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Future Internet
Special Issues
New Perspectives on Semantic Web Technologies and Applications
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New Perspectives on Semantic Web Technologies and Applications

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 21489

Special Issue Editors

Prof. Dr. Rafael Valencia-Garcia

E-Mail Website
Guest Editor
Department of Informatics and Systems, Faculty of Informatics, University of Murcia, 30100 Murcia, Spain
Interests: natural language processing; semantic web technologies; ontologies; knowledge acquisition; recommender systems; opinion mining; social semantic web
Prof. Dr. Giner Alor-Hernández

E-Mail Website
Guest Editor
Tecnológico Nacional de México/ I. T. Orizaba, Orizaba 94320, Mexico
Interests: big data; Internet of Things; knowledge management; software engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Mario Andrés Paredes-Valverde

E-Mail Website
Guest Editor
Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 No. 852, Col. E. Zapata, Orizaba 94320, Veracruz, Mexico
Interests: natural language processing; information retrieval; ontologies; opinion mining; semantic web technologies, knowledge-based decision support systems

Special Issue Information

Dear Colleagues,

Semantic web technologies have been active in recent years and have attracted interest among researchers. Such technologies allow software agents as well as humans to understand and interpret the meaning of data in order to perform sophisticated tasks. Furthermore, they have had a considerable impact on data and knowledge analysis and sharing. Semantic web technologies have become a key driver for innovation and change in many domains, including eCommerce, education, eGovernment, entertainment, healthcare, social networks, and software engineering, among others. For instance, ontologies, a fundamental pillar of the semantic web, have been successfully applied to develop or improve systems addressing knowledge-based tasks and processes. Moreover, leveraging ontologies towards interoperability and model-based systems allows the current lack of common understanding among systems and applications to be dealt with efficiently.

Several novel areas leveraging semantic web technologies have emerged. These include, among others: Industry 4.0, social web, geospatial semantics, ontology-based data access, knowledge-based decision support systems, and semantic web of things. These areas have given way to new needs in different domains. Therefore, semantic web-based systems have to evolve and adapt to each domain and application. In this sense, researchers from semantic web areas such as ontological engineering, knowledge representation and reasoning, ontology alignment, ontology population, ontology matching, and others are working intensively to develop innovative systems as solutions to emerging needs. However, the evolution and specialization of this kind of system seem to be moderate. To accelerate this process, original research on advanced semantic web technologies, innovative designs that combine semantic web technologies with other technologies, research efforts that deploy semantic web-based systems, and the exploration of new semantic web technologies are necessary.

This Special Issue aims to collect innovative and high-quality research contributions that illustrate how semantic web technologies can benefit from the interaction of different disciplines, such as artificial intelligence, Big Data, cloud computing, knowledge engineering, machine learning, natural language processing, and social web, among others, to provide effective solutions to currently unresolved key problems. This Special Issue is soliciting original, conceptual, theoretical, and experimental scientific contributions discussing and treating the development of semantic web-based applications.

Relevant topics include, but are not restricted to:

  • Application of machine learning and Big Data analytics;
  • Consumption and publication of linked data;
  • Context-aware system using semantic web technologies;
  • Data mining for web of data;
  • Industry 4.0 implementation and real-world case studies;
  • Integration of heterogeneous data sources;
  • Ontology-based data access;
  • Knowledge-based decision support systems;
  • Knowledge-based recommender systems;
  • Knowledge representation;
  • Machine learning for web of data;
  • Mobile web;
  • Natural language processing;
  • Ontological engineering;
  • Ontology population;
  • Semantic web of things;
  • Semantic web-based technologies for Big Data;
  • Sentiment analysis and semantic web;
  • Geospatial semantics;
  • Social networks and graph analysis;
  • Social web and semantic web.

Prof. Dr. Rafael Valencia-Garcia
Dr. Giner Alor-Hernández
Dr. Mario Andrés Paredes-Valverde
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. Future Internet 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 1600 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.

Keywords

  • data sources integration
  • knowledge engineering
  • ontologies
  • ontological engineering
  • knowledge-based systems
  • semantic technologies and ontologies
  • semantic web
  • social web
  • semantic web-based applications
  • semantic web of things

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

Published Papers (4 papers)

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Research

10 pages, 623 KiB  
Article
Semantic Search Enhanced with Rating Scores
by Anna Formica, Elaheh Pourabbas and Francesco Taglino
Future Internet 2020, 12(4), 67; https://doi.org/10.3390/fi12040067 - 15 Apr 2020
Cited by 6 | Viewed by 3442
Abstract
This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the [...] Read more.
This paper presents SemSime, a method based on semantic similarity for searching over a set of digital resources previously annotated by means of concepts from a weighted reference ontology. SemSime is an enhancement of SemSim and, with respect to the latter, it uses a frequency approach for weighting the ontology, and refines both the user request and the digital resources with the addition of rating scores. Such scores are High, Medium, and Low, and in the user request indicate the preferences assigned by the user to each of the concepts representing the searching criteria, whereas in the annotation of the digital resources they represent the levels of quality associated with each concept in describing the resources. The SemSime has been evaluated and the results of the experiment show that it performs better than SemSim and an evolution of it, referred to as S e m S i m R V . Full article
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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24 pages, 5127 KiB  
Article
Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism
by Jingren Zhang, Fang’ai Liu, Weizhi Xu and Hui Yu
Future Internet 2019, 11(11), 237; https://doi.org/10.3390/fi11110237 - 12 Nov 2019
Cited by 30 | Viewed by 6789
Abstract
Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual [...] Read more.
Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method. Full article
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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23 pages, 1739 KiB  
Article
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
by Paul Sheridan, Mikael Onsjö, Claudia Becerra, Sergio Jimenez and George Dueñas
Future Internet 2019, 11(9), 182; https://doi.org/10.3390/fi11090182 - 22 Aug 2019
Cited by 11 | Viewed by 4651
Abstract
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems [...] Read more.
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content. Full article
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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23 pages, 2514 KiB  
Article
myDIG: Personalized Illicit Domain-Specific Knowledge Discovery with No Programming
by Mayank Kejriwal and Pedro Szekely
Future Internet 2019, 11(3), 59; https://doi.org/10.3390/fi11030059 - 4 Mar 2019
Cited by 9 | Viewed by 5513
Abstract
With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an [...] Read more.
With advances in machine learning, knowledge discovery systems have become very complicated to set up, requiring extensive tuning and programming effort. Democratizing such technology so that non-technical domain experts can avail themselves of these advances in an interactive and personalized way is an important problem. We describe myDIG, a highly modular, open source pipeline-construction system that is specifically geared towards investigative users (e.g., law enforcement) with no programming abilities. The myDIG system allows users both to build a knowledge graph of entities, relationships, and attributes for illicit domains from a raw HTML corpus and also to set up a personalized search interface for analyzing the structured knowledge. We use qualitative and quantitative data from five case studies involving investigative experts from illicit domains such as securities fraud and illegal firearms sales to illustrate the potential of myDIG. Full article
(This article belongs to the Special Issue New Perspectives on Semantic Web Technologies and Applications)
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