Semantics in the Deep: Semantic Analytics for Big Data

Dear Colleagues,

During the last decade, the field of Artificial Intelligence (AI) has experienced an explosion of applications and innovations, having many facets: On the one hand, there are strong symbolic representations to underpin the next generation of the Web, and the advent of the Semantic Web has a major role in giving everyday browsing tasks a blend of intelligence; and, on the other hand, deep learning techniques and achievements have been proven to tackle problems that would seem intractable some time ago. The wide availability of information on the Internet, storage space, and web-generated content put still more impetus on devising applications that would take advantage of such unprecedented resources, but would also stand up to the challenges posed by processing and value extraction out of big data. Now that big data have become everyday data, two fundamental questions naturally arise:

• How can semantic technologies contribute towards big data analysis?
• What is the relationship between Semantic Web logical formalisms and automated- and deep-learning techniques?

The aim of this Special Issue is to put emphasis on big data analysis and, more specifically, on how semantics-aware applications can contribute in this field. The interplay between the logical formalisms of the Semantic Web and automated learning and deep learning techniques is currently an open research topic for both technologies to achieve their next step and forms the state-of-the-art in this area. In this sense, there are numerous open problems, ranging from efficient ontological processing of big data ontologies to knowledge graphs maintenance to ontology evolvement with machine learning techniques.

Topics
Following the theme of SEDSEAL 2018, this special issue solicits contributions to the open problems above, such as innovative techniques, tools, case studies, comparisons, and theoretical advances. The papers should consider and present contributions towards how Semantic Web technologies can help to implement and enhance big data analytics. This can be achieved either by extracting value out of these data (e.g., through reasoning), creating sustainable ontology models, offering a solid foundation for deploying learning techniques or anything in between. In particular, topics of interest include, but are not limited to, the following:

• Ontologies for big data
• Semantic applications in big data domains including:
• open datasets, linked data, scholarly information, e-learning
• economics, insurance, sensors, bioinformatics
• Reasoning approaches for knowledge extraction
• Ontology learning and topic modeling
• NLP and word embedding
• Semantic deep learning
• Semantic lakes and blockchain
• OBDA approaches for big data access
• Data science and semantics
• Evaluation techniques
• Semantic deep learning
• Ontologies as training sets
• Ontology evolution and learning feedback
• Scalability issues

Dr. Dimitrios A. Koutsomitropoulos
Prof. Dr. Spiridon D. Likothanassis
Prof. Dr. Panos Kalnis
Guest Editors

Editorial Review Board (TBC)

Andreas Andreou, Cyprus University of Technology, Cyprus
Christos Alexakos, University of Patras, Greece
Dimitrios Tsolis, University of Patras, Greece
Dimitrios Tzovaras, CERTH/ITI, Greece
Efstratios Georgopoulos, Technological Institute of Kalamata, Greece
Filipe Portela, University of Minho, Portugal
Jouni Tuominen, University of Helsinki, Finland
Konstantinos Votis, CERTH/ITI, Greece
Miguel-Angel Sicilia, University of Alcala, Spain
Minjuan Wang, San Diego State University, USA
Vassilis Plagianakos, University of Thessaly, Greece

Manuscript Submission Information

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• Big Data
• Ontologies
• Reasoning
• Deep learning
• Analytics
• Semantic Web
• Data Science
• Artificial Intelligence