The acquisition of commonsense knowledge remains a core challenge in both robotics and artificial intelligence. While Large Language Models (LLMs) encode rich latent knowledge, their unstructured and probabilistic nature limits their direct use in safety-critical domains like robotics. In this paper, we present a pipeline for extracting structured commonsense knowledge from LLMs and integrating it into a symbolic knowledge graph based on the Ontology for Robotic Knowledge Acquisition (ORKA). Grounded in the theory of conceptual spaces, our method targets physical object properties—both categorical (e.g., shape, material, location) and quantifiable (e.g., size, weight, temperature)—relevant to embodied reasoning. We evaluate the pipeline across a diverse set of LLMs, model sizes, and quantization levels, using human-annotated ground truth to assess accuracy. Results show that even compact and quantized models can produce reliable, interpretable knowledge. We also analyze the influence of measurement units and contextual relevance, highlighting trade-offs between model types and use cases. These results indicate that even smaller LLMs can serve as a useful intermediate source for deriving structured commonsense representations in robotics contexts.

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes. We investigate whether KE methods can be applied to HI scenarios, and specifically whether common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. We first adapt the well-known CommonKADS methodology to HI, and then use it to analyze several HI projects and identify common tasks. The results are (i) a high-level ontology of HI knowledge roles, (ii) a set of novel, HI-specific tasks and (iii) an open repository to store scenarios1 – allowing reuse, validation and design of existing and new HI applications.

Deep neural networks have exploded in popularity and different types of networks are used to solve a multitude of complex tasks. One such task is anomaly detection, that a type of deep neural network called auto-encoder has become extremely proficient at solving. The low level neural activity, produced by such a network, generates extremely rich representations of the data, which can be used to extract task specific knowledge. In this paper, we built upon previous work and used co-activation graph analysis to extract knowledge from auto-encoders, that were trained for the specific task of anomaly detection. First, we outlined a method for extracting co-activation graphs from auto-encoders. Then, we performed graph analysis to discover that task specific knowledge from the auto-encoder was being encoded into the co-activation graph, and that the extracted knowledge could be used to reveal the role of individual neurons in the network.

The focus of this work is to exploit ontologies to make robotic systems more accessible to non-expert users, therefore supporting the deployment of robot-integrated applications. Due to the increasing number of robotic platforms available for commercial use, robotic systems are nowadays being approached by users with different backgrounds, who are often more interested in the robots' high-level capabilities than their technical architecture. Without the right expertise however, using robots is restricted to the capabilities exposed by the platform provider, i.e. they can only be used as end products rather than as development platforms. Our hypothesis is that an ontological representation of the capabilities of robots could make these capabilities more accessible, reducing the complexity of robot programming and enabling non-experts to exploit these systems to a much larger extent. To demonstrate this, an ontology abstracting the capabilities exposed by the most common robotic middleware (ROS) is integrated in a system to allow non-experts to program robots of different types and capabilities without previous knowledge either of the specific robotic platform being considered, or of the intricate systems used in its implementation. Our experiments, in which non-experts users had to configure the system in order to make robots achieve different tasks, show how the efforts required for realizing basic tasks using available robotic platforms can be sensibly reduced through our approach.

In this paper, we propose an ontology design pattern for the concept of "explanation". The motivation behind this work comes from our research, which focuses on automatically identifying explanations for data patterns. If we want to produce explanations from data agnostically from the application domain, we first need a formal definition of what an explanation is, i.e. which are its components, their roles or their interactions. We analysed and surveyed works from the disciplines grouped under the name of Cognitive Sciences, with the aim of identifying differences and commonalities in the way their researchers intend the concept of explanation. We then produced not only an ontology design pattern to model it, but also the instantiations of this in each of the analysed disciplines. Besides those contributions, the paper presents how the proposed ontology design pattern can be used to analyse the validity of the explanations produced by our, and other, frameworks.