Context

In recent years, deep learning has been applied successfully and achieved state-of-the-art performance in a variety of domains, such as image analysis. Despite this success, deep learning models remain hard to analyze data and understand what knowledge is represented in them, and how they generate decisions.

Deep Learning (DL) meets Natural Language Processing (NLP) to solve human language problems for further applications, such as information extraction, machine translation, search, and summarization. Previous works have attested the positive impact of domain knowledge on data analysis and vice versa, for example pre-processing data, searching data, redundancy and inconsistency data, knowledge engineering, domain concepts, and relationships extraction, etc. Ontology is a structured knowledge representation that facilitates data access (data sharing and reuse) and assists the DL process as well. DL meets recent ontologies and tries to model data representations with many layers of non-linear transformations.

The combination of DL, ontologies, and NLP might be beneficial for different tasks:

• Deep Learning for Ontologies: ontology population, ontology extension, ontology learning, ontology alignment, and integration,

• Ontologies for Deep Learning: semantic graph embeddings, latent semantic representation, hybrid embeddings (symbolic and semantic representations),

• Deep Learning for NLP: summarization, translation, named entity recognition, question answering, document classification, etc.

• NLP for Deep Learning: parsing (part-of-speech tagging), tokenization, sentence detection, dependency parsing, semantic role labeling, semantic dependency parsing, etc.

Objective

This workshop aims at demonstrating recent and future advances in semantic rich deep learning by using Semantic Web and NLP techniques which can reduce the semantic gap between the data, applications, machine learning process, in order to obtain semantic-aware approaches. In addition, the goal of this workshop is to bring together an area for experts from industry, science, and academia to exchange ideas and discuss the results of ongoing research in natural language processing, structured knowledge, and deep learning approaches.