Knowledge Engineering Review - Impact Factor

Knowledge Engineering Review - Cambridge University Press | 2024 Impact Factor:2 | Cite Score:7.7 | Q2

Impact Factor and Journal Rank of Knowledge Engineering Review

About: The Knowledge Engineering Review is a peer-reviewed journal dedicated to the dissemination of high-quality research and developments in the field of knowledge engineering. The journal provides a platform for researchers, practitioners, and academics to share innovative ideas, methodologies, and technologies related to the acquisition, representation, processing, and utilization of knowledge in artificial intelligence and other related domains.
Objective:
The journal aims to advance the theoretical and practical aspects of knowledge engineering by publishing original research articles, comprehensive review papers, and significant technical advancements. It seeks to bridge the gap between theoretical research and practical applications, fostering the development of intelligent systems and knowledge-based technologies.
Focus Areas:
Topics covered in the Knowledge Engineering Review include, but are not limited to: Knowledge representation and reasoning Ontologies and semantic web technologies Machine learning and data mining Natural language processing Expert systems and decision support systems Knowledge acquisition and management Cognitive modeling and artificial intelligence Intelligent agents and multi-agent systems Knowledge-based system applications in various domains (healthcare, education, business, etc.) Advances in methodologies and tools for knowledge engineering
Impact:
The journal plays a significant role in the advancement of knowledge engineering by providing insights into the latest research, innovative methodologies, and practical applications. It influences the development of intelligent systems that effectively manage and utilize knowledge in various fields.
Significance:
The Knowledge Engineering Review is significant for promoting interdisciplinary collaboration and knowledge exchange among researchers, practitioners, and academics. By addressing both theoretical and practical aspects of knowledge engineering, it supports the creation of intelligent solutions that address complex problems and enhance decision-making processes.

Journal Home: Journal Homepage

Editor-in-Chief: Professor Peter McBurney

Scope: Knowledge Engineering Review Journal covers a broad spectrum of topics within knowledge engineering, including but not limited to:
Knowledge Representation: Techniques and methodologies for representing knowledge in various forms such as rules, frames, ontologies, and semantic networks.
Reasoning and Inference: Approaches to automated reasoning, including logical, probabilistic, and heuristic methods for deriving new knowledge from existing information.
Knowledge Acquisition: Methods for acquiring knowledge from experts, data, and other sources, including machine learning and data mining techniques.
Knowledge Management: Strategies for the organization, storage, retrieval, and dissemination of knowledge within organizations and systems.
Ontology Engineering: Design, development, and application of ontologies to support knowledge-based systems and semantic web technologies.
Intelligent Systems: Integration of knowledge engineering techniques in the development of intelligent systems, including expert systems, decision support systems, and intelligent agents.
Knowledge-Based Applications: Practical applications of knowledge engineering in various domains such as healthcare, finance, education, and manufacturing.
Natural Language Processing: Use of knowledge engineering in the understanding, generation, and translation of natural language.
Human-Computer Interaction: Enhancing the interaction between humans and computers through the application of knowledge engineering principles.
Emerging Trends: Exploration of new and emerging trends in knowledge engineering, including cognitive computing, big data analytics, and the integration of AI technologies.