cost, and load. Subsequently, the effective searching criteria are computed for finding the optimal service on the basis of fitness constraints. The searching criteria are formulated as optimization problems, which are tackled using CS. The proposed CS is designed by integrating CSO with the SSA such that the fitness function is evaluated for the optimal VM migration by considering several parameters, such as delay, cost, bandwidth, and load. Thus, the cloud manager will perform the migration of VM in cloud based on proposed CS-based VM migration approach. The performance of the CS-based VM migration is evaluated in terms of delay, cost, and load. The proposed CS-based VM migration method achieves the minimal delay of 0.146, minimal cost of 0.052, and the minimal load of 0.182.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-23
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297142
      Issue No: Vol. 18, No. 1 (2022)
       
  • Analyzing the Sociodemographic Factors Impacting the Use of Virtual
           Reality for Controlling Obesity

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      Authors: Alduailij; Mona, Alhalabi, Wadee, Alduaili, Mai, Al-Rashee, Amal, Alabdulkareem, Eatedal, Alharb, Seham Saad
      Pages: 1 - 38
      Abstract: Obesity is one of the most pressing issues in society today. Virtual reality has been used in the design of tools that promotes obesity control. However, the design of current VR tools lacks the involvement of prospective users and health practitioners. Such engagement is crucial in gathering semantic information that identifies stakeholders’ needs and ensures that all aspects of health are considered. Therefore, this paper aims to study the sociodemographic factors and individual-level characteristics and preferences that make the design of any obesity-control VR tool effective and satisfactory for a wide range of users. The paper also aims to solicit opinions of health practitioners to identify best health aspects that should be available in the design of any VR tool for obesity control. Organizations, businesses, and people will be able to readily augment such VR technologies on the semantic web, as well as on personal and mobile devices.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-38
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300819
      Issue No: Vol. 18, No. 1 (2022)
       
  • Raising Consent Awareness With Gamification and Knowledge Graphs

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      Authors: Rasmusen; Sven Carsten, Penz, Manuel, Widauer, Stephanie, Nako, Petraq, Kurteva, Anelia, Roa-Valverde, Antonio, Fensel, Anna
      Pages: 1 - 21
      Abstract: Consent is one of GDPR’s lawful bases for data processing and specific requirements for it apply. Consent should be specific, unambiguous and most of all informed. However, an informed consent request does not guarantee having individuals who are aware of what it means to consent and the implications that follow. Consent is often given blindly now, in particular because of information overload from long privacy policies written in legal language and complex interface designs that cause consent fatigue on the users' side. This paper presents a knowledge graph-based user interface for consent solicitation, which uses gamification to raise the legal awareness and ease individual’s comprehension of consent. The knowledge graph models informed consent in a machine-readable format and provides a unified consent model to all entities involved in the data sharing process. The evaluation shows that with the help of gamification, the interface can raise individuals' average legal awareness to 92.86%.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.300820
      Issue No: Vol. 18, No. 1 (2022)
       
  • Evaluation and Comparative Analysis of Semantic Web-Based Strategies for
           Enhancing Educational System Development

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      Authors: Hu; Bin, Gaurav, Akshat, Choi, Chang, Almomani, Ammar
      Pages: 1 - 14
      Abstract: Educators have been calling for reform for a decade. Recent technical breakthroughs have led to various improvements in the semantic web-based education system. After last year's COVID-19 outbreak, development quickened. Many countries and educational systems now concentrate on providing students with online education, which differs greatly from traditional classroom education. Online education allows students to learn at their own pace and the system. As a consequence, we may say that education has become more dynamic. In the educational system, this changing nature makes user demands difficult to identify. Many instructors suggest using machine learning, artificial intelligence, or ontology to improve traditional teaching methods. Due to the lack of survey studies examining and comparing all of the researcher's semantic web-based teaching methodologies, we decided to conduct this survey. This paper's goal is to analyse all available possibilities for semantic web-based education systems that enable new researchers to develop their knowledge.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-14
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.302895
      Issue No: Vol. 18, No. 1 (2022)
       
