- From: Alberto Nogales <anogales81@gmail.com>
- Date: Sat, 17 Mar 2018 12:09:54 +0100
- To: "Koutraki, Maria (AIFB)" <maria.koutraki@kit.edu>
- Cc: "semantic-web@w3.org" <semantic-web@w3.org>
- Message-ID: <CAN6TzO5syKuTq8juU1A3aWJxf5GZYOSxzcxyhsPaExNmY96Gyw@mail.gmail.com>
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 2018, 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