Connectionists: [CFP] 2nd International workshop on Ontology Uses and Contribution to Artificial Intelligence @PAKDD-2022

[Sarra Ben Abbès]

Dear colleagues and researchers,

Please consider contributing to the 2nd edition of the
international workshop "* Ontology Uses and Contribution to Artificial
Intelligence* ", in conjunction with  *PAKDD 2022*
<http://www.pakdd.net/> which
will be held online or in Chengdu, China - May 16 - 19, 2022.

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                           The deadline for paper submissions is *March 11,
2022*

==================================================================

*OnUCAI-2022*

2nd International workshop on Ontology Uses and Contribution to Artificial
Intelligence at *PAKDD 2022* <http://www.pakdd.net/>, Chengdu, China - May
16 - 19, 2022
Workshop website: https://sites.google.com/view/onucai-pakdd-2022

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*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.

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

*Topics of interest:*

* Ontology for Machine Learning/Deep Learning

* Ontology for Uncertainty and Probabilistic Graphical Models

* Ontology for Edge Computing

* 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 submitting 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:
PAKDD2022_authors_kit
<https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines>


Papers are to be submitted through the workshop's easychair
<https://easychair.org/conferences/?conf=onucai2022> 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 acknowledgments: 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
acknowledgments: Ongoing works with relevant preliminary results, opened to
discussion.

Submitting a paper to the workshop means that the authors agree that at
least one author should attend the workshop to present the paper if the
paper is accepted. For no-show authors, their affiliations will receive a
notification. For further instructions, please refer to the PAKDD 2022
<http://www.pakdd.net/> page.

*Important dates:*

* Workshop paper submission due: *March 11, 2022*

*  Workshop paper notifications: March 31, 2022

* Workshop paper camera-ready versions due: April 15, 2022

* Workshop: May 16-19, 2022 (Half-Day)

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

*Publication:*

The accepted papers of this workshop may be included in the Proceedings of
PAKDD 2022 Workshops published by Springer.

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*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

*  Oudom Kem, Researcher at Engie, France

*  Philippe Leray, Professor at University of Nantes

*  Stefan Fenz, key researcher at SBA Research and Senior Scientist at
Vienna University of Technology

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

*  Ammar Mechouche, Data Science expert at AIRBUS Helicopters

*  Aarón Ayllón Benitez, PhD in bioinformatics and Ontology Lead at BASF
Digital Solutions S.L.

*  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

*  Sanju Tiwari, Universidad Autonoma de Tamaulipas, Mexico
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