Knowledge and Information Extraction Research

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1065

Special Issue Editors


E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: data mining; natural language processing; graph neural network
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
Interests: data mining; data privacy; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of "Knowledge and Information Extraction Research" is characterized by its focus on advancing methods and techniques for extracting structured knowledge from diverse data sources. While this area has explored various aspects of knowledge extraction, there is a continued need to address complex challenges and enhance the practical applications of these methods.

This Special Issue aims to contribute to the evolution of knowledge and information extraction by fostering innovative research. It invites researchers to explore novel methodologies and tools that can improve the precision and efficiency of knowledge extraction processes. The scope includes interdisciplinary collaboration, drawing on expertise from fields like natural language processing, machine learning, data mining, and information retrieval.

The main purposes of this Special Issue are to advance collective knowledge in this field, introduce methodological innovations, and emphasize the practical relevance of knowledge extraction. Research within this issue is not limited to academic exploration but also seeks to bridge the gap between theory and real-world problem-solving in industries such as healthcare, finance, e-commerce, and more.

Topics of interest include, but are not limited to, the following:

  • Advanced knowledge extraction techniques;
  • Interdisciplinary approaches;
  • Practical applications in diverse domains;
  • Methodological innovations;
  • Bridging the gap between academia and industry;
  • Collaborative efforts to enhance knowledge extraction.

The aim of this Special Issue is to contribute to the existing literature by consolidating knowledge, introducing new methodologies, and promoting the application of information extraction techniques to real-world challenges. It provides a platform for researchers to share their insights and advancements, shaping the future of knowledge and information extraction.

We invite contributions that expand the boundaries of knowledge extraction and encourage researchers to explore the potential of these techniques in practical contexts. Your submissions will play a crucial role in advancing this field and addressing the ever-evolving demands for structured knowledge from a wide range of data sources.

Dr. Lanting Fang
Dr. Yubo Song
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. Electronics 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

  • data mining
  • information retrieval
  • machine learning
  • information extraction
  • techniques
  • knowledge graphs

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Published Papers (1 paper)

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Research

12 pages, 1128 KiB  
Article
Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing
by Dongsheng Jing, Yu Yang, Zhimin Gu, Renjun Feng, Yan Li and Haitao Jiang
Electronics 2024, 13(17), 3412; https://doi.org/10.3390/electronics13173412 - 28 Aug 2024
Viewed by 710
Abstract
With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely [...] Read more.
With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposing an innovative data network propagation path tracing model, which is constructed based on graph convolutional networks (GCNs) and the BERT model. Firstly, propagation trees are constructed based on the propagation structure, and the key attributes of data nodes are extracted and screened. Then, GCNs are utilized to learn the representation of node features with different attribute feature combinations in the propagation path graph, while the Bidirectional Encoder Representations from Transformers (BERT) model is employed to capture the deep semantic features of the original text content. The core of this research is to effectively integrate these two feature representations, namely the structural features obtained by GCNs and the semantic features obtained by the BERT model, in order to enhance the ability of the model to recognize the data propagation path. The experimental results demonstrate that this model performs well in power data propagation and tracing tasks, and the data recognition accuracy reaches 92.5%, which is significantly better than the existing schemes. This achievement not only improves the power system’s ability to cope with data security threats but also provides strong support for protecting data transmission security and privacy. Full article
(This article belongs to the Special Issue Knowledge and Information Extraction Research)
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