  • Information Entropy Augmented High Density Crowd Counting Network

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      Authors: Hao; Yu, Wang, Lingzhe, Liu, Ying, Fan, Jiulun
      Pages: 1 - 15
      Abstract: The research proposes an innovated structure of the density map-based crowd counting network augmented by information entropy. The network comprises of a front-end network to extract features and a back-end network to generate density maps. In order to validate the assumption that the entropy can boost the accuracy of density map generation, a multi-scale entropy map extraction process is imported into the front-end network along with a fine-tuned convolutional feature extraction process, In the back-end network, extracted features are decoded into the density map with a multi-column dilated convolution network. Finally, the decoded density map can be mapped as the estimated counting number. Experimental results indicate that the devised network is capable of accurately estimating the count in extremely high crowd density. Compared to similar structured networks which don’t adapt entropy feature, the proposed network exhibits higher performance. This result proves the feature of information entropy is capable of enhancing the efficiency of density map-based crowd counting approaches.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-15
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297144
      Issue No: Vol. 18, No. 1 (2022)
       
  • False Alert Detection Based on Deep Learning and Machine Learning

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      Authors: Li; Shudong, Qin, Danyi, Wu, Xiaobo, Li, Juan, Li, Baohui, Han, Weihong
      Pages: 1 - 21
      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.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297035
      Issue No: Vol. 18, No. 1 (2022)
       
  • Handling Data Scarcity Through Data Augmentation in Training of Deep
           Neural Networks for 3D Data Processing

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      Authors: Srivastava; Akhilesh Mohan, Rotte, Priyanka Ajay, Jain, Arushi, Prakash, Surya
      Pages: 1 - 16
      Abstract: Due to the availability of cheap 3D sensors such as Kinect and LiDAR, the use of 3D data in various domains such as manufacturing, healthcare, and retail to achieve operational safety, improved outcomes, and enhanced customer experience has gained momentum in recent years. In many of these domains, object recognition is being performed using 3D data against the difficulties posed by illumination, pose variation, scaling, etc present in 2D data. In this work, we propose three data augmentation techniques for 3D data in point cloud representation that use sub-sampling. We then verify that the 3D samples created through data augmentation carry the same information by comparing the Iterative Closest Point Registration Error within the sub-samples, between the sub-samples and their parent sample, between the sub-samples with different parents and the same subject, and finally, between the sub-samples of different subjects. We also verify that the augmented sub-samples have the same characteristics and features as those of the original 3D point cloud by applying the Central Limit Theorem.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-16
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297038
      Issue No: Vol. 18, No. 1 (2022)
       
  • NIR Spectroscopy Oranges Origin Identification Framework Based on Machine
           Learning

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      Authors: Dan; Songjian
      Pages: 1 - 16
      Abstract: Research on the identification model of orange origin based on machine learning in Near infrared (NIR) spectroscopy. According to the characteristics of NIR spectral data, a complete general framework for origin identification is proposed. It includes steps such as data preprocessing, feature selection, model building and cross validation. Compare multiple preprocessing algorithms and multiple machine learning algorithms under the framework. Based on NIR spectroscopy to identify the origin of orange, a good identification result was obtained. Improve the accuracy of orange origin identification and obtained the best origin identification accuracy of 92.8%.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-16
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297039
      Issue No: Vol. 18, No. 1 (2022)
       
  • A Semantic Framework Supporting Multilayer Networks Analysis for Rare
           Diseases

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      Authors: Capuano; Nicola, Foggia, Pasquale, Greco, Luca, Ritrovato, Pierluigi
      Pages: 1 - 22
      Abstract: Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-22
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297141
      Issue No: Vol. 18, No. 1 (2022)
       
  • Deep Embedding Learning With Auto-Encoder for Large-Scale Ontology
           Matching

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      Authors: Khoudja; Meriem Ali, Fareh, Messaouda, Bouarfa, Hafida
      Pages: 1 - 18
      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.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297042
      Issue No: Vol. 18, No. 1 (2022)
       
  • Distributed Denial-of-Service (DDoS) Attacks and Defense Mechanisms in
           Various Web-Enabled Computing Platforms

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      Authors: Singh; Anshuman, Gupta, Brij B.
      Pages: 1 - 43
      Abstract: The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-43
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297143
      Issue No: Vol. 18, No. 1 (2022)
       
  • Learning Disease Causality Knowledge From the Web of Health Data

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      Authors: Yu; Hong Qing, Reiff-Marganiec, Stephan
      Pages: 1 - 19
      Abstract: Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-19
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297145
      Issue No: Vol. 18, No. 1 (2022)
       
  • Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs

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      Authors: Li; Pu, Li, Tianci, Wang, Xin, Zhang, Suzhi, Jiang, Yuncheng, Tang, Yong
      Pages: 1 - 19
      Abstract: In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-19
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297146
      Issue No: Vol. 18, No. 1 (2022)
       
