The role of knowledge for machine understanding of natural phenomena and language has been growing during the last decade. Knowledge Graphs (KGs) have a large number of applications like semantic search, disambiguation of natural language, deep reasoning, machine reading, and text analytics. Additionally KG applied for Internet of Things use cases is an emerging topic, as demonstrated with iot.schema.org currently under development. However, the adoption of standards to create KG is not always consistent to make use of the full potential of the Open Web Platform’s ability to link one fact to another. This workshop intends to bring together researchers and practitioners that have faced and addressed the challenge of combining diverse methods for knowledge representations in different domains, and for different tasks with Knowledge Graphs by Linked Data experts with the goal of creating and sharing a set of best practices for generating and maintaining KGs.
This workshop aims at bringing together research and industry communities working on the different aspects of data semantics, and standards-based solutions for the Internet and Web of Things.
Important DatesPaper submission deadline: March 15, 2019 (23:59 Hawaii Time) Paper notification: April 2, 2019
News03-10-2018: KOGPIT accepted by KGWC 2019 05-10-2018: First version of the website online
CALL FOR PAPERS
The role of knowledge for machine understanding of natural phenomena and language has been growing during the last decade. Knowledge Graphs (KG) are being widely used by companies such as Google, Facebook, Microsoft, Apple, etc. for this purpose to extract knowledge out of the vast amounts of information on the Web. Additionally KG applied for Internet of Things (IoT) use cases is an emerging topic, as demonstrated with iot.schema.org which is under development. Rapid growth in the Internet of Things (IoT) means that connected sensors and actuators will be inundating the Web infrastructure with data. Semantics is increasingly seen as a key enabler for integration of sensor data and the broader Web ecosystem. Building intelligent applications for everyday use is the long-cherished aim of Artificial Intelligence (AI). With numerous devices deployed and used in day-to-day applications including mobile phones, tablets, wearable and other connected sensing and actuation devices, collectively referred to as the Internet of Things (IoT). Thus, there is an unprecedented opportunity to develop contextually intelligent applications with far- reaching societal implications. They can deliver fine-grained services in various areas such as healthcare, manufacturing, transportation and social good. However, the adoption of standards to create KG is not always consistent to make use of the full potential of the Open Web Platform's ability to link one fact to another. There are several challenges such as how to design interoperable KGs to ensure reusability? How to measure the quality of KGs according to a set of qualitative criteria? Hence, there is a need to develop best practices to easily discover and reuse existing knowledge.
The purpose of the workshop is to discuss how Semantic Web standards and/or AI related techniques can help create and consume data in IoT following clear methodology to build intelligent applications. This workshop aims at bringing together research and industry communities working on the different aspects of data semantics, and standard-based solutions for the Internet and Web of Things.
Topics of interest include, but not limited to:
- Best practices for Knowledge Graph creation and maintenance
- Dynamic Knowledge Graphs curation and improvement
- Ontology evolution
- Knowledge Graph applications, and use cases
- Ontology, and data quality assessment methodologies and tools
- Ontology assessment metrics, ontology usability, ontology ranking, ontology evaluation, ontology creation methodology
- Knowledge Graph evaluation methodologies and tools
- Linked Data Quality assessment and improvement methodologies and tools
- Temporal knowledge extraction, knowledge evolution
- Knowledge Graph Embedding techniques
- Knowledge extraction using ontologies
- iot.schema.org, Knowledge Graph for Internet of Things (IoT)
- Ontology catalogs for Internet of Things (IoT)
- Semantic interoperability for Internet of Things (IoT)
- Machine Learning and Dynamic Knowledge Graph curation and evolution
- IoT application use cases in various domains: healthcare, smart agriculture, smart, transportation, robotics, industry 4.0, etc.
- Semantic Web used in constrained devices (e.g., mobile devices)
The submissions must be written in English using the Springer style (LNCS/CCIS one-column page format). We receive full papers (12-15 pages), short papers (6-8 pages) and demos (2-4 pages). Please note that HTML+RDFa contributions are also welcome as long as the layout complies with the LNCS style.
All proposed papers must be submitted using the KOGPIT19 conference management system.
Important DatesPaper submission deadline: March 15, 2019 (23:59 Hawaii Time) Paper notification: April 2nd, 2019 (23:59 Hawaii Time) Camera-ready Paper Due: April 9th, 2019 (23:59 Hawaii Time) Workshop Day: June 24th, 2019
Following is the list of program committee members in no particular order :
- José Luis Redondo García, Amazon Research (UK)
- Aidan Hogan, DCC, Universidad de Chile (CL)
- Soumya Kanti Datta, EURECOM (FR)
- Michel Dumontier, Maastricht University (NL)
- Pankesh Patel, Fraunhofer CESE (US)
- Mahda Noura, Technische Universitat Chemnitz, (GE)
- Sarasi Lalithsena, IBM (US)
- Raúl García-Castro, Universidad Politécnica de Madrid (ES)
- Elena Demidova, L3S Research Center (GE)
- Michele Ruta, Politecnico di Bari (IT)
- Victor Charpenay, Siemens AG (GE)
- Bernadette Farias Lóscio, Federal University of Pernambuco (BR)
- Shreyansh Bhatt, Kno.e.sis (US)
- Juan Antonio Lossio-Ventura, University of Florida (US)
- Oscar Corcho, Universidad Politécnica de Madrid (ES)
- Maria Esther Vidal, Universidad Simon Bolivar (VE)
- María Poveda Villalón, Universidad Politécnica de Madrid (ES)
- Hacene Cherfi, France, IBM