[DL] [CFP] 1st International Workshop on Ontology Uses and Contribution to Artificial Intelligence @ KR-2021

Sarra Ben Abbès benabbessarra at gmail.com
Wed Aug 25 18:04:43 CEST 2021


Dear colleagues and researchers,

Please consider submitting a paper for the 1st International workshop on
"Ontology Uses and Contribution to Artificial Intelligence"  which will be
held online or in Hanoi, Vietnam - November 6-12, 2021.


*                         ****  OnUCAI - CALL FOR PAPERS *****

*                     Ontology Uses and Contribution to Artificial
Intelligence*

*                              1st International Workshop, in conjunction
with *KR 2021 <https://kr2021.kbsg.rwth-aachen.de/>

*                            November 6-12, 2021 - Online or in Hanoi,
Vietnam*

                           https://sites.google.com/view/onucai-kr2021


** Important dates **

   -
   - *Workshop paper submission due:**September 06, 2021*
   - *Workshop paper notifications: *September 30, 2021
   - *Workshop paper camera-ready versions due: *October 12, 2021
   - *Workshop registration deadline: *TBA
   - *Workshop: *November 06-12, 2021

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


** Workshop description **

An ontology is well known to be the best way to represent knowledge in a
domain of interest. It is defined by Gruber 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 and
explanation, commitment and agreement on a domain of discourse, ontology
evolution and 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), Probabilistic
Graphical Models (PGMs), Computer Vision (CV) and Natural Languages
Processing (NLP) is a promising association. Indeed, ontological modeling
plays a vital role to help AI reducing 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 interpretation process, learning, enrichment,
prediction, semantic disambiguation and discovering of complex inferences.
Finally, the ultimate goal of ontologies is the ability to be integrated in
a 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 and 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.
   - Probabilistic Graphical Models: learning PGM (structure or parameters)
   using ontologies, probabilistic semantic reasoning, semantic causality and
   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 labelling, semantic machine translation,
   semantic question answering, ontology based text summarizing, semantic
   recommendation systems, etc.
   - Robotics: semantic task composition, task assignment, communication,
   cooperation and coordination, 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 it
can be used in order to reduce the semantic gap between the data,
applications, machine learning process, etc., in order to obtain a
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 results of on-going research in ontologies and
AI approaches.

We invite the submission of original works that is related -- but are not
limited to -- the topics below.


** Topics of interests **

   - Ontology for Machine Learning/Deep Learning
   - Ontology for Probabilistic Graphical Models
   - Ontology for Federated Machine Learning
   - Ontology for Smart Contracts
   - Ontology for Computer Vision
   - Ontology for Natural Language Processing
   - Ontology for Robotics and Multi-agent Systems
   - Ontology for Voice-video-speech
   - Ontology for Game Theory
   - 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:
*KR2021_authors_kit
<https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2Fdownloads%2FKR21_authors_kit.zip&sa=D&sntz=1&usg=AFQjCNFYtenqTF-Mya6ZrNGgBeit_xLXyw>*

Papers are to be submitted through the workshop's *EasyChair
<https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2F&sa=D&sntz=1&usg=AFQjCNG4iC-6zde_-RJieT-QIFfKn8WSyA>*
submission
page.

We welcome the following types of contributions:

   - *Full papers* of up to 9 pages, including abstract, figures and
   appendices (if any), but excluding references and acknowledgements:
   Finished or consolidated R&D works, to be included in one of the Workshop
   topics.
   - *Short papers* of up to 4 pages, excluding references and
   acknowledgements: Ongoing works with relevant preliminary results, opened
   to discussion.


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 *KR 2021
<https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2F&sa=D&sntz=1&usg=AFQjCNFSM6Btf5QWGflVsMEJT0AEx1kD7Q>*
 page.


** Workshop chairs **

   - Sarra Ben Abbès, Engie, France
   - Lynda Temal, Engie, France
   - Nada Mimouni, CNAM, France
   - Ahmed Mabrouk, Engie, France
   - Philippe Calvez, Engie, France

** Program Committee **

   -

   Shridhar Devamane, Physical Design Engineer, Tecsec Technologies,
   Bangalore, India
   -

   Philippe Leray, Professor at University of Nantes
   -

   Stefan FENZ, key researcher at SBA Research and Senior Scientist at Vienna
   University of Technology
   <https://informatics.tuwien.ac.at/people/stefan-fenz>
   -

   Olivier Dameron, Professor at Université de Rennes I, Dyliss team, Irisa
   / Inria Rennes-Bretagne Atlantique
   -

   Aarón Ayllón Benitez, Phd in bioinformatique, ontology leader at BASF
   digital solutions
   -

   François Scharffe, Researcher on Knowledge based AI, New York, United
   States
   -

   Maxime Lefrançois, Associate Professor at Saint Etienne University,
   France
   -

    Pierre Maret, The QA Company & Saint Etienne University, France

**Publication*

The best papers from this workshop may be included in the supplementary
proceedings of KR 2021.
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