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: *July 02, 2021 *July 19, 2021* - *Workshop paper notifications: *August 06, 2021 - *Workshop paper camera-ready versions due: *September 06, 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.