Context and Objective:

An ontology is well known to be the best way to represent knowledge in a domain of discourse. It is defined by Gruber as “an explicit specification of a conceptualization”. It allows 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 resolve many challenging problems related to information domain (e.g. semantic interoperability among systems, knowledge sharing, and knowledge capitalization). Ontology formalization can be based on First order logic (FOL) to describe concepts, relationships and constraints, enabling it to make inferences and giving it a graphical representation. Using ontology has many advantages, among them we can cite: ontology reusing, reasoning and explanation, commitment and agreement on a domain of discourse, ontology evolution and mapping, etc.

Over the last 30 years, another representation model called Bayesian Network has emerged as a practically feasible framework of expert knowledge encoding and as a new comprehensive data analysis framework.

Bayesian networks, also referred to as Belief networks have emerged as one of the most successful tools for diagnosis tasks and have been applied in many real domain applications (diagnosis, machine diagnosis, etc.). BNs offer mechanisms to accurately represent the dependences between random variables and to perform automated reasoning under uncertainty. They are supplied with fast inference engines that enable to answer efficiently various types of probabilistic queries (computation of marginal, a priori, a posteriori, probabilities, of most probable explanations, of maximum a posteriori, etc.).

In practice, the combination of Bayesian networks and ontologies might be beneficial to have high expressiveness and reasoning possibilities under uncertainty. Despite the difference between these two domain representation models, they have the potential to complement each other: part of the value of ontology baseline knowledge may be used to enhance BN by resolving challenging tasks: (i) the identification of relevant variables (variable selection), (ii) the determination of structural relationships between the considered variables (arcs), and (iii) the estimation of parameters which are represented by conditional probability tables (CPTs) associated to for each node in the BN model. Once the Bayesian network is learned, its results can be used together with ontology reasoning engine to perform probabilistic inference.

This first regular workshop aims at demonstrating recent and future advances in Semantic Bayesian Networks and Probabilistic Ontologies. Moreover, this workshop offers an invaluable opportunity to boost collaboration and conversation between Industrial Experts and academic researchers, allowing therefore ideas exchanging and presenting results of on-going research in structured knowledge and causality approaches.

We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged.


Topics of interests:

  • Construction of probabilistic ontologies
  • Construction of semantic Bayesian networks
  • Semantic causality and probability
  • Causality and ontology
  • Bayesian Neural Networks for ontology modelling and ontology reasoning
  • Bayesian Network for ontology mapping
  • Bayesian network learning
  • Ontology for Bayesian Network construction
  • Probabilistic inference engine
  • Tools, systems and applications


Important dates:

  • Workshop paper submission due: February 28 March 14, 2021
  • Workshop paper notifications: March 28 April 4, 2021
  • Workshop paper camera-ready versions due: April 11, 2021
  • Workshop: June 23-26, 2021 (half-day)


Organizing committee: