Application of Knowledge Graph in Communication Engineering
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 July 2025 | Viewed by 6014
Special Issue Editor
Interests: information security; artificial intelligence; big data; software engineering
Special 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,...,fn}, where fi 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 Duan
Guest Editor
Manuscript Submission Information
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Keywords
- knowledge graphs
- knowledge graph-based network
- communication systems
- communication networks
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