*Apologies for cross-posting*
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Call For Papers
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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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Workshop Overview
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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. Most famous examples of KGs are: DBpedia, Yago and Wikidata.
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
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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
* 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
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Important Dates
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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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WORKSHOP CO-CHAIRS
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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/
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