Buildings employ an ensemble of technical systems like those for heating and ventilation. Ontologies such as Brick, IFC, SSN/SOSA, and SAREF have been created to describe such technical systems in a machine-understandable manner. However, these focus on describing system topology, whereas several relevant use cases (e.g., automated fault detection and diagnostics (AFDD)) also need knowledge about the physical processes. While mathematical simulation can be used to model physical processes, these are practically expensive to run and are not integrated with mainstream technical systems ontologies today. We propose to describe the effect of component actuation on underlying physical mechanisms within component stereotypes. These stereotypes are linked to actual component instances in the technical system description, thereby accomplishing an integration of knowledge about system structure and physical processes. We contribute an ontology for such stereotypes and show that it covers 100% of Brick heating, ventilation, and air-conditioning (HVAC) components. We further show that the ontology enables automatically inferring relationships between components in a real-world building in most cases, except in two situations where component dependencies are underreported. This is due to missing component models for passive parts like splits and join in ducts, and hence points at concrete future extensions of the Brick ontology. Finally, we demonstrate how AFDD applications can utilize the resulting knowledge graph to find expected consequences of an action, or conversely, to identify components that may be responsible for an observed state of the process.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Standards are an important source of knowledge in product development. Due to the increasing digitization of the product development process, standard development organizations aim to develop machine-actionable standards that automatically enforce operations in output devices. However, the current representation format in PDF or XML does not meet the requirements of machine-actionable standards. This paper examines existing approaches towards the representation of XML data in knowledge graphs and their transferability towards the domain of digital standards. Based on these approaches, the paper aims to develop and validate a concept for transferring standard content from XML format to a graph-based representation, using the example of formulas. For this purpose, a concept for the automatic identification, extraction and modeling of formulas will be presented. Afterwards, the concept is validated using the example of DIN ISO 281 whereas a chatbot application serves as conversational user interface. It is proven, that knowledge graphs are suitable for the representation of machine-actionable standard content. Future work will investigate the abstraction towards a general approach as well as further information objects of standards.

This article presents our approach for conflict management in our knowledge acquisition tool KATEMES, aimed at tackling multiple experts. We offer a method for helping the knowledge engineer to detect expertise conflicts in the framework of the KADS knowledge acquisition method. We also propose techniques for conflict management through comparison of knowledge graphs.