Competency questions for ontologies are used in a number of ontology development tasks. The questions’ sentences structure have been analysed to inform ontology authoring and validation. One of the problems to make this a seamless process is the hurdle of writing good CQs manually or offering automated assistance in writing CQs. In this paper, we propose an enhanced and automated pipeline where one can trace meticulously through each step, using a mini-corpus, T5, and the SQuAD dataset to generate questions, and the CLaRO controlled language, semantic similarity, and other steps for filtering. This was evaluated with two corpora of different genre in the same broad domain and evaluated with domain experts. The final output questions across the experiments were around 25% for scope and relevance and 45% of unproblematic quality. Technically, it provided ample insight into trade-offs in generation and filtering, where relaxing filtering increased sentence structure diversity but also led to more spurious sentences that required additional processing

This paper presents a method for the automatic extraction of the structure of wastewater networks from geographical maps, as well as their representation in the form of graphs. The approach consists first in detecting the different important elementary elements composing a wastewater network, such as the manholes, their identifiers (using optical character recognition, OCR), and the wastewater pipes that connect them. Detecting these elementary elements is a first difficult problem, despite many existing tools, mainly due to the quality of the wastewater network maps used. However, the challenge addressed in this paper is how to select the relevant elementary elements detected, and bring them together to finally extract the wastewater network. One of the main contributions of this paper is to propose an efficient algorithm to solve these selection and assignment problems (e.g., manhole identifiers to manholes represented by circles). We also deal with the situation of isolated nodes to have the most connected clusters possible. The experimental results conducted on real map data show very promising results despite the low quality of the maps.