Major industries including manufacturing, transport and logistics, personal and public health, smart cities and smart energy, crisis management and many others spanning commercial, civic, and scientific operations that involve sensors and actuators exposed on the web. In potential combination with other AI techniques, the Semantic Web offers representational, analytical, and reasoning capabilities that are important for the development of advanced applications that rely on sensors and actuators, potentially geographically distributed and/or exposed on the Web of Things. SAW 2019 targets Semantic Web practitioners that represent, reason with, publish, or use knowledge related to sensors and actuators in general. This workshop gives a breath of fresh air to the Semantic Sensor Network Workshop series that started within ISWC in 2006, was complemented by special tracks at ESWC since 2010, and was resumed by a successful 9th edition at ISWC 2019 which benefited from renewed energy arising from the October 2017 W3C recommendation and OGC standard, and more importantly, increases significance due to the growth in IoT enabled applications. SAW 2019 is organized by the contributors to SOSA/SSN and particularly welcomes early adopters of this ontology, whose work may not be mature enough to get published at the main conference, or consist in migrating previous work.
The SAW workshop is a half-day event that strengthens the community around the ontological representation of sensor and actuation data and welcomes researchers from related communities. It is highly interactive, so as to facilitate discussions among participants that could result in future collaborative work. Two invited talks were given by Raphaël Troncy and Payam Barnaghi, and three long research papers. The event ended with a general discussion on experience with relevant ontologies and ideas for next steps.
|14:00-14:30||Keynote||Payam Barnaghi. Search, Discovery and Analysis of Sensory Data Streams (slides)|
|14:30-14:55||Long paper||Mathias De Brouwer, Dörthe Arndt, Pieter Bonte, Filip De Turck and Femke Ongenae. DIVIDE: Adaptive Context-Aware Query Derivation for IoT Data Streams (paper - slides)|
|14:55-15:20||Long paper||Yulia Svetashova, Stefan Schmid and York Sure-Vetter. Semantic Interoperability and Interrater Agreement in Annotation of IoT Data (paper - slides)|
|16:00-16:30||Keynote||Raphaël Troncy. Semantic Technologies for Connected Vehicles in a Web of Things Environment (slides)|
|16:30-16:55||Long paper||Madhawa Perera, Armin Haller and Matt Adcock. A roadmap for semantically-enabled HumanDevice Interactions (paper - slides)|
Keynote: Semantic Technologies for Connected Vehicles in a Web of Things Environment (slides)
Abstract: Vehicles are evolving from purely mechanical entities to highly connected and autonomous ones. This transition is made possible thanks to the emergence of recent technologies, among others 5G, big data or deep learning. While this leads to new business and technical opportunities, making vehicle fleets interoperable is still highly challenging. Many standards are competing and no unique solution fits all situations. Application developers in the automotive domain have to deal with thousands of different signals and attributes, represented in highly heterogeneous formats, and coming from various car architectures. This situation limits the development of modern applications. We hypothesize that a formal model of car signals, in which the definition of signals are uncorrelated with the physical implementations producing them, as well as a common data layer, would improve interoperability between connected cars and their ecosystem. We also propose to develop tools to automatically produce new information from vehicle data and formally enrich contextual knowledge about drivers, vehicles and their situations. In this talk, we present VSSo, a vehicle signal and attribute ontology that builds on the automotive standard VSS, and that follows the SSN/SOSA design pattern for representing observations and actuations. VSSo is comprehensive while being extensible for OEMs, so that they can use additional private signals in an interoperable way. VSSo is used as a future data model in the W3C automotive Working Group. We contribute to the Web of Things specification by aligning VSSo and the driving context ontology with it. We provide automotive-specific requirements and implementations, and highlight the benefit of the Web of Things for automotive application developers.
Bio: Raphael Troncy is an Associate Professor in the Data Science Department of EURECOM where he leads the D2KLab team (Data 2 Knowledge). His research interests include knowledge engineering, methods and tools to develop knowledge graphs in a broad variety of domains, natural language processing and understanding and information extraction from multimedia documents as well as recommender systems. His team has developed numerous open source software such as the NERD (Named Entity Recognition and Disambiguation) and ADEL (Adaptive Entity Linking) frameworks, the Minotour chatbot, the STEM instance matching tool, the entity2vec embedding method, etc. His team maintains also numerous large linked open datasets covering many domains such as tourism (3cixty), cultural heritage (DOREMUS, SILKNOW), automotive (vsso) and media sectors (ASRAEL, MeMAD). He is the Program Chair of the 2019 K-CAP (Knowledge Capture) conference and he will be General Co-Chair of TheWebConference 2022.
