Semantic Web - IOS Press - Impact Factor

Semantic Web - IOS Press | 2024 Impact Factor:2.9 | Cite Score:7.1 | Q2

Impact Factor and Journal Rank of Semantic Web

About: Semantic Web Journal is a peer-reviewed academic journal published by IOS Press. It focuses on research related to the semantic web, including technologies, methodologies, and applications that enhance the interoperability and understanding of web data. The journal provides a platform for researchers, practitioners, and educators to publish original research articles, reviews, and case studies that advance the development and deployment of semantic web technologies.
Objective: The primary objective of Semantic Web Journal is to promote research and innovation in the field of semantic web technologies. The journal aims to explore novel approaches, tools, and applications that enhance the semantic understanding of web data, facilitate knowledge representation, and improve data interoperability across diverse domains. By publishing high-quality research, the journal contributes to the advancement of semantic web methodologies and their impact on the web functionality and usability.
Interdisciplinary Focus: Semantic Web Journal adopts an interdisciplinary approach, welcoming contributions from various fields related to the semantic web, including but not limited to: Computer Science Artificial Intelligence Knowledge Engineering Data Science Information Systems Web Technologies Ontology Engineering Natural Language Processing Database Systems Social Web and Web Mining This interdisciplinary perspective fosters collaboration and innovation, leading to the development of advanced semantic web technologies and solutions that address complex challenges in data interoperability, knowledge management, and web applications.
Global Reach and Impact: With a broad international readership and authorship, Semantic Web Journal has a global reach and impact. Its publications contribute to the dissemination of knowledge and advancements in semantic web technologies worldwide. The journal content influences both academic research and practical applications, driving progress in areas such as linked data, semantic annotation, and knowledge graphs.
High Standards and Rigorous Review: Maintaining high academic standards, Semantic Web Journal conducts a rigorous peer-review process. Each submitted manuscript undergoes thorough evaluation by experts in the field to ensure the quality, originality, and scientific rigor of the research. This stringent review process upholds the integrity and reputation of the journal, ensuring that only high-quality and impactful research is published.
Significance: Semantic Web Journal plays a significant role in advancing research and practice in semantic web technologies. By providing a platform for the publication of cutting-edge research findings, the journal contributes to the growth of knowledge and innovation in semantic web methodologies. It serves as an essential resource for researchers, practitioners, and educators seeking to leverage semantic web technologies to enhance data interoperability, knowledge sharing, and the overall functionality of the web.

Journal Home: Journal Homepage

Editor-in-Chief: Prof. Dr. Krzysztof Janowicz

Scope: The Semantic Web Journal, published by IOS Press, is a peer-reviewed academic journal focusing on research related to the Semantic Web, knowledge representation, and ontologies. It serves as a platform for researchers and practitioners to publish their innovative work in areas that include semantic technologies, Linked Data, ontological engineering, and knowledge graphs. Here is an overview of its key focus areas and scope:
1. Semantic Technologies:
Research on foundational semantic technologies and standards, including RDF (Resource Description Framework), OWL (Web Ontology Language), SPARQL (SPARQL Protocol and RDF Query Language), and SHACL (Shapes Constraint Language).
Advancements in semantic annotation, metadata, and semantic interoperability.
2. Knowledge Representation:
Studies on formalisms and methodologies for knowledge representation and reasoning on the Semantic Web.
Research on description logics, rule-based systems, and hybrid approaches for representing complex knowledge.
3. Ontologies and Ontological Engineering:
Advancements in the development, evaluation, and application of ontologies.
Research on ontology design patterns, ontology alignment, ontology evolution, and ontology-based data integration.
4. Linked Data:
Research on the principles and practices of publishing and interlinking structured data on the Web.
Advancements in data interoperability, data integration, and data management using Linked Data principles.
5. Knowledge Graphs:
Studies on the construction, enrichment, and utilization of knowledge graphs.
Research on graph-based representations of knowledge, entity linking, and graph analytics on large-scale knowledge graphs.
6. Semantic Web Applications:
Research on innovative applications leveraging Semantic Web technologies in domains such as healthcare, e-commerce, cultural heritage, and smart cities.
Case studies demonstrating the practical benefits and challenges of applying Semantic Web technologies.
7. Semantic Web Services and APIs:
Advancements in service-oriented architectures, RESTful APIs, and Web services enhanced with semantic descriptions.
Research on service composition, discovery, invocation, and monitoring using semantic technologies.
8. Semantic Web Standards and Best Practices:
Studies on the development, adoption, and implementation of Semantic Web standards and best practices.
Research on community-driven initiatives, guidelines, and recommendations for Semantic Web deployment.
9. Semantic Search and Information Retrieval:
Research on semantic search engines, query languages, and techniques for retrieving and ranking semantically enriched information.
Advancements in semantic indexing, information extraction, and natural language processing for the Semantic Web.
10. Knowledge-Based Systems:
Studies on the integration of Semantic Web technologies with AI techniques, knowledge-based systems, and intelligent agents.
Research on reasoning engines, inference mechanisms, and knowledge-driven decision support systems.