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Social and Semantic Models, Tools and Applications in Science and...
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Social and Semantic Models, Tools and Applications in Science and Technology

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (10 January 2022) | Viewed by 21967

Special Issue Editors

Dr. Katia Lida Kermanidis

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Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: machine learning; data mining; natural language processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Manolis Maragoudakis

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: data & social mining; big data; information retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Phivos Mylonas

E-Mail Website
Guest Editor
Department of Informatics, Ionian University, 491 32 Corfu, Greece
Interests: knowledge management; context representation and analysis; knowledge-assisted multimedia analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At the end of a peculiar and difficult academic year due to the worldwide COVID-19 pandemic, the need for efficient data mining and machine learning models, tools, and applications is more evident than ever. The power of social network communities and the vast amount of data they produce forms a clear new source of valuable information. New, innovative approaches are required in order to tackle the new research challenges. In this framework, social and semantic analysis may be described as one of the most crucial and challenging research tasks of this period. Since this view is heavily applicable to the research community, it also faces huge challenges from the data management aspect and also involves emerging disciplines in social information processing and related social tools and semantic applications.

The purpose of the SMAP workshop series and the content related to the proposed Special Issue are formed around two main axes. The first axis focuses on the extraction, processing, manipulation, and analysis of data, information, and knowledge, while the second axis focuses on the use of the above results for the production of effective models, tools, and applications. Of course, the final task is to facilitate the various human actions related to the associated computational task in order to make the lives of the individuals involved easier relative to their daily lives.

This Special Issue aims to bring together an interdisciplinary approach, focusing on innovative applications and existing social and semantic methodologies. Since typical computational data are usually controlled by semantic heterogeneity and are rather dynamic in nature, computer science researchers are encouraged to develop new or adapt existing suitable models, tools, and applications to effectively solve these problems. Therefore, this Special Issue is completely open to anyone who wants to submit a relevant research manuscript.

In addition to the Open Call, selected papers which will be presented during SMAP 2021 will be invited to be submitted as extended versions to this Special Issue. In this case, the workshop paper should be cited and noted on the first page of the submitted paper; authors are asked to disclose that it is a workshop paper in their cover letter and include a statement on what has been changed compared to the original workshop paper. Each submission to this journal issue should contain at least 50% new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.

All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and collected together on this Special Issue’s website.

Dr. Katia Lida Kermanidis
Prof. Dr. Manolis Maragoudakis
Prof. Dr. Phivos Mylonas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

