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.
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
Semantic deep learning
Ontologies as training sets
Ontology evolution and learning feedback
Manuscripts should be submitted online at www.mdpi.com. 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.