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Re: DL4KGS@ESWC2018: Deadline Extended

From: Alberto Nogales <anogales81@gmail.com>
Date: Sat, 17 Mar 2018 12:09:54 +0100
Message-ID: <CAN6TzO5syKuTq8juU1A3aWJxf5GZYOSxzcxyhsPaExNmY96Gyw@mail.gmail.com>
To: "Koutraki, Maria (AIFB)" <maria.koutraki@kit.edu>
Cc: "semantic-web@w3.org" <semantic-web@w3.org>
Hello,
which time zone is valid for the deadline?
Thank you.
Kind regards.

On 15 March 2018 at 07:20, Koutraki, Maria (AIFB) <maria.koutraki@kit.edu>
wrote:

> ​*Apologies for cross-posting*​
>
> ------------------------------------------------------------
> ---------------------
>                                              Call For Papers
> ------------------------------------------------------------
> ---------------------​
> 1st International​ Workshop on Deep Learning for Knowledge Graphs
>                     and Semantic Technologies (DL4KGS)
>                      http://usc-isi-i2.github.io/DL4KGS/
>
> In conjunction with ESWC 20​18, 3rd-7th June 2018, Heraklion, Crete, Greece
>
>
> ------------------------------------------------------------
> ---------------------
>                                              Workshop Overview
> ------------------------------------------------------------
> ---------------------
> 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.
>
>
> ------------------------------------------------------------
> ---------------------
>                                                  Topics of Interest
> ------------------------------------------------------------
> ---------------------
> 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
> 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
>
> ------------------------------------------------------------
> ------------------------
>                                                 Important  Dates
> ------------------------------------------------------------
> ------------------------
> Submission deadline (extended):               Monday March 19, 2018
> Notification of Acceptance:        Tuesday April 17, 2018
> Camera-ready Submission:       Tuesday April 24, 2018
>
> ​
> ------------------------------------------------------------
> ---------------------
>                                                  WORKSHOP CO-CHAIRS
> ------------------------------------------------------------
> ---------------------
>
> 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/​
>
>
>
Received on Saturday, 17 March 2018 11:10:20 UTC

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