19 pages, 2155 KiB  
Article
Assisting Educational Analytics with AutoML Functionalities
by Spyridon Garmpis, Manolis Maragoudakis and Aristogiannis Garmpis
Computers 2022, 11(6), 97; https://doi.org/10.3390/computers11060097 - 15 Jun 2022
Cited by 2 | Viewed by 2863
Abstract
The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required [...] Read more.
The plethora of changes that have taken place in policy formulations on higher education in recent years in Greece has led to unification, the abolition of departments or technological educational institutions (TEI) and mergers at universities. As a result, many students are required to complete their studies in departments of the abolished TEI. Dropout or a delay in graduation is a significant problem that results from newly joined students at the university, in addition to the provision of studies. There are various reasons for this, with student performance during studies being one of the major contributing factors. This study was aimed at predicting the time required for weak students to pass their courses so as to allow the university to develop strategic programs that will help them improve performance and graduate in time. This paper presents various components of educational data mining incorporating a new state-of-the-art strategy, called AutoML, which is used to find the best models and parameters and is capable of predicting the length of time required for students to pass their courses using their past course performance and academic information. A dataset of 23,687 “Computer Networking” module students was used to train and evaluate the classification of a model developed in the KNIME Analytics (open source) data science platform. The accuracy of the model was measured using well-known evaluation criteria, such as precision, recall, and F-measure. The model was applied to data related to three basic courses and correctly predicted approximately 92% of students’ performance and, specifically, students who are likely to drop out or experience a delay before graduating. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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18 pages, 674 KiB  
Article
Spatial Impressions Monitoring during COVID-19 Pandemic Using Machine Learning Techniques
by Talal H. Noor, Abdulqader Almars, Ibrahim Gad, El-Sayed Atlam and Mahmoud Elmezain
Computers 2022, 11(4), 52; https://doi.org/10.3390/computers11040052 - 29 Mar 2022
Cited by 9 | Viewed by 2674
Abstract
During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. [...] Read more.
During the COVID-19 epidemic, Twitter has become a vital platform for people to express their impressions and feelings towards the COVID-19 epidemic. There is an unavoidable need to examine various patterns on social media platforms in order to reduce public anxiety and misconceptions. Based on this study, various public service messages can be disseminated, and necessary steps can be taken to manage the scourge. There has already been a lot of work conducted in several languages, but little has been conducted on Arabic tweets. The primary goal of this study is to analyze Arabic tweets about COVID-19 and extract people’s impressions of different locations. This analysis will provide some insights into understanding public mood variation on Twitter, which could be useful for governments to identify the effect of COVID-19 over space and make decisions based on that understanding. To achieve that, two strategies are used to analyze people’s impressions from Twitter: machine learning approach and the deep learning approach. To conduct this study, we scraped Arabic tweets up with 12,000 tweets that were manually labeled and classify them as positive, neutral or negative feelings. Specialising in Saudi Arabia, the collected dataset consists of 2174 positive tweets and 2879 negative tweets. First, TF-IDF feature vectors are used for feature representation. Then, several models are implemented to identify people’s impression over time using Twitter Geo-tag information. Finally, Geographic Information Systems (GIS) are used to map the spatial distribution of people’s emotions and impressions. Experimental results show that SVC outperforms other methods in terms of performance and accuracy. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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18 pages, 1154 KiB  
Article
Rating the Dominance of Concepts in Semantic Taxonomies
by Gerasimos Razis, Ioannis Anagnostopoulos and Hong Zhou
Computers 2022, 11(3), 35; https://doi.org/10.3390/computers11030035 - 2 Mar 2022
Cited by 1 | Viewed by 2531
Abstract
The descriptive concepts of “semantic” taxonomies are assigned to content items of the publishing domain for supporting a plethora of operations, mostly regarding the organization and discoverability of the content, as well as for recommendation tasks. However, either not all publishers rely on [...] Read more.
The descriptive concepts of “semantic” taxonomies are assigned to content items of the publishing domain for supporting a plethora of operations, mostly regarding the organization and discoverability of the content, as well as for recommendation tasks. However, either not all publishers rely on such structures, or in many cases employ their own proprietary taxonomies, thus the content is either difficult to be retrieved by the end users or stored in publisher-specific fragmented “data-silos”, respectively. To address these issues, the modular and scalable “Dominance Metric” methodology is proposed for rating the dominance and importance of concepts in semantic taxonomies. Our proposed metric is applied both on the vast multidisciplinary Microsoft Academic Graph Fields of Study taxonomy and the MeSH controlled vocabulary in order for their enhanced and refined versions to be produced. Moreover, we describe the cleansing process of the resulting taxonomy from Microsoft’s structure by deduplicating concepts and refining the hierarchical relations towards the increase of its representation quality. Our evaluation procedure provided valuable insights by showcasing that high volume, namely the number of publications a concept is assigned to, does not necessarily imply high influence, but the latter is also affected by the structural and topological properties of the individual entities. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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15 pages, 2508 KiB  
