Special Issue "Deep Learning and Semantic Technologies"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 31 March 2019
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.