DL4KGS

Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS)


Held in conjunction with ESWC’18 in June 2018 in Crete, Greece

DL4KGS

Introduction

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.

Format

This full-day workshop aims to gather researchers and practitioners presenting innovative research contents as well as applications involving deep learning, knowledge graphs and semantic technologies. The workshop will include oral presentations of short papers and full papers as well as a keynote speech.

Call for Papers

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:

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

Important Dates

Paper submission: Monday 19th March
Notification to authors: Tuesday 17th April
Camera ready papers due: Tuesday 24th April

Accepted Papers

TBA

Program

TBA

Keynote Speaker

The workshop will host a keynote speech given by Pascal Hitzler entitled "Neural-Symbolic Integration and Its Relevance to Deep Learning and the Semantic Web".

Organization

Organizing Committee

Program Committee