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Future Internet
Special Issues
Applications of Semantic Web, Linked Open Data and Knowledge Graphs
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Applications of Semantic Web, Linked Open Data and Knowledge Graphs

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 8087

Special Issue Editor

Dr. András Micsik

E-Mail Website
Guest Editor
Department of Distributed Systems, MTA SZTAKI, Budapest, Hungary
Interests: linked data; knowledge graphs; data repositories; open data; metadata vocabularies; semantic interoperability; digital libraries

Special Issue Information

Dear Colleagues,

The concept of the Semantic Web aims at machine processable and understandable data on the Internet. Based on the Semantic Web, Linked Data and its 5-star deployment scheme is a general foundation and roadmap for accessible and interoperable data. Linked Data gave momentum to new solutions in many areas, including government data, broadcasting, libraries, cultural heritage, biology, advertising, etc. A natural follow-up of the Semantic Web concept is the knowledge graph, which plays a more and more important role in both enterprises and everyday Internet usage. Ontologies and inferencing over Linked Data enable the construction of intelligent services and better information access. In this Special Issue, we are looking for new results or experience reports in the area, especially in the following topics:

  • Innovative applications using Linked Data or knowledge graphs;
  • Services mixing deep learning and ontologies (Semantic AI);
  • Semantic data lakes, big semantic data;
  • New intelligent solutions using ontologies or reasoning;
  • Emerging interoperability formats and solutions;
  • Distributed platforms with distributed knowledge;
  • Semantic Web criticism, amendments and alternative solutions.

Dr. András Micsik
Guest Editor

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

  • Linked Data
  • Semantic Web
  • Knowledge graphs
  • Semantic interoperability
  • Data on the web
  • Semantic AI
  • Ontologies
  • Reasoning
  • Knowledge management
  • Semantic data lakes

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Published Papers (2 papers)

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Research

15 pages, 41589 KiB  
Article
Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
by Ghazal Faraj and András Micsik
Future Internet 2021, 13(11), 277; https://doi.org/10.3390/fi13110277 - 29 Oct 2021
Cited by 7 | Viewed by 3304
Abstract
In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning [...] Read more.
In order to unify access to multiple heterogeneous sources of cultural heritage data, many datasets were mapped to the CIDOC-CRM ontology. CIDOC-CRM provides a formal structure and definitions for most cultural heritage concepts and their relationships. The COURAGE project includes historic data concerning people, organizations, cultural heritage collections, and collection items covering the period between 1950 and 1990. Therefore, CIDOC-CRM seemed the optimal choice for describing COURAGE entities, improving knowledge sharing, and facilitating the COURAGE dataset unification with other datasets. This paper introduces the results of translating the COURAGE dataset to CIDOC-CRM semantically. This mapping was implemented automatically according to predefined mapping rules. Several SPARQL queries were applied to validate the migration process manually. In addition, multiple SHACL shapes were conducted to validate the data and mapping models. Full article
(This article belongs to the Special Issue Applications of Semantic Web, Linked Open Data and Knowledge Graphs)
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17 pages, 1170 KiB  
Article
An Ontology-Driven Personalized Faceted Search for Exploring Knowledge Bases of Capsicum
by Zaenal Akbar, Hani Febri Mustika, Dwi Setyo Rini, Lindung Parningotan Manik, Ariani Indrawati, Agusdin Dharma Fefirenta and Tutie Djarwaningsih
Future Internet 2021, 13(7), 172; https://doi.org/10.3390/fi13070172 - 30 Jun 2021
Cited by 6 | Viewed by 2811
Abstract
Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It [...] Read more.
Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain. Full article
(This article belongs to the Special Issue Applications of Semantic Web, Linked Open Data and Knowledge Graphs)
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