Monterey, California

Semantic Web (SW) Technologies and Deep Learning (DL) share the goal of 
creating intelligent artifacts. Both disciplines have had a remarkable impact 
in data and knowledge analysis, as well as knowledge representation, and in 
fact constitute two complementary directions for modeling expressible 
linguistic phenomena and solving semantically complex problems. In this 
context, and following the main foundations set in past editions, SemDeep-4 
aims to bring together SW and DL research as well as industrial communities. 
SemDeep is interested in contributions of DL to classic problems in semantic 
applications, such as: (semi-automated) ontology learning, ontology alignment, 
ontology annotation, duplicate recognition, ontology prediction, knowledge base 
completion, relation extraction, and semantically grounded inference, among 
many others. At the same time, we invite contributions that analyse the 
interaction of SW technologies and resources with DL architectures, such as 
knowledge-based embeddings, collocation discovery and classification, or 
lexical entailment, to name only a few. This workshop seeks to provide an 
invigorating environment where semantically challenging problems which appeal 
to both Semantic Web and Computational Linguistic communities are addressed and 

We invite submissions on any approach combining Semantic Web technologies and 
Deep Learning and suggest the following topics.

Structured knowledge in deep learning.

  *   neural networks and logic rules for semantic compositionality

  *   learning and applying knowledge graph embeddings to NLP tasks

  *   learning semantic similarity and encoding distances as knowledge graph

  *   ontology-based text classification

  *   multilingual resources for neural representations of linguistics

  *   semantic role labeling

Reasoning and inferences and deep learning

  *   commonsense reasoning and vector space models

  *   reasoning with deep learning methods

  *   learning knowledge representations with deep learning

  *   deep learning methods for knowledge-base completion

  *   deep ontology learning

  *   deep learning models for learning knowledge representations from text

  *   deep learning ontological annotations



*Firm* Submission deadline: June 1, 2018

Notification of acceptance: June 27, 2018

Camera-ready version: July 20, 2018

Workshop dates: October 8-9, 2018


We invite three types of submissions:

  1.  Long papers with new results (max. 12 pages)

  2.  Short papers presenting innovative not fully empirically validated ideas 
or position papers (max. 4 pages)

  3.  Short descriptions of systems that participate in the demo jam (max. 4 

All papers need to follow the LCNS formatting guidelines. The demo jam takes 
the format of system demonstrations where the theoretical background may be 
explained in the presentation slot and the functioning of the system is 
showcased in a 5 minute demo.


Luis Espinosa Anke, Cardiff University, UK

Thierry Declerck, DFKI GmbH, Germany

Dagmar Gromann, Technical University Dresden, Germany


Stephan Baier, Ludwig Maximilian University, Munich, Germany

Michael Cochez, RWTH University Aachen, Germany

Brigitte Grau, LIMSI, CNRS, Orsay, France

Wei Hu, Nanjing University, China

Rezaul Karim, RWTH University Aachen, Germany

Stratos Kontopoulos, Multimedia Knowledge \& Social Media Analytics Laboratory, 
Thessanloniki, Greece

Brigitte Krenn, Austrian Research Institute for AI, Vienna, Austria

Jose Moreno, Universite Paul Sabatier, IRIT, Toulouse, France

Luis Nieto PiƱa, University of Goteborg, Goteburg, Sweden

Sergio Oramas, Universitat Pompeu Fabra, Barcelona, Spain

Alessandro Raganato, Sapienza University of Rome, Rome, Italy

Simon Razniewksi, Max-Planck-Institute, Germany

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