Article
Multimodal Lip-Reading for Tracheostomy Patients in the Greek Language
by Yorghos Voutos, Georgios Drakopoulos, Georgios Chrysovitsiotis, Zoi Zachou, Dimitris Kikidis, Efthymios Kyrodimos and Themis Exarchos
Computers 2022, 11(3), 34; https://doi.org/10.3390/computers11030034 - 28 Feb 2022
Cited by 2 | Viewed by 2838
Abstract
Voice loss constitutes a crucial disorder which is highly associated with social isolation. The use of multimodal information sources, such as, audiovisual information, is crucial since it can lead to the development of straightforward personalized word prediction models which can reproduce the patient’s [...] Read more.
Voice loss constitutes a crucial disorder which is highly associated with social isolation. The use of multimodal information sources, such as, audiovisual information, is crucial since it can lead to the development of straightforward personalized word prediction models which can reproduce the patient’s original voice. In this work we designed a multimodal approach based on audiovisual information from patients before loss-of-voice to develop a system for automated lip-reading in the Greek language. Data pre-processing methods, such as, lip-segmentation and frame-level sampling techniques were used to enhance the quality of the imaging data. Audio information was incorporated in the model to automatically annotate sets of frames as words. Recurrent neural networks were trained on four different video recordings to develop a robust word prediction model. The model was able to correctly identify test words in different time frames with 95% accuracy. To our knowledge, this is the first word prediction model that is trained to recognize words from video recordings in the Greek language. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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18 pages, 4829 KiB  
Article
Tangible and Personalized DS Application Approach in Cultural Heritage: The CHATS Project
by Giorgos Trichopoulos, John Aliprantis, Markos Konstantakis, Konstantinos Michalakis and George Caridakis
Computers 2022, 11(2), 19; https://doi.org/10.3390/computers11020019 - 31 Jan 2022
Cited by 10 | Viewed by 3395
Abstract
Storytelling is widely used to project cultural elements and engage people emotionally. Digital storytelling enhances the process by integrating images, music, narrative, and voice along with traditional storytelling methods. Newer visualization technologies such as Augmented Reality allow more vivid representations and further influence [...] Read more.
Storytelling is widely used to project cultural elements and engage people emotionally. Digital storytelling enhances the process by integrating images, music, narrative, and voice along with traditional storytelling methods. Newer visualization technologies such as Augmented Reality allow more vivid representations and further influence the way museums present their narratives. Cultural institutions aim towards integrating such technologies in order to provide a more engaging experience, which is also tailored to the user by exploiting personalization and context-awareness. This paper presents CHATS, a system for personalized digital storytelling in cultural heritage sites. Storytelling is based on a tangible interface, which adds a gamification aspect and improves interactivity for people with visual impairment. Technologies of AR and Smart Glasses are used to enhance visitors’ experience. To test CHATS, a case study was implemented and evaluated. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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22 pages, 2827 KiB  
Article
LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation
by Rafael Lahoz-Beltra and Claudia Corona López
Computers 2021, 10(12), 170; https://doi.org/10.3390/computers10120170 - 13 Dec 2021
Cited by 8 | Viewed by 3719
Abstract
Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in [...] Read more.
Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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23 pages, 2789 KiB  
Article
Ontology-Based Reasoning for Educational Assistance in Noncommunicable Chronic Diseases
by Andrêsa Vargas Larentis, Eduardo Gonçalves de Azevedo Neto, Jorge Luis Victória Barbosa, Débora Nice Ferrari Barbosa, Valderi Reis Quietinho Leithardt and Sérgio Duarte Correia
Computers 2021, 10(10), 128; https://doi.org/10.3390/computers10100128 - 12 Oct 2021
Cited by 5 | Viewed by 2608
Abstract
Noncommunicable chronic diseases (NCDs) affect a large part of the population. With the emergence of COVID-19, its most severe cases impact people with NCDs, increasing the mortality rate. For this reason, it is necessary to develop personalized solutions to support healthcare considering the [...] Read more.
Noncommunicable chronic diseases (NCDs) affect a large part of the population. With the emergence of COVID-19, its most severe cases impact people with NCDs, increasing the mortality rate. For this reason, it is necessary to develop personalized solutions to support healthcare considering the specific characteristics of individuals. This paper proposes an ontology to represent the knowledge of educational assistance in NCDs. The purpose of ontology is to support educational practices and systems oriented towards preventing and monitoring these diseases. The ontology is implemented under Protégé 5.5.0 in Ontology Web Language (OWL) format, and defined competency questions, SWRL rules, and SPARQL queries. The current version of ontology includes 138 classes, 31 relations, 6 semantic rules, and 575 axioms. The ontology serves as a NCDs knowledge base and supports automatic reasoning. Evaluations performed through a demo dataset demonstrated the effectiveness of the ontology. SWRL rules were used to define accurate axioms, improving the correct classification and inference of six instantiated individuals. As a scientific contribution, this study presents the first ontology for educational assistance in NCDs. Full article
(This article belongs to the Special Issue Social and Semantic Models, Tools and Applications in Science and Technology)
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