Dear Colleague,

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 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. 

Dr. George Fazekas
Prof. Dr. Robert Stevens
Guest Editors

Manuscript Submission Information

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