  • Flesch-Kincaid Measure as Proxy of Socio-Economic Status on Twitter

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      Authors: Ahmed; Samara, Rajput, Adil, Sarirete, Akila, Chowdhry, Tauseef J.
      Pages: 1 - 19
      Abstract: Social media gives researchers an invaluable opportunity to gain insight into different facets of human life. Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and chatrooms are common tools of conversations grouping. Crowdsourcing involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We analyzed online forum posts from various geographical regions in the US and characterized the readability scores of users. Specifically, we collected 10,000 tweets from the members of US Senate and computed the Flesch-Kincaid readability score. Comparing the Senators’ tweets to the ones from average internet users, we note 1) US Senators’ readability based on their tweets rate is much higher, and 2) immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-19
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297037
      Issue No: Vol. 18, No. 1 (2022)
       
  • A New Alignment Word-Space Approach for Measuring Semantic Similarity for
           Arabic Text

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      Authors: Ismail; Shimaa, Shishtawy, Tarek EL, Alsammak, Abdelwahab Kamel
      Pages: 1 - 18
      Abstract: This work presents a new alignment word-space approach for measuring the similarity between two snipped texts. The approach combines two similarity measurement methods: alignment-based and vector space-based. The vector space-based method depends on a semantic net that represents the meaning of words as vectors. These vectors are lemmatized to enrich the search space. The alignment-based method generates an alignment word space matrix (AWSM) for the snipped texts according to the generated semantic word spaces. Finally, the degree of sentence semantic similarity is measured using some proposed alignment rules. Four experiments were carried out to evaluate the performance of the proposed approach, using two different datasets. The experimental results proved that applying the lemmatization process for the input text and the vector model has a better effect. The degree of correctness of the results reaches 0.7212 which is considered one of the best two results of the published Arabic semantic similarities.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-18
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297036
      Issue No: Vol. 18, No. 1 (2022)
       
  • Predictive Model Using a Machine Learning Approach for Enhancing the
           Retention Rate of Students At-Risk

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      Authors: Brdesee; Hani Sami, Alsaggaf, Wafaa, Aljohani, Naif, Hassan, Saeed-Ul
      Pages: 1 - 21
      Abstract: Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-21
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.299859
      Issue No: Vol. 18, No. 1 (2022)
       
  • Semantic Trajectory Frequent Pattern Mining Model

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      Authors: Li; Jun, Su, Jie
      Pages: 1 - 20
      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.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-20
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297031
      Issue No: Vol. 18, No. 1 (2022)
       
  • Phishing Website Detection With Semantic Features Based on Machine
           Learning Classifiers

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      Authors: Almomani; Ammar, Alauthman, Mohammad, Shatnawi, Mohd Taib, Alweshah, Mohammed, Alrosan, Ayat, Alomoush, Waleed, Gupta, Brij B.
      Pages: 1 - 24
      Abstract: The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-24
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.297032
      Issue No: Vol. 18, No. 1 (2022)
       
  • A Context-Independent Ontological Linked Data Alignment Approach to
           Instance Matching

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      Authors: Barbosa; Armando, Bittencourt, Ig I., Siqueira, Sean W., Dermeval, Diego, Cruz, Nicholas J. T.
      Pages: 1 - 29
      Abstract: Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-29
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295977
      Issue No: Vol. 18, No. 1 (2022)
       
  • A Model of Semantic-Based Image Retrieval Using C-Tree and Neighbor Graph

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      Authors: Nhi; Nguyen Thi Uyen, Le, Thanh Manh, Thanh The Van,
      Pages: 1 - 23
      Abstract: The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-23
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295551
      Issue No: Vol. 18, No. 1 (2022)
       
  • Doc2KG

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      Authors: Stylianou; Nikolaos, Vlachava, Danai, Konstantinidis, Ioannis, Bassiliades, Nick, Peristeras, Vassilios
      Pages: 1 - 20
      Abstract: Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-20
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295552
      Issue No: Vol. 18, No. 1 (2022)
       
  • Mc-DNN

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      Authors: Tembhurne; Jitendra Vikram, Almin, Md. Moin, Diwan, Tausif
      Pages: 1 - 20
      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.
      Keywords: Web Technologies; Computer Science & IT; Semantic Web
      Citation: International Journal on Semantic Web and Information Systems (IJSWIS), Volume: 18, Issue: 1 (2022) Pages: 1-20
      PubDate: 2022-01-01T05:00:00Z
      DOI: 10.4018/IJSWIS.295553
      Issue No: Vol. 18, No. 1 (2022)
       
 
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