Semantic Web technologies and deep learning share the goal of creating intelligent artifacts that emulate human capacities such as reasoning, validating, and predicting. Both fields have been impacting data and knowledge analysis considerably as well as their associated abstract representations. Deep learning is a term used to refer to deep neural network algorithms that learn data representations by means of transformations with multiple processing layers. These architectures have frequently been applied in NLP to feature learning from raw data, such as part-of-speech-tagging, morphological tagging, language modeling, and so forth. Semantic Web technologies and knowledge representation, on the other hand, boost the re-use and sharing of knowledge in a structured and machine readable fashion. Semantic resources such as WikiData, Yago, BabelNet or DBpedia, as well as knowledge base construction and completion methods have been successfully applied to improved systems addressing semantically intensive tasks (e.g. Question Answering).

There are notable examples of contributions leveraging either deep neural architectures or distributed representations learned via deep neural networks in the broad area of Semantic Web technologies. These include, among others: (lightweight) ontology learning, ontology alignment, ontology annotation, joined relational and multi-modal knowledge representations, and ontology prediction. Ontologies, on the other hand, have been repeatedly utilized as background knowledge for machine learning tasks. As an example, there is a myriad of hybrid approaches for learning embeddings by jointly incorporating corpus-based evidence and semantic resources. This interplay between structured knowledge and corpus-based approaches has given way to knowledge-rich embeddings, which in turn have proven useful for tasks such as hypernym discovery, collocation discovery and classification, word sense disambiguation, joined relational and multi-modal knowledge representations and many others.

In this special issue, we invite submissions that illustrate how Semantic Web resources and technologies can benefit from an interaction with deep learning. At the same time, we are interested in submissions that show how knowledge representation can assist in deep learning tasks deployed in the field of NLP and how knowledge representation systems can build on top of deep learning results.

Structured knowledge in deep learning

learning and applying knowledge graph embeddings

applications of knowledge-rich embeddings

neural networks and logic rules

learning semantic similarity and encoding distances as knowledge graph

ontology-based text classification

multilingual resources for neural representations of linguistics

semantic role labeling

Deep reasoning and inferences

commonsense reasoning and vector space models

reasoning with deep learning methods

Learning knowledge representations with deep learning

word embeddings for ontology matching and alignment

deep learning and semantic web technologies for specialized domains

deep learning ontologies

deep learning models for learning knowledge representations from text

deep learning ontological annotations

Joint tasks

mining multilingual natural language for SPARQL queries

information retrieval and extraction with knowledge graphs and deep 
learning models

knowledge-based deep word sense disambiguation and entity linking

investigation of compatibilities and incompatibilities between deep learning and Semantic Web approaches

neural networks for learning Linked Data

Submission deadline: 28 February 2018. Papers submitted before the deadline will be reviewed upon receipt
Submission Instructions
Submissions shall be made through the Semantic Web journal website at

Prospective authors must take notice of the submission guidelines posted at

We welcome four main types of submissions: (i) full research papers, (ii) reports on tools and systems, (iii) application reports,

and (iv) survey articles. The description of the submission types is posted at

While there is no upper limit, paper length must be justified by content.

Note that you need to request an account on the website for submitting a paper. When submitting,

please indicate in the cover letter that it is for the Special Issue on Semantic Deep Learning and the chosen submission type.

All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available

online during the review process.