The first activity of the project focuses on the data collection and completion phase, which takes place within Work Package 1. The majority of the data, whether already collected or to be gathered, pertains to wastewater and stormwater networks, the primary case study for our Starwars project. The datasets are available in various formats, including geographical information systems (GIS) (see Figure 1), images, maps (see Figure 2), videos (see Figure 3), texts, and more. Figure 4 provides an overview of the types of data collected or expected to be collected during the project. Alongside the data collected, we developed an ontology to model a hierarchy of concepts related to physical objects within the context of wastewater networks. A screenshot showing part of the ontology, named SewerNet, is displayed in Figure 5.
Within Work Package 2, we explored description logics and graph-based representations to model the data, capturing the interconnections within wastewater networks (see Figure 6). To handle uncertainty and inconsistencies, we applied possibilistic logic and a partial order relation to assertions. Figure 7 summarizes the different representational languages considered for this project.
In Work Package 3, we proposed tractable methods for conditioning and managing conflicting information. For conditioning, we introduced a syntactic reformulation of FH-conditioning that maintains consistency while incorporating new information. For inconsistency management, we developed more efficient characterizations of elect and possibilistic approaches in partially ordered lightweight ontologies. Additionally, we focused on the classification and extraction of wastewater network components from maps and inspection videos to identify manholes, pipelines, flow directions, etc.
In Work Package 4, we addressed query answering and explainability in the context of imperfect, incomplete, or inconsistent knowledge, or knowledge subject to exceptions. The key question explored here is how to define and compute explanations when dealing with uncertain axioms that may allow for exceptions, as opposed to classical description logic knowledge bases, where axioms are fully certain. Furthermore, rather than estimating missing data, we adapted query answering mechanisms by assigning confidence levels based on data availability.
Finally, we use meta-information to enhance query answering, conflict resolution, and explanation generation. These mechanisms include the use of weights assigned to criteria, a partial order applied to assertions within the knowledge base, and a total preorder on defeasible axioms reflecting their typicality or exceptionality. By leveraging these meta-information techniques, we improve the relevance of query responses, facilitate handling inconsistencies, and support the generation of more informative explanations.
The Starwars project website provides further details on the results obtained, along with a list of publications available in open access.
