[CfP] Deep Learning and Semantic Technologies (MDPI Algorithms Special Issue)
[Algorithms Special Issue on Deep Learning and Semantic Technologies, Deadline: Nov. 15. 2018 Submission:bit.ly/DeepSemTech, Apologies for crossposting, please distribute
If your recent research involves the use of Deep Learning to complement, enhance or improve Semantic Technologies and Semantic Web research, please consider contributing to the following special issue.
========================================== Special Issue of Algorithms on
“Deep Learning and Semantic Technologies" ==========================================
Sustained increase in computational capacity, advances in training and optimisation techniques and the availability of big data caused a resurgence of interest in neural networks. Deep learning opened new avenues in information extraction and processing in
a wide range of application domains, including natural language processing, audio and visual object recognition and synthesis, bioinformatics, genomics, health informatics, recommendation systems and many other areas where learning effective representations
from raw data or recognising small patterns amid large variations in data is beneficial. At the same time, semantic technologies including ontologies provide a well-established mechanism for structured knowledge representation and inference. They allow domain
experts to construct and maintain knowledge bases, often without training data, which may be used in high-level decision-making procedures. These approaches can be distinctly complementary. They may facilitate solving problems where very complex decisions
are needed, where large datasets are not yet available, or when expert knowledge can augment big data analytics. Deep learning provides the state-of-the-art in converting raw data into symbols that may be manipulated using logic. In this Special Issue, we
invite original research papers and reviews related to the combination of these techniques, including new paradigms for complex reasoning over semantic structures and applications where deep learning and semantic technologies are used in tandem.
-- Topics of interest --
Topics of interest include but are not limited to the following:
* Ontology structure and content learning from text and media
* Ontology matching and evaluation using deep neural networks
* Named Entity Recognition and term disambiguation using e.g. word embeddings or enhanced by using knowledge representations
* Using ontologies as priors for deep neural network training
* Learning neural networks from knowledge graphs
* Ontology learning from non-textual data (e.g. music signals, social networks, graph signals etc.)
* Deep Learning for ontology reasoning
* Recurrent and memory networks for complex inference
* Statistical Relational Learning and Reasoning
* Semantic deep mining and knowledge completion using big data analytics
* Applications in Semantic Web, biomedical research, media, audio, video, music, recommendation systems, intelligent user interfaces, broadcasting, manufacturing, etc.
-- How to submit --
Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Manuscripts should be submitted online by registering and logging in to the MDPI website. All manuscripts are thoroughly refereed through a single-blind peer-review process. Manuscripts can be submitted until the deadline. Accepted papers will be published
continuously in the journal (as soon as accepted) and will be listed on the designated special issue page. Research articles, review articles as well as short communications are invited.
* Submission deadline: 15 November 2018
* Notification: continuously after paper submission
-- Guest editors -- * Dr. George Fazekas, School of Electronic Engineering and Computer Science, Queen Mary University of London
* Prof. Dr. Robert Stevens, School of Computer Science, University of Manchester
Dr. George Fazekas,
Lecturer in Digital Media
Programme Coordinator, Sound and Music Computing (SMC)
Centre for Digital Music (C4DM)
School of Electronic Engineering and Computer Science
Queen Mary University of London, UK