KNOWLEDGE-BASED SYSTEMS

Scope & Guideline

Shaping the Future of Decision-Making Through Interdisciplinary Research

Introduction

Welcome to the KNOWLEDGE-BASED SYSTEMS information hub, where our guidelines provide a wealth of knowledge about the journal’s focus and academic contributions. This page includes an extensive look at the aims and scope of KNOWLEDGE-BASED SYSTEMS, highlighting trending and emerging areas of study. We also examine declining topics to offer insight into academic interest shifts. Our curated list of highly cited topics and recent publications is part of our effort to guide scholars, using these guidelines to stay ahead in their research endeavors.
LanguageEnglish
ISSN0950-7051
PublisherELSEVIER
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1987 to 2024
AbbreviationKNOWL-BASED SYST / Knowledge-Based Syst.
Frequency8 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'KNOWLEDGE-BASED SYSTEMS' focuses on the intersection of knowledge representation, artificial intelligence, and machine learning, emphasizing the development and application of intelligent systems that leverage knowledge-based techniques. Its methodologies span a diverse range of computational techniques, including deep learning, reinforcement learning, and statistical approaches, to solve complex problems across various domains.
  1. Knowledge Representation and Reasoning:
    Research in this area explores how knowledge can be represented in a form that a computer system can utilize to solve complex tasks. This includes the use of ontologies, knowledge graphs, and logical reasoning systems.
  2. Machine Learning and Data Mining:
    The journal features studies on advanced machine learning techniques and data mining methods, focusing on their application to various datasets for classification, regression, and anomaly detection.
  3. Artificial Intelligence Applications:
    Papers often address the application of AI techniques in real-world scenarios, including healthcare, finance, and industrial processes, showcasing innovative solutions that enhance operational efficiency and decision-making.
  4. Human-Computer Interaction:
    Research on how knowledge-based systems can improve user experience and interaction, emphasizing the importance of explainability and user-centered design in intelligent systems.
  5. Multi-Agent Systems:
    The journal also covers the development of systems where multiple agents interact, focusing on cooperation, negotiation, and collective problem-solving strategies.
  6. Optimization Techniques:
    A significant focus on optimization methods, including evolutionary algorithms and reinforcement learning, applied to various engineering and computational problems.
The journal is currently witnessing a surge in interest in several emerging themes, reflecting the changing landscape of knowledge-based systems and their applications.
  1. Explainable AI (XAI):
    A growing focus on explainability in AI systems, as researchers seek methods to make machine learning models more interpretable and understandable to users, particularly in critical applications such as healthcare and finance.
  2. Federated Learning and Privacy Preservation:
    An increasing number of studies are dedicated to federated learning, emphasizing privacy-preserving techniques that allow for collaborative learning without compromising sensitive data.
  3. Integration of Multi-Modal Data:
    Research is trending towards the integration of various data modalities (e.g., text, images, and audio) to enhance the performance of knowledge-based systems, particularly in sentiment analysis and recommendation systems.
  4. Dynamic Graph Representation Learning:
    Emerging interest in graph neural networks and dynamic graph representations to model complex relationships and interactions in data, especially for social networks and knowledge graphs.
  5. Reinforcement Learning in Complex Environments:
    There is a notable trend towards applying reinforcement learning techniques in complex, real-world scenarios, such as robotics and autonomous systems, where adaptive decision-making is crucial.

Declining or Waning

As the field evolves, certain themes within 'KNOWLEDGE-BASED SYSTEMS' are witnessing a decline in prominence. This shift may reflect changes in research priorities or emerging technologies that overshadow older methodologies.
  1. Traditional Rule-Based Systems:
    There is a noticeable decrease in publications focusing on traditional rule-based systems, as researchers increasingly favor more flexible and adaptive machine learning approaches that can handle uncertainty and large datasets.
  2. Simple Statistical Methods:
    The reliance on basic statistical methods is waning, with a shift towards more complex models that incorporate deep learning and other advanced techniques to tackle challenging problems.
  3. Standalone Knowledge-Based Systems:
    Research on isolated knowledge-based systems that do not integrate with machine learning or deep learning techniques is becoming less common, as interdisciplinary approaches gain traction.

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