Journal Information
Journal of Web Semantics
Impact Factor:

Call For Papers
The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include: knowledge technologies, ontology, agents, databases and the semantic grid, obviously disciplines like information retrieval, language technology, human-computer interaction and knowledge discovery are of major relevance as well. All aspects of the Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents and services. The journal emphasizes the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications.

The Journal of Web Semantics addresses various prominent application areas including: e-business, e-community, knowledge management, e-learning, digital libraries and e-sciences.

The Journal of Web Semantics features a multi-purpose web site, which can be found at: Readers are also encouraged to visit the Journal of Web Semantics blog, at for more information and related links.

The Journal of Web Semantics includes, but is not limited to, the following major technology areas:
• The Semantic Web
• Knowledge Technologies
• Ontology
• Agents
• Databases
• Semantic Grid and Peer-to-Peer Technology
• Information Retrieval
• Language Technology
• Human-Computer Interaction
• Knowledge Discovery
• Web Standards

Major application areas that are covered by the Journal of Web Semantics are:
• eBusiness
• eCommunity
• Knowledge Management
• eLearning
• Digital Libraries
• eScience

Each of these areas is covered by an area editor who supports the editors-in-chief. Furthermore, area editors manage the review process for submitted papers in the respective areas.

The Journal of Web Semantics publishes four types of papers:
• Research papers: Research papers are judged by originality, technical depth and correctness, as well as interest to our target readership. Research papers are recommended to have 15 - 25 pages in double column format.
• Survey papers: We rarely accept survey papers, and beyond a sheer enumeration of relevant methods and systems, we expect a substantial technical insight to be gained by a survey paper. Survey papers are recommended to have 15 - 25 pages in double column format.
• Ontology papers: We publish community-oriented description of ontology papers, if they generate interests from real-world users and semantic Web experts. Ontology papers are recommended to have 6 - 8 pages in double column format. Interested authors may here find a detailed Call-for-Ontology papers
• System papers: Widely adopted semantic systems and systems that generate a far above average amount of interest in the Semantic Web community, may be explained in systems papers. Systems papers are recommended to have 6 - 8 pages in double column format.

Shorter or longer papers are allowable, if the objectives of a paper warrant deviating length. Descriptions that are either unnecessarily short or long will negatively impact chances of acceptance.
Last updated by Xin Yao in 2017-11-12
Special Issues
Special Issue on Representation Learning
Submission Date: 2017-11-30

The Journal of Web Semanticsinvites submissions for a special issue on representation learning for the Semantic Web, to be edited by Heiko Paulheim, Volker Tresp, and Zhiyuan Liu. Submissions are due by November 30th, 2017. In the past years, learning vector space embeddings has rapidly gained attention, first in the natural language processing community with the advent of word2vec, and more recently also in the Semantic Web community, e.g., with the adaptations RDF2vec or node2vec, as well as the RESCAL/HolE and Trans* family. Their properties - the representation of entities in a dense vector space, the proximity of semantically related entities, and the preservation of the direction of semantic relations - make them interesting for many applications. There are various ways of creating such embeddings. They range from applying the word2vec paradigm to sequences derived from graphs to translation learning and tensor factorization. Those methods differ both in their scalability on different types of input datasets, as well as in the characteristics of the resulting embeddings. At the same time, novel use cases for embeddings of Semantic Web data are discussed. Those applications range from Semantic Web specific use cases, such as link prediction in knowledge graphs, to general applications, such as recommender systems and decision support systems. In many of those fields, approaches leveraging embeddings have recently been reported to outperform traditional techniques. The aim of this special issue is to present latest advances in neural embeddings for the Semantic Web, as well as novel applications. Topics of submissions include, but are not limited to: - Novel methods for learning embeddings - Theory of representation learning for the Semantic Web - Embeddings of ontologies and knowledge graphs - Joint embeddings of Semantic Web and non-Semantic Web data (e.g., text, media, ...) - Paradigms for sharing and reusing embeddings - Embedding systems for modeling human memories - Scalability of embedding learning - Implementations - Reusable embeddings for popular Semantic Web resources, e.g., DBpedia or Wikidata - Software frameworks for learning and using embeddings - Experimental studies and benchmarks - Application areas of embeddings, e.g., recommender systems, entity search, or named entity disambiguation
Last updated by Dou Sun in 2017-10-16
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