[DL] [CFP] First International Workshop on Joint Use of Probabilistic Graphical Models and Ontology @KGSWC 2021

Sarra Ben Abbès benabbessarra at gmail.com
Mon Sep 20 10:15:25 CEST 2021


Dear colleagues and researchers,

Please consider submitting a paper for the 1st International workshop on
"Joint Use of Probabilistic Graphical Models and Ontology" which will be
held online - November 19-24, 2021.

*PGMOnto: *
*Joint Use of Probabilistic Graphical Models and Ontology*

1st International Workshop, in conjunction with KGSWC 2021
<https://kgswc.org/>

November 19- 24, 2021 - Online

https://kgswc.org/pgmonto2021/


*Important dates:*
• Workshop paper submission due: *October 01, 2021*
• Workshop paper notifications: October 23, 2021
• Workshop paper camera-ready versions due: November 02, 2021
• Workshop: November 19-24, 2021 (half-day)

All deadlines are 23:59 anywhere on earth (UTC-12).

*Context of the workshop:*
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 the 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 three decades, graphical probabilistic models (PGMs) have
enjoyed a surge of interest as a practically feasible framework of expert
knowledge encoding and as a new comprehensive data analysis framework.

Probabilistic graphical models (PGMs) such as Bayesian network, influence
diagram or probabilistic relational model are considered as one of the most
successful tools that enable a compact representation of complex systems
and the increased ability to effectively learn and perform inference in
large networks. Besides the compact representation of probability, PGMs are
also intuitively easier for a human to understand than joint probabilities
because they highlight the direct dependencies between random variables and
their overall semantics is easily captured visually through their graphical
representation.

In practice, the combination of PGMs 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 PGM 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 associated to the
model. Once the PGM is learned, its results can be used together with an
ontology reasoning engine to perform probabilistic inference.

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

*Objective:*

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 probabilistic graphical model
   - Semantic causality and probability
   - Causality and ontology
   - PGM for ontology mapping
   - PGM learning
   - Ontology for PGM construction
   - Probabilistic inference engine
   - Tools, systems and applications
   - and so on.

*Submission:*

The workshop is open to submit unpublished work resulting from research
that presents original scientific results, methodological aspects, concepts
and approaches. All submissions are not anonymous and must be PDF documents
written in English and formatted using the following style files:
KGSWC2021_authors_kit
<http://www.springer.com/%20computer/lncs/lncs+authors?SGWID=0-40209-0-0-0?SGWID=0-40209-0-0-0>

Papers are to be submitted through the workshop's
<https://easychair.org/conferences/?conf=pgmonto2021>*EasyChair* submission
page.

We welcome the following types of contributions:

   - *Full papers* (8-10 pages): Finished or consolidated R&D works, to be
   included in one of the Workshop topics.
   - *Short papers* (6-8 pages): Ongoing works with relevant preliminary
   results, opened to discussion.
   Accepted papers are planned to publish with Springer Proceeding
   (Approval Pending)


At least one author of each accepted paper must register for the workshop,
in order to present the paper. For further instructions, please refer to
the *KGSWC 2021 <https://kgswc.org/>* page.


*Workshop chairs:*

Sarra Ben Abbès, Engie, France

Ahmed Mabrouk, Engie, France

Lynda Temal, Engie, France

Philippe Calvez, Engie, France
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