[open-linguistics] Call for Papers: Special Issue of the Semantic Web Journal on Semantic Deep Learning

Thu Nov 16 02:11:13 UTC 2017

 please check the date :Submission deadline: 28 February 2017. Papers submitted before the deadline will be reviewed upon receipt.

    On ‎Monday‎, ‎November‎ ‎13‎, ‎2017‎ ‎06‎:‎43‎:‎04‎ ‎PM‎ ‎EET, Dagmar Gromann <dgromann at iiia.csic.es> wrote:  

 #yiv6411229537 p {margin:0;}Dear colleagues, 

Please find attached the Call for Papers of the Special Issue on Semantic Deep Learning at the Semantic Web Journal.

Please do not hesitate to contact us with any questions or requests for further information. 

Kind regards,
Dagmar, Thierry, Luis 

--------------------------------
http://www.semantic-web-journal.net/blog/call-papers-special-issue-semantic-deep-learning

Call for Papers: 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 results.

Topics include, but are not limited to: 

   -    
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 models

   -    
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

Deadline

   -    
Submission deadline: 28 February 2017. Papers submitted before the deadline will be reviewed upon receipt.

Submission Instructions

Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors.

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 http://www.semantic-web-journal.net/authors#types. 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

The guest editors can be reached at semdeep at googlegroups.com.

Luis Espinosa Anke, Cardiff University, UK

Thierry Declerck, DFKI GmbH, Germany

Dagmar Gromann, Technical University Dresden, Germany

Guest editorial board

(to be completed)

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

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, Ireland

Sergio Oramas, Music Technology Group, Pompeu Fabra University, Barcelona, Spain

Petya Osenova, Bulgarian Academy of Sciences, Soﬁa, Bulgaria

Simone Paolo Ponzetto, 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, Isreal

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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