DL4KGS 2018 - 1st Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies

We invite paper submissions, short (4-6 pages) and long (8-12 pages), that show how Semantic Web resources and technologies can benefit from deep learning or how deep learning tasks can build on top of Semantic Web resources and technologies. Below is a non-exhaustive list of topics:
Knowledge graph embeddings for entity linking, recommendation, relatedness
Knowledge graph embeddings for link prediction and validation
Time-aware and scalable knowledge graph embeddings
Text-based entity embeddings vs knowledge graph entity embeddings
Deep learning models for learning knowledge representations from text
Knowledge graph agnostic embeddings
Knowledge Base Completion
Type Inference
Question Answering
Domain Specific Knowledge Base Construction
Reasoning over KGs and with deep learning methods
Neural networks and logic rules for semantic compositionality
Quality checking and Data cleaning
Multilingual resources for neural representations of linguistics
Commonsense reasoning and vector space models
Deep ontology learning
Deep learning ontological annotations
Applications of knowledge graph embeddings in real business scenarios
All papers should be formatted according to the latest Springer format also used for the main research track at ESWC. All papers will be peer reviewed, single-blinded. Authors whose papers are accepted to the workshop will have the opportunity to participate in a poster session, and some set may also be chosen for oral presentation. Long papers range from 8-12 pages, and short papers range from 4-6 pages, including all references. The accepted papers will be published as CEUR-WS Proceedings and will not be considered archival.
We are using the EasyChair system for submissions. Please follow this link to submit your work.
Please email any enquiries to Enrico Palumbo, Dagmar Gromann, Besnik Fetahu, and Maria Koutraki