Semantic Modeling of Waste Dataflow for Automating Circular Economy Systems

Circular Economy (CE) is a model with a concrete action plan covering the whole life cycle of a product, from production and consumption to waste management (WM). Information technologies considerably contribute to the transition towards CE, e.g., waste tracking using Internet of Things (IoT). This will cause the businesses and organizations to confront a large diversity of data (i.e. waste amount, types, locations, etc.). The generated data is often stored and processed through manual or semi-manual methods by each business or organization. However, an automated method which can also interpret and integrate the diverse data in WM fields across different organizations is still in its infancy. Often, such data is not organized and falls short of reaching its full potential in facilitating coordinated management and enabling Circular Economy initiatives. In this paper, we aim to address this need through automated interpretation and integration of municipal waste data by applying semantic data modeling. Our approach proposes to capture the semantical description of entities in the WM process and their relations, which can appear between waste producers, authorities and consumers. Then, the obtained semantic model will facilitate and automate the required interpretation and integration of waste data, both for intra- and inter-organization scenarios. We realize intelligent semantic-based searching using natural language processing and large language models.