International Journal of Semantic Computing

Scope & Guideline

Transforming Knowledge Through Semantic Innovation

Introduction

Welcome to the International Journal of Semantic Computing 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 International Journal of Semantic Computing, 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
ISSN1793-351x
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
Support Open AccessNo
CountrySingapore
TypeJournal
Convergefrom 2007 to 2024
AbbreviationINT J SEMANT COMPUT / Int. J. Semant. Comput.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE

Aims and Scopes

The International Journal of Semantic Computing focuses on the intersection of semantic technologies, artificial intelligence, and computing methodologies. It aims to advance the understanding and application of semantic computing across various domains, promoting interdisciplinary research that integrates knowledge representation, machine learning, and intelligent systems.
  1. Semantic Technologies and Knowledge Representation:
    The journal emphasizes the development and application of semantic technologies, including knowledge graphs, ontologies, and semantic networks, to facilitate better data understanding and interoperability.
  2. Artificial Intelligence and Machine Learning:
    A core area of focus is the application of AI and machine learning techniques to enhance semantic computing, including natural language processing, computer vision, and reinforcement learning.
  3. Transdisciplinary Approaches:
    The journal encourages research that bridges multiple disciplines, such as healthcare, robotics, and environmental studies, using semantic computing as a foundational tool.
  4. Real-World Applications:
    There is a consistent focus on practical applications of semantic computing in various sectors, including smart cities, healthcare, and multimedia processing, demonstrating the technology's impact on real-world challenges.
  5. Innovative Algorithms and Methodologies:
    The journal showcases innovative algorithms and methodologies that advance the field of semantic computing, including deep learning architectures and graph-based learning techniques.
Recent publications in the International Journal of Semantic Computing reveal emerging themes and trends that reflect the evolving landscape of semantic technologies and their applications. These trends highlight areas with increasing research interest and potential for future exploration.
  1. Generative AI and Large Language Models (LLMs):
    With the rise of LLMs, there is a growing focus on their capabilities, limitations, and potential applications, indicating a significant shift in how researchers approach natural language processing.
  2. Transdisciplinary AI Applications:
    Research is increasingly oriented towards transdisciplinary applications of AI, particularly in sectors like healthcare and environmental science, showcasing the versatility of semantic computing.
  3. Real-Time Data Processing and Analysis:
    There is an emerging trend toward methodologies that support real-time data processing and analysis, particularly in contexts like live video streaming and dynamic sensor data.
  4. Explainable AI and Trustworthy Systems:
    An increasing number of papers address the need for explainability and transparency in AI systems, reflecting a growing concern for ethical considerations and user trust in AI technologies.
  5. Advanced Graph-Based Techniques:
    Graph neural networks and other graph-based methodologies are gaining traction as researchers explore their potential in various applications, including image classification and knowledge graph construction.

Declining or Waning

While the International Journal of Semantic Computing has seen a broad range of research topics, certain themes appear to be declining in prominence. These waning scopes suggest a shift in focus towards more contemporary issues within the field.
  1. Traditional Rule-Based Systems:
    Research on traditional rule-based systems has decreased as newer, more dynamic AI approaches gain traction. This shift reflects a broader trend towards machine learning and data-driven methodologies.
  2. Basic Ontology Development:
    The emphasis on developing basic ontologies without application context is waning. Researchers are now prioritizing ontologies that are integrated with advanced AI techniques and real-world applications.
  3. Single-Domain Applications:
    There is a noticeable decline in studies focused solely on single-domain applications of semantic computing. Instead, interdisciplinary and transdisciplinary studies are becoming more prevalent.
  4. Static Data Integration Techniques:
    As the field evolves, static approaches to data integration are less favored compared to dynamic, context-aware systems that leverage real-time data and machine learning.

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