Dear colleagues, 

We would like to inform you that the deadline for the Special Issue on Semantic 
Deep Learning at the Semantic Web Journal has been extended to 31 March 2018. 

Please do not hesitate to contact us with any queries at 

Kind regards, 
Dagmar, Luis, Thierry 

--------------Extended deadline: 31 March 2018 ------------------------ 

Final Call for Papers and deadline extension : 
Special Issue of the Semantic Web Journal on 
Semantic Deep Learning 

Semantic Web technologies and deep learning share the goal of creating 
intelligent artifacts that emulate human capacities such as reasoning, 
validating, and predicting. Both fields have been impacting data and knowledge 
analysis considerably as well as their associated abstract representations. 
Deep learning is a term used to refer to deep neural network algorithms that 
learn data representations by means of transformations with multiple processing 
layers. These architectures have frequently been applied in NLP to feature 
learning from raw data, such as part-of-speech-tagging, morphological tagging, 
language modeling, and so forth. Semantic Web technologies and knowledge 
representation, on the other hand, boost the re-use and sharing of knowledge in 
a structured and machine readable fashion. Semantic resources such as WikiData, 
Yago, BabelNet or DBpedia, as well as knowledge base construction and 
completion methods have been successfully applied to improved systems 
addressing semantically intensive tasks (e.g. Question Answering). 
There are notable examples of contributions leveraging either deep neural 
architectures or distributed representations learned via deep neural networks 
in the broad area of Semantic Web technologies. These include, among others: 
(lightweight) ontology learning, ontology alignment, ontology annotation, 
joined relational and multi-modal knowledge representations, and ontology 
prediction. Ontologies, on the other hand, have been repeatedly utilized as 
background knowledge for machine learning tasks. As an example, there is a 
myriad of hybrid approaches for learning embeddings by jointly incorporating 
corpus-based evidence and semantic resources. This interplay between structured 
knowledge and corpus-based approaches has given way to knowledge-rich 
embeddings, which in turn have proven useful for tasks such as hypernym 
discovery, collocation discovery and classification, word sense disambiguation, 
joined relational and multi-modal knowledge representations and many others. 
In this special issue, we invite submissions that illustrate how Semantic Web 
resources and technologies can benefit from an interaction with deep learning. 
At the same time, we are interested in submissions that show how knowledge 
representation can assist in deep learning tasks deployed in the field of NLP 
and how knowledge representation systems can build on top of deep learning 

Structured knowledge in deep learning 

learning and applying knowledge graph embeddings 
applications of knowledge-rich embeddings 
neural networks and logic rules 
learning semantic similarity and encoding distances as knowledge graph 
ontology-based text classification 
multilingual resources for neural representations of linguistics 
semantic role labeling 

Deep reasoning and inferences 

commonsense reasoning and vector space models 
reasoning with deep learning methods 

Learning knowledge representations with deep learning 

word embeddings for ontology matching and alignment 
deep learning and semantic web technologies for specialized domains 
deep learning ontologies 
deep learning models for learning knowledge representations from text 
deep learning ontological annotations 

Joint tasks 

mining multilingual natural language for SPARQL queries 
information retrieval and extraction with knowledge graphs and deep learning 
knowledge-based deep word sense disambiguation and entity linking 
investigation of compatibilities and incompatibilities between deep learning 
and Semantic Web approaches 
neural networks for learning Linked Data 
Submission deadline: 31 March 2018 . Papers submitted before the deadline will 
be reviewed upon receipt. 

Submission Instructions 

Submissions shall be made through the Semantic Web journal website at . 

Prospective authors must take notice of the submission guidelines posted at . 

We welcome four main types of submissions: (i) full research papers, (ii) 
reports on tools and systems, (iii) application reports, 

and (iv) survey articles. The description of the submission types is posted at . 

While there is no upper limit, paper length must be justified by content. 

Note that you need to request an account on the website for submitting a paper. 
When submitting, 

please indicate in the cover letter that it is for the Special Issue on 
Semantic Deep Learning and the chosen submission type. 

All manuscripts will be reviewed based on the SWJ open and transparent review 
policy and will be made available 

online during the review process. 

Guest editors 

Luis Espinosa Anke, Cardiff University, UK 
Thierry Declerck, DFKI GmbH, Germany 
Dagmar Gromann, Technical University Dresden, Germany 

Guest editorial board 

Kemo Adrian, Artificial Intelligence Research Institute (IIIA-CSIC), 
Bellaterra, Spain 
Luu Ahn Tuan, Institute for Infocomm Research, Singapore 
Miguel Ballesteros, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA 
Peter Bloem, VU University Amsterdam, The Netherlands 
Jose Camacho-Collados, Sapienza University of Rome, Rome, Italy 
Stamatia Dasiopoulou, Pompeu Fabra University, Barcelona, Spain 
Derek Doran, Kno.e.sis Research Center, Wright State University, Ohio, USA 
Claudia d'Amato, Università degli Studi di Bari, Bari, Italy 
Maarten Grachten, Austrian Research Institute for AI, Vienna, Austria 
Dario Garcia-Casulla, Barcelona Supercomputing Center (BSC), Barcelona, Spain 
Jorge Gracia Del Río, Ontology Engineering Group, UPM, Madrid, Spain 
Jindrich Helcl, Charles University, Prague, Czech Republic 
Dirk Hovy, Computer Science Department of the University of Copenhagen, 
Copenhagen, Denmark 
Mayank Kejriwal, University of Southern California, California, USA 
Freddy Lecue, Accenture Technology Labs, Dublin, Ireland 
Alessandro Lenci, University of Pisa, Pisa, Italy 
Antonio Lieto, University of Turin, Turin, Italy 
Alessandra Mileo, INSIGHT Center for Data Analytics, Dublin City University, 
Sergio Oramas, Music Technology Group, Pompeu Fabra University, Barcelona, 
Petya Osenova, Bulgarian Academy of Sciences, Sofia, Bulgaria 
Simone Paolo Ponzetto, University of Mannheim, Mannheim, Germany 
Heiko Paulheim, University of Mannheim, Mannheim, Germany 
Martin Riedel, University of Stuttgart, Stuttgart, Germany 
Francesco Ronzano, Pompeu Fabra University, Barcelona, Spain 
Enrico Santus, Singapore University of Technology and Design, Singapore 
Francois Scharffe, Axon Research, New York, USA 
Vered Shwartz, Bar-Ilan University, Ramat Gan, Israel 
Kiril Simov, Bulgarian Academy of Sciences, Sofia, Bulgaria 
Michael Spranger, Sony Computer Science Laboratories Inc., Tokyo, Japan 
Armand Vilalta, Barcelona Supercomputing Center (BSC), Barcelona, Spain 
Piek Vossen, VU University Amsterdam, The Netherlands 
Arkaitz Zubiaga, University of Warwick, Coventry, UK
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