*Apologies for cross-posting*

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1st International Workshop on Deep Learning for Knowledge Graphs

and Semantic Technologies (DL4KGS)

http://usc-isi-i2.github.io/DL4KGS/

In conjunction with ESWC 2018, 3rd-7th June 2018, Heraklion, Crete, Greece

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Semantic Web technologies and deep learning share the goal of creating intelligent artifacts that emulate human capacities such as reasoning, validating, and predicting. 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. Knowledge Graphs (KG) are one of the most well-known outcomes from the Semantic Web community, with wide use in web search, text classification, entity linking etc. KGs are large networks of real-world entities described in terms of their semantic types and their relationships to each other.

A challenging but paramount task for problems ranging from entity classification to entity recommendation or entity linking is that of learning features representing entities in the knowledge graph (building “knowledge graph embeddings”) that can be fed into machine learning algorithms. The feature learning process ought to be able to effectively capture the relational structure of the graph (i.e. connectivity patterns) as well as the semantics of its properties and classes, either in an unsupervised way and/or in a supervised way to optimize a downstream prediction task. In the past years, Deep Learning (DL) algorithms have been used to learn features from knowledge graphs, resulting in enhancements of the state-of-the-art in entity relatedness measures, entity recommendation systems and entity classification. DL algorithms have equally been applied to classic problems in semantic applications, such as (semi-automated) ontology learning, ontology alignment, duplicate recognition, ontology prediction, relation extraction, and semantically grounded inference.

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Topics of interest for this first workshop on Deep Learning for Knowledge Graphs and Semantic Technologies, include but are not limited to the following fields and problems:

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

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

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Submission deadline: Friday March 16, 2018

Notification of Acceptance: Tuesday April 17, 2018

Camera-ready Submission: Tuesday April 24, 2018

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Michael Cochez, Fraunhofer Institute for Applied Information Technology, Germany

Thierry Declerck, DFKI GmbH, Germany

Gerard de Melo, Rutgers University, USA

Luis Espinosa Anke, Cardiff University, UK

Besnik Fetahu, L3S Research Center, Leibniz University of Hannover, Germany

Dagmar Gromann, Technical University Dresden, Germany

Mayank Kejriwal, Information Sciences Institute, USA

Maria Koutraki, FIZ-Karlsruhe, Karlsruhe Institute of Technology (KIT), Germany

Freddy Lecue, Accenture Technology Labs, Ireland; INRIA, France

Enrico Palumbo, ISMB, Italy; EURECOM, France; Politecnico di Torino, Italy

Harald Sack, FIZ Karlsruhe, Karlsruhe Institute of Technology (KIT), Germany

More information about DL4KGs 2018 is available at: http://usc-isi-i2.github.io/DL4KGS/