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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: closed (20 September 2024) | Viewed by 8392

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

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Published Papers (4 papers)

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Research

20 pages, 2918 KiB  
Article
A Text Generation Method Based on a Multimodal Knowledge Graph for Fault Diagnosis of Consumer Electronics
by Yuezhong Wu, Yuxuan Sun, Lingjiao Chen, Xuanang Zhang and Qiang Liu
Appl. Sci. 2024, 14(21), 10068; https://doi.org/10.3390/app142110068 - 4 Nov 2024
Cited by 1 | Viewed by 1328
Abstract
As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics [...] Read more.
As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies. Full article
(This article belongs to the Special Issue State-of-the-Art of Knowledge Graphs and Their Applications)
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13 pages, 3001 KiB  
Article
Concurrent Access Performance Comparison Between Relational Databases and Graph NoSQL Databases for Complex Algorithms
by Elena Lupu, Adriana Olteanu and Anca Daniela Ionita
Appl. Sci. 2024, 14(21), 9867; https://doi.org/10.3390/app14219867 - 28 Oct 2024
Viewed by 2371
Abstract
Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its [...] Read more.
Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability. Full article
(This article belongs to the Special Issue State-of-the-Art of Knowledge Graphs and Their Applications)
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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 - 8 Apr 2024
Viewed by 2286
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
Cited by 1 | Viewed by 1204
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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