CFP: Special Issue on Representation Learning for the Semantic Web

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

 

Guest Editors

Heiko Paulheim (Data and Web Science Group, University of Mannheim. Web: http://www.heikopaulheim.com, Mail: heiko@informatik.uni-mannheim.de) is an assistant professor and interim chair of Data Science at university of Mannheim. His research focus is at the crossroads of Semantic Web and Linked Data on the one hand, and data mining and machine learning on the other hand. Heiko is a co-author of more than 120 peer-reviewed papers published in Semantic Web, artificial intelligence and machine learning conferences and journals. He is an editorial board member of Web Intelligence and Web Semantics, and has served as a PC member on conferences such ISWC, ESWC, IJCAI, KI, AAAI, or Hypertext.

 

Volker Tresp (Siemens Corporate Technology and Ludwig Maximilian University of Munich. Web: http://www.tresp.org/, Mail: Volker.Tresp@Siemens.com) is the head of a research team in machine learning at Siemens, CorporateTechnology. He filed more than 70 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 20 Ph.D. theses. His research focus in recent years has been „Machine Learning in Information Networks“ for modelling Knowledge Graphs, medical decision processes and sensor networks. In addition, he is exploring mathematical models of the memory systems of the human brain. Since 2011 he is a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.

 

Zhiyuan Liu (Natural Language Processing Group, Tsinghua University. Web: http://nlp.csai.tsinghua.edu.cn/~lzy, Mail: liuzy@tsinghua.edu.cn) is an assistant professor at Tsinghua University. His research focus is representation learning, knowledge graphs and social computation. Zhiyuan has published more than 30 papers in leading conferences and journals of AI and NLP including ACL, IJCAI and AAAI. He serves as Youth Associate Editor of Frontiers of Computer Science, Area Chair of ACL, and PC members of ACL, IJCAI, AAAI, etc.

 

Important Dates

  • Submission deadline: November 30th, 2017
  • Author notification: January 15th, 2018
  • Final version: April 15th, 2018
  • Final notification: May 15th, 2018
  • Publication: May 31st, 2018


Submission Guidelines

The Journal of Web Semantics solicits original scientific contributions of high quality. Following the overall mission of the journal, we emphasize the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications. 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.

 

Submission of your manuscript is welcome provided that it, or any translation of it, has not been copyrighted or published and is not being submitted for publication elsewhere. Manuscripts should be prepared for publication in accordance with instructions given in the JWS guide for authors. The submission and review process will be carried out using Elsevier's Web-based EES system. Please select “SI:Embeddings” when reaching the Article Type selection.

 

Upon acceptance of an article, the author(s) will be asked to transfer copyright of the article to the publisher. This transfer will ensure the widest possible dissemination of information. Elsevier's liberal preprint policy permits authors and their institutions to host preprints on their web sites. Preprints of the articles will be made freely accessible on the JWS preprint server. Final copies of accepted publications will appear in print and at Elsevier's archival online server.