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Electronics
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Knowledge Representation and Reasoning in Artificial Intelligence
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Knowledge Representation and Reasoning in Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 1473

Special Issue Editors

Dr. Suping Xu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: artificial Intelligence; machine learning; data mining
Dr. Hengrong Ju

E-Mail Website
Guest Editor
School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China
Interests: artificial intelligence; knowledge discovery; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Keyu Liu

E-Mail Website
Guest Editor
School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Interests: machine learning; data mining; uncertainty reasoning

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has witnessed remarkable advancements, driven by advanced techniques in knowledge representation and reasoning. These techniques are fundamental to AI systems, enabling them to model complex scenarios, make informed decisions, and exhibit human-like judgment. Knowledge representation provides a structured framework for organizing and processing information, while reasoning mechanisms facilitate logical inference and problem-solving, essential for autonomous agents and intelligent systems. This Special Issue aims to explore recent developments and emerging trends in knowledge representation and reasoning in artificial intelligence. It seeks to showcase innovative research, addressing theoretical challenges and demonstrating practical applications in real-world scenarios. The issue aims to foster collaboration and the exchange of ideas among researchers and practitioners, driving forward the field of AI and its impact on society.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  1. Cognitive architectures and knowledge representation.
  2. Deep knowledge representation and learning.
  3. Explainable AI with knowledge representation.
  4. Graph-based knowledge representation.
  5. Knowledge graphs and their applications.
  6. Knowledge-based systems.
  7. Probabilistic reasoning.
  8. Reasoning under uncertainty.
  9. Knowledge representation in natural language processing.
  10. Knowledge representation in computer vision.
  11. Knowledge representation in audio analysis.
  12. Knowledge representation for big data.

We look forward to receiving your contributions.

Dr. Suping Xu
Dr. Hengrong Ju
Dr. Keyu Liu
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

  • interpretable artificial intelligence
  • knowledge representation and reasoning
  • deep learning and neural networks
  • pattern recognition
  • computer vision and video understanding
  • audio analysis
  • natural language processing
  • AI4Science

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Published Papers (2 papers)

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Research

24 pages, 15110 KiB  
Article
Embedding Hierarchical Tree Structure of Concepts in Knowledge Graph Embedding
by Jibin Yu, Chunhong Zhang, Zheng Hu and Yang Ji
Electronics 2024, 13(22), 4486; https://doi.org/10.3390/electronics13224486 - 15 Nov 2024
Viewed by 273
Abstract
Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods [...] Read more.
Knowledge Graph Embedding aims to encode both entities and relations into a continuous low-dimensional vector space, which is crucial for knowledge-driven application scenarios. As abstract entities in knowledge graphs, concepts inherently possess unique hierarchical structures and encompass rich semantic information. Although existing methods for jointly embedding concepts and instances achieve promising performance, they still face two issues: (1) They fail to explicitly reconstruct the hierarchical tree structure of concepts in the embedding space; (2) They ignore disjoint concept pairs and overlapping concept pairs derived from concepts. In this paper, we propose a novel concept representation approach, called Hyper Spherical Cone Concept Embedding (HCCE), to explicitly model the hierarchical tree structure of concepts in the embedding space. Specifically, HCCE represents each concept as a hyperspherical cone and each instance as a vector, maintaining the anisotropy of concept embeddings. We propose two variant methods to explore the impact of embedding concepts and instances in the same or different spaces. Moreover, we design score functions for disjoint concept pairs and overlapping concept pairs, using relative position relations to incorporate them seamlessly into our geometric models. Experimental results on three benchmark datasets show that HCCE outperforms most existing state-of-the-art methods on concept-related triples and achieves competitive results on instance-related triples. The visualization of embedding results intuitively shows the hierarchical tree structure of concepts in the embedding space. Full article
(This article belongs to the Special Issue Knowledge Representation and Reasoning in Artificial Intelligence)
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26 pages, 5233 KiB  
Article
Prompt Update Algorithm Based on the Boolean Vector Inner Product and Ant Colony Algorithm for Fast Target Type Recognition
by Quan Zhou, Jie Shi, Qi Wang, Bin Kong, Shang Gao and Weibo Zhong
Electronics 2024, 13(21), 4243; https://doi.org/10.3390/electronics13214243 - 29 Oct 2024
Viewed by 492
Abstract
In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining [...] Read more.
In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition. Full article
(This article belongs to the Special Issue Knowledge Representation and Reasoning in Artificial Intelligence)
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