University of Surrey, UK
Keynote: Search, Discovery and Analysis of Sensory Data Streams (slides)
Abstract: This talk will discuss the query, search and discovery issues in finding and accessing sensory data streams their content on the Web. Semantic technologies have been widely investigated and used to publish linked and interoperable sensor data on the Web. The semantically annotated data is seen as a key enabler to create a large-scale and integrated web of sensors, actuators and their data. However, discovering, integrating and analysis of patterns and content of the sensor data streams play even a more important role in creating real-world applications using the connected sensing technologies. Using AI, machine learning and analytical models can lead to extracting actionable-information from raw sensory data and integrating heterogeneous and multimodal data sources. We will discuss the applications of AI and machine learning in processing sensor data streams and will explore real-world use-cases in healthcare and remote monitoring applications.
Bio: Payam Barnaghi is Professor of Machine Intelligence in the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey. He works on the development of new machine learning and semantic computing algorithms and techniques for future internet and web systems, with a focus on Internet of Things, time-series data analysis, information search and retrieval and their applications in healthcare and smart cities. He is Deputy Director of the Care Research and Technology Centre at the UK Dementia Research Institute (UK DRI).
The SAW workshop will be a full day event meant to be highly interactive, so as to facilitate discussions among participants that could result in future collaborative work. A keynote/invited talk will be planned, and followed by authors presenting their paper. We will ensure sufficient time will be dedicated to questions and answers for each paper. The session will end with a general discussion session to discuss experience with the ontologies and decide on the next steps. It is expected that most of the standard's editors will be present.
Topics include, but are not limited to:
We invite research papers and demonstration papers, either in long (16 pages) or short (8 pages) format.
MINES Saint-Étienne, France
Maxime Lefrançois is a former student of the Ecole Normale Supérieure de Cachan, and prepared and passed the Agrégation exam in Mechanics in 2008. He then received a Master degree from Grenoble INP in Signal Processing in 2009, and another from Université Grenoble 2 in Informatics in 2010, while being sessional Lecturer in Mechanics at Université Grenoble 1. During his Ph.D. he prepared in the WIMMICS team, INRIA Sophia-Antipolis, he worked on knowledge representation and reasoning for the Meaning-Text linguistic theory. Between 2014 and 2017, he was a post-doctoral researcher at the École des Mines de Saint-Étienne, and was involved in several bilateral, national, and European projects, including the ITEA2 SEAS project in the context of which he bootstrapped the development of the SEAS ontology: a modular and versioned ontology built on top of the OGC&W3C SOSA/SSN standard, that consists of simple ontology patterns that can be instantiated for different engineering-related verticals. Maxime is one of the co-editors of the SOSA/SSN standard, and currently leads an ETSI Specialist Task Force (STF 556) to inject the SEAS proposals in the ETSI SmartM2M SAREF European standard ontology. He also initiated the development of the SPARQL-Generate RDF lifting language, and the cdt:ucum Datatypes. Finally, he has experience in organizing workshops and tutorials in international events. Since 2017 he is Associate Professor in the Connected-Intelligence team at the École des Mines de Saint-Étienne, France
Australian National University, Canberra, Australia
Dr Armin Haller is MBA Director and Senior Lecturer at the Australian National University with a joint appointment at the Research School of Management and the Research School of Computer Science. He teaches courses on Digital Transformation and Data Analytics. He also manages the Australian Office of the W3C, evangelising and promoting the adoption of W3C standards in Australia and supporting and developing the relationships of the W3C with local industry and government agencies. He has a keen interest in several data science disciplines, with a particular research focus on Linked Data and the Internet of Things, where he publishes articles in the relevant premier academic conferences and journals. In 2017, he was chairing the Semantic Sensor Network Ontology working group at the W3C. He is also chairing the Australian Government Linked Data Working Group, a think tank that discusses strategies and develops best practises for the use and the publishing of Linked Open Data in the Australian Government.
University of California, Santa Barbara
Krzysztof Janowicz is an Associate Professor for Geographic Information Science and Geoinformatics at the Geography Department of the University of California, Santa Barbara, USA. He is the program chair of the Cognitive Science Program, associate director of the Center for Spatial Studies, one of two Editors-in-Chief of the Semantic Web journal, a Faculty Research Affiliate of the Center for Information Technology and Society, and the community leader of the 52° North semantics community. Finally, he is running the STKO Lab which investigates the role of space and time for knowledge organization. Before, he was an Assistant Professor at the GeoVISTA Center, Department of Geography at the Pennsylvania State University, USA. Before moving to the US, he was working as postdoctoral researcher at the Institute for Geoinformatics (ifgi), University of Münster in Germany for the international research training group on Semantic Integration of Geospatial Information and the Münster Semantic Interoperability Lab (MUSIL). Methodologically, his niche is the combination of theory-driven (e.g., semantics) and data-driven (e.g., data mining) techniques.