28.4797, Calls: Computational Linguistics, Semantics / Semantic Web (Jrnl)

[The LINGUIST List]

LINGUIST List: Vol-28-4797. Tue Nov 14 2017. ISSN: 1069 - 4875.

Subject: 28.4797, Calls: Computational Linguistics, Semantics / Semantic Web (Jrnl)

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Date: Tue, 14 Nov 2017 13:18:52
From: Dagmar Gromann [dagmar.gromann at gmail.com]
Subject: Computational Linguistics, Semantics / Semantic Web (Jrnl)

 
Full Title: Semantic Web 


Linguistic Field(s): Computational Linguistics; Semantics 

Call Deadline: 28-Feb-2018 

Call for Papers:

Special Issue on Semantic Deep Learning at the Semantic Web Journal
For more details please visit:
http://www.semantic-web-journal.net/blog/call-papers-special-issue-semantic-de
ep-learning

Semantic Web technologies and deep learning share the goal of creating
intelligent artifacts that emulate human capacities such as reasoning,
validating, and predicting. Both fields have been impacting data and knowledge
analysis considerably as well as their associated abstract representations.
Deep learning is a term used to refer to deep neural network algorithms that
learn data representations by means of transformations with multiple
processing layers. These architectures have frequently been applied in NLP to
feature learning from raw data, such as part-of-speech-tagging, morphological
tagging, language modeling, and so forth. Semantic Web technologies and
knowledge representation, on the other hand, boost the re-use and sharing of
knowledge in a structured and machine readable fashion. Semantic resources
such as WikiData, Yago, BabelNet or DBpedia, as well as knowledge base
construction and completion methods have been successfully applied to improved
systems addressing semantically intensive tasks (e.g. Question Answering).

Topics include, but are not limited to:

Structured knowledge in deep learning:
- learning and applying knowledge graph embeddings 
- applications of knowledge-rich embeddings 
- neural networks and logic rules 
- learning semantic similarity and encoding distances as knowledge graph
- ontology-based text classification
- multilingual resources for neural representations of linguistics
- semantic role labeling 

Deep reasoning and inferences:
- commonsense reasoning and vector space models
- reasoning with deep learning methods

Learning knowledge representations with deep learning:
- word embeddings for ontology matching and alignment
- deep learning and semantic web technologies for specialized domains
- deep learning ontologies 
- deep learning models for learning knowledge representations from text
- deep learning ontological annotations

Joint tasks:
-mining multilingual natural language for SPARQL queries 
-information retrieval and extraction with knowledge graphs and deep learning
models
-knowledge-based deep word sense disambiguation and entity linking
-investigation of compatibilities and incompatibilities between deep learning
and Semantic Web approaches
-neural networks for learning Linked Data

Deadline:
Submission deadline: 28 February 2017. Papers submitted before the deadline
will be reviewed upon receipt.
Submission Instructions
http://www.semantic-web-journal.net/blog/call-papers-special-issue-semantic-de
ep-learning

Guest Editors:
The guest editors can be reached at semdeep at googlegroups.com.
Luis Espinosa Anke, Cardiff University, UK
Thierry Declerck, DFKI GmbH, Germany
Dagmar Gromann, Technical University Dresden, Germany




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