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Applied Sciences
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State-of-the-Art of Knowledge Graphs and Their Applications
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State-of-the-Art of Knowledge Graphs and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1325

Special Issue Editors

Dr. Johannes Zenkert

E-Mail Website
Guest Editor
Institute of Knowledge-Based Systems and Knowledge Management, Department of Electrical Engineering and Computer Science, University of Siegen, 57068 Siegen, Germany
Interests: knowledge graphs; AI; Web; text and data mining; machine learning in natural language processing; sentiment analysis
Dr. Artur Jorge Ferreira

E-Mail Website
Guest Editor
Department of Electronics, Telecommunications, and Computers, Instituto Superior de Engenharia de Lisboa, and Instituto de Telecomunicações, 1049-001 Lisbon, Portugal
Interests: machine learning; pattern recognition; information theory; feature selection; feature discretization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Knowledge graphs are an established concept in the field of machine learning and artificial intelligence. They provide a way to link different data sources together to gain a holistic understanding of complex systems. Knowledge graphs are already used in many fields.

This Special Issue will highlight the application of knowledge graphs in practice. Different applications and fields of implementation, such as the automatic classification of entities, knowledge-graph-supported personalization and recommendations, or semantic searches in databases will be the focus.

Contributions should address the State-of-the-Art of knowledge graphs and present their relation to technical foundations, such as the use of ontologies, RDF and SPARQL, from an application-oriented perspective.

Furthermore, another important aspect of this Special Issue is the implementation of knowledge graphs in businesses. This involves the integration of different data sources and the development of novel algorithms to analyze the data.

The application of knowledge graphs also requires a high level of expertise and knowledge management, and close collaboration between data scientists, domain experts and business analysts.

Dr. Johannes Zenkert
Dr. Artur Jorge Ferreira
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • knowledge graphs
  • application
  • data integration
  • graph databases
  • data modelling
  • linked data
  • semantic search

Published Papers (2 papers)

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Research

16 pages, 751 KiB  
Article
Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph
by Xin Tian and Yuan Meng
Appl. Sci. 2024, 14(7), 3122; https://doi.org/10.3390/app14073122 - 08 Apr 2024
Viewed by 438
Abstract
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a [...] Read more.
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a novel KG reasoning framework. This framework introduces relation graphs to explicitly model the interactions between different relations, enabling more comprehensive and accurate handling of representation learning and reasoning tasks on KGs. Furthermore, we design a machine learning algorithm based on the attention mechanism to simultaneously optimize the original graph and its corresponding relation graph. Benchmark and experimental results on large-scale KGs demonstrate that the Relgraph framework improves KG reasoning performance. The framework exhibits a certain degree of versatility and can be seamlessly integrated with various traditional translation models. Full article
(This article belongs to the Special Issue State-of-the-Art of Knowledge Graphs and Their Applications)
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26 pages, 1692 KiB  
Article
High-Risk HPV Cervical Lesion Potential Correlations Mining over Large-Scale Knowledge Graphs
by Tiehua Zhou, Pengcheng Xu, Ling Wang and Yingxuan Tang
Appl. Sci. 2024, 14(6), 2456; https://doi.org/10.3390/app14062456 - 14 Mar 2024
Viewed by 437
Abstract
Lesion prediction, a very important aspect of cancer disease prediction, is an important marker for patients before they become cancerous. Currently, traditional machine learning methods are gradually applied in disease prediction based on patient vital signs data. Accurate prediction requires a large amount [...] Read more.
Lesion prediction, a very important aspect of cancer disease prediction, is an important marker for patients before they become cancerous. Currently, traditional machine learning methods are gradually applied in disease prediction based on patient vital signs data. Accurate prediction requires a large amount and high quality of data, however, the difficulty in obtaining and incompleteness of electronic medical record (EMR) data leads to certain difficulties in disease prediction by traditional machine learning methods. Secondly, there are many factors that contribute to the development of cervical lesions, some risk factors are directly related to it while others are indirectly related to them. In addition, risk factors have an interactive effect on the development of cervical lesions; it does not occur in isolation, a large-scale knowledge graph is constructed base on the close relationships among risk factors in the literature, and new potential key risk factors are mined based on common risk factors through a subgraph mining method. Then lesion prediction algorithm is proposed to predict the likelihood of lesions in patients base on the set of key risk factors. Experimental results show that the circumvents the problems of large number of missing values in EMR data and discovered key risk factors that are easily ignored but have better prediction effect. Therefore, The method had better accuracy in predicting cervical lesions. Full article
(This article belongs to the Special Issue State-of-the-Art of Knowledge Graphs and Their Applications)
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