Track 10: Knowledge Management & Ontology engineering
- Arkopaul Sarkar, ENIT France,
- Alessandro Adamou, Bibliotheca Hertziana - Max Planck Institute for Art History, Italy
Knowledge acquisition and management is a multi-disciplinary subject that focuses on methods of collecting, storing, retrieval and exchange of semantically meaningful data in many areas in scientific research and industrial activities. Ontology engineering concerns the development of well-founded semantic models that provides interoperability, consistency, inference capability to knowledge-based applications, and act as a canonical model for semantic integration of heterogeneous data in both storage and communication. Ontology engineering is a multi-disciplinary subject and includes theories from philosophy, computer science, and linguistic to formalize its logical and semantical foundation. Additionally, knowledge management for a particular domain also requires ontology engineers to have a good grasp of domain-specific theories and practices. Such shared vocabulary with a strong foundation on logic forms the vision of semantic web by infusing meaning and context to the existing web data aiming to make them more shareable and integrable. The purpose of this track is to bring together researchers from both theory and practice of knowledge management, ontology engineering, and semantic web technologies.
We invite authors to submit both research and application papers. Research papers in this area are expected to present new knowledge management technology and standards, methodologies for ontology development, evaluation, and maintenance, and new domain-dependent or independent semantic model. Application papers are expected to describe real-life applications and systems that used semantic web technologies and ontologies.
- Knowledge graph (e.g. construction, maintenance, reasoning)
- Knowledge-driven NLP and ML
- Ontology development methodologies
- Ontology learning through information retrieval
- Ontology modularity, mapping, merging, and alignment
- Ontology characterization and evaluation
- Search, Query, Integration and Analysis on the Semantic Web and Knowledge Graphs
- Cleaning, quality assurance, and provenance of Semantic Web data, services, and processes
- Ontology based Neurosymbolic reasoning
- Semantic data exchange and communication (e.g. communication protocols for agents, semantic API, and services)
- Adaptation of formal ontology and semantic web technologies in companies and organizations (success stories, lessons learned).
- Application of formal ontology and semantic web technologies in for engineering (manufacturing, design, etc.).
- Application of formal ontology and semantic web technologies electronic catalogs, e-commerce, e-government, etc.
- Application of formal ontology and semantic web technologies in marketing.
- Application of formal ontology and semantic web technologies in finance.
- Application of formal ontology and semantic web technologies healthcare, medical sciences, and life sciences.
- Application of formal ontology and semantic web technologies in IoT.
- Application of formal ontology and semantic web technologies in energy sector.
- Dusan Sormaz, OHIO University, USA
- Mounira Harzallah, University of Nantes, France,
- Nejib Moalla, University of Lyon, France
- Emna Amdouni, LIRMM, France
- Amir Laadhar, Center for Data-Intensive Systems, Denmark
- Haifa Zargayouna, University of Paris 13, France
- Alsayed Algergawy, Jena University, Germany
- Mustafa Jarrar, Birzeit university, Palestine
- Djamal Benslimane, Lyon 1 University, FRANCE
- Nada Matta, UTT, France
- Sylvie Despres, Laboratoire d'Informatique Médicale et de BIOinformatique, FRANCE
- Nadia Yacoubi, ISG Tunis, Tunisia,
- Sanju Tiwari, Universidad Autonoma de Tamaulipas. Mexico
- Fatiha Saïs, CNRS, France
- Emilio Sanfilippo, CNR, Italy
- Bernard Archimede, ENIT, France,
- Thierry Louge, INP Toulouse, France