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https://www.mdpi.com/journal/applsci/special_issues/UTU27315R2

Application of Knowledge Graph in Communication Engineering

• Special Issue Editors

• Special Issue Information

• Keywords

• Published Papers

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 December 2024 | Viewed by 43

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Special Issue Editor

Dr. Yucong Duan

E-Mail WebsiteGuest Editor

College of Science and Information Technology, Hainan University, Haikou, ChinaInterests: information security; artificial intelligence; big data; software engineeringSpecial Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of communication engineering is evolving rapidly with the advent of advanced technologies like Knowledge Graphs, which organize and integrate complex data for improved decision making and automation. This Special Issue aims to explore the latest developments, challenges, and applications of Knowledge Graphs in communication engineering. We invite high-quality, original research papers that address theoretical, practical, and implementation aspects.

To appreciate the application of Knowledge Graphs in communication engineering, it is essential to understand the DIKWP model (data, information, knowledge, wisdom, and purpose) and its role in cognitive processes.

1. Data Graph

Data represent raw facts or observations that need classification in a conceptual or semantic space to be meaningful.

• Semantic definition: Data convey "identical" meanings within cognitive processes. They are identified and confirmed by matching them with pre-existing cognitive structures.

• Processing: semantic matching and conceptual confirmation, extracting features from data for classification and identification;

• Mathematical representation: data can be described as a set of semantic attributes, S={f₁,f₂,...,f_n}, where f_i represents a feature.

A Data Graph structures these semantic attributes into nodes and edges, linking raw data points to their contextual meanings.

2. Information Graph

Information is processed data, expressing "different" semantic meanings in cognition.

• Semantic definition: information represents differentiated semantic elements linked through specific purposes in cognitive space;

• Processing: input recognition, semantic matching, classification, and the generation of new semantics;

• Mathematical representation: information is the function I:X→Y, where X represents DIKWP content and Y is the new semantic association.

An Information Graph captures these new semantic associations, showing the relationships and differences between various pieces of information.

3. Knowledge Graph

Knowledge integrates information into coherent, comprehensive semantic structures through abstraction and validation.

• Semantic definition: knowledge represents "complete" semantic structures formed through hypotheses and abstraction;

• Processing: observation, learning, hypothesis formation, and validation to understand and explain objects;

• Mathematical representation: knowledge is a semantic network K=(N,E) where N denotes concepts and E their relationships.

A Knowledge Graph organizes these concepts and their inter-relationships, providing a deeper understanding and context.

4. Wisdom Graph

Wisdom is the application of knowledge ethically and socially, emphasizing decision making's comprehensive and goal-oriented nature.

• Semantic definition: wisdom involves value-laden information from cultural and societal contexts, stressing ethical responsibility;

• Processing: integrating ethics, morality, social responsibility, and feasibility into decision making;

• Mathematical representation: wisdom can be represented as a decision function W:{D,I,K,W,P}→D* where the inputs are DIKWP content and the output is the optimal decision.

A Wisdom Graph visualizes the integration of ethical, social, and practical considerations into the decision making process.

5. Purpose Graph

Purpose directs DIKWP content processing toward specific goals, reflecting stakeholders' understanding and desired outcomes.

• Semantic definition: purpose is defined as a pair (input and output), representing understanding phenomena (input) and desired outcomes (output);

• Processing: transforming DIKWP content based on goals through learning and adaptation;

• Mathematical representation: Purpose is P=(Input,Output) with a transformation function T:Input→Output.

A Purpose Graph maps inputs to desired outcomes, guiding the processing of DIKWP content towards achieving specific objectives.

Submissions are encouraged on a range of topics related to Knowledge Graphs in communication engineering, including, but not limited to, the following:

• Design and development of Knowledge Graphs for communication systems;

• Knowledge Graph-based network optimization and management;

• Enhancing network security using Knowledge Graphs;

• Applications of Knowledge Graphs in wireless communication;

• Integrating Knowledge Graphs with IoT and 5G networks;

• Knowledge Graphs for cognitive radio networks;

• Semantic data integration and analysis in communication networks;

• Machine learning and data mining with Knowledge Graphs for communication engineering;

• Real-time decision making and automation in communication networks using Knowledge Graphs;

• Case studies and real-world applications of Knowledge Graphs in communication systems.

Dr. Yucong DuanGuest 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. 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

• knowledge graph-based network

• communication systems

• communication networks

Published Papers

This special issue is now open for submission.