GrOWTH: Goal-Oriented End User Development for Web of Things Devices

Overall Objective

GrOWTH project intends to facilite end users to be active participants of Web of Things through a goal-oriented approach which enables end users to model their smart environments based on the desired goals. GrOWTH uses semantic web ontologies for knowledge representation and planning techniques from artificial intelligence to dynamically generate plans at runtime considering user goals, context, and available WoT devices. WoTDL (Web of Things Description Language), is the ontology desgined for describing WoT environments that supports interoperability and follows best practices. Furthermore, WoTDL2API (Web of Things Description Language to API) uses Model-Driven Engineering (MDE) paradigm to automatically generate and deploy RESTful APIs for controlling and accessing data from IoT devices and from the instances of WoTDL.

This research contributes to interoperability at different layers: Semantic interoperability, Device interoperability and Platform interoperability. Semantic interoperability of IoT scenario descriptions is achieved through WoTDL ontology. Device and Platform interoperability are achieved by providing methods that allow to wrap IoT devices with a Web-based interface, thus turning them into WoT devices which allows for easier integration with other platforms and to create composite WoT applications regardless of the hardware specification and the communication protocols.



GrOWTH: Goal-Oriented End User Development for Web of Things Devices

ICWE 2018 Paper

WoTDL2API: Webifying Heterogeneous Internet of Things Devices

ICWE 2019 Demo Video

WoTDL: Web of Things Ontology supporting AI Planning

WoTDL OWL Ontology

Concept Extraction from Web of Things Knowledge Bases

ICWI 2018 Paper (Outstanding Paper Award)

WoTDL2API: Webifying Heterogeneous Internet of Things Devices

WoTDL2API Github Repository

An Automated Cyclic Planning Framework Based on Plan-Do-Check-Act for Web of Things Composition

Web Science 2019 Paper

WoTDL: Web of Things Description Language for Automatic Composition

Web Intelligence 2019 Paper

Automatic Knowledge Extraction to build Semantic Web of Things Applications

IEEE Internet of Things Journal Paper 2019

VISH Dataset and Trained Models

VISH Dataset

Trained Models

VISH:Does Your Smart Home Dialogue Systems Also Need Training Data?

VISH Presentation Slides

VISH Full Youtube Video

Natural Language Goal Understanding for Smart Home Environments

Evaluation Dataset

VISH Grammar

OntoSpect: IoT Ontology Inspection by Concept Extraction and Natural Language Generation

Evaluation Dataset

OntoSpect Github Repository