Context

An ontology is well known to be the best way to represent knowledge in a domain of interest. It is defined by Gruber and Guarino as “an explicit specification of a conceptualization”. It allows us to represent explicitly and formally existing entities, their relationships, and their constraints in an application domain. This representation is the most suitable and beneficial way to solve many challenging problems related to the information domain (e.g., knowledge representation, knowledge sharing, knowledge reusing, automated reasoning, knowledge capitalizing, and ensuring semantic interoperability among heterogeneous systems). Using ontology has many advantages, among them we can cite ontology reusing, reasoning, explanation, commitment, and agreement on a domain of discourse, ontology evolution, mapping, etc. As a field of artificial intelligence (AI), ontology aims at representing knowledge based on declarative and symbolic formalization. Combining this symbolic field with computational fields of IA such as Machine Learning (ML), Deep Learning (DL), Uncertainty and Probabilistic Graphical Models (PGMs), Computer Vision (CV), Multi-Agent Systems (SMA) and Natural Languages Processing (NLP) is a promising association. Indeed, ontological modeling plays a vital role to help AI reduce the complexity of the studied domain and organizing information inside it. It broadens AI’s scope allowing it to include any data type as it supports unstructured, semi-structured, or structured data format which enables smoother data integration. The ontology also assists AI for the interpretation process, learning, enrichment, prediction, semantic disambiguation, and discovery of complex inferences. Finally, the ultimate goal of ontologies is the ability to be integrated into the software to make sense of all information.

In the last decade, ontologies are increasingly being used to provide background knowledge for several AI domains in different sectors (e.g. energy, transport, health, banking, insurance, etc.). Some of these AI domains are:

• Machine learning and deep learning: semantic data selection, semantic data pre-processing, semantic data transformation, semantic data prediction, semantic clustering correction of the outputs, semantic enrichment with ontological concepts, use the semantic structure for promoting distance measure, etc.

• Uncertainty and Probabilistic Graphical Models: learning PGM (structure or parameters) using ontologies, probabilistic semantic reasoning, semantic causality, probability, etc.

• Computer Vision: semantic image processing, semantic image classification, semantic object recognition/classification, etc.

• Blockchain: semantic transactions, interoperable blockchain systems, etc.

• Natural Language Processing: semantic text mining, semantic text classification, semantic role labeling, semantic machine translation, semantic question answering, ontology-based text summarizing, semantic recommendation systems, etc.

• Multi-Agent Systems and Robotics: semantic task composition, task assignment, communication, cooperation, coordination, plans and plannification, etc.

• Voice-video-speech: semantic voice recognition, semantic speech annotation, etc.

• Game Theory: semantic definition of specific games, semantic rules and goals definition, etc.

• etc.

Objective

This workshop aims at highlighting recent and future advances on the role of ontologies and knowledge graphs in different domains of AI and how they can be used in order to reduce the semantic gap between the data, applications, machine learning process, etc., 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 ontologies and AI approaches.