Martin Thomas Horsch

Fadi Al Machot, Martin Thomas Horsch, Habib Ullah

Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenges to transparency and trust. To address this, the paper proposes the TranspNet pipeline, which integrates symbolic AI with LLMs. By leveraging domain expert knowledge, retrieval-augmented generation (RAG), and formal reasoning frameworks like Answer Set Programming (ASP), TranspNet enhances LLM outputs with structured reasoning and verification.This approach strives to help AI systems deliver results that are as accurate, explainable, and trustworthy as possible, aligning with regulatory expectations for transparency and accountability. TranspNet provides a solution for developing AI systems that are reliable and interpretable, making it suitable for real-world applications where trust is critical.

Fadi Al Machot, Martin Thomas Horsch, Habib Ullah

This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set programming (ASP). By integrating these symbolic AI methods, we encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process, thereby improving both performance and trustworthiness. The proposed approach is flexible and applicable to both regression and classification tasks, demonstrating generalizability across various fields such as healthcare, autonomous systems, engineering, and battery manufacturing applications. Unlike other state-of-the-art methods, the strength of our approach lies in its scalability across different domains. The design allows for the automation of the loss function by simply updating the ASP rules, making the system highly scalable and user-friendly. This facilitates seamless adaptation to new domains without significant redesign, offering a practical solution for integrating expert knowledge into DL models in industrial settings such as battery manufacturing.

Martin Thomas Horsch, Silvia Chiacchiera, Björn Schembera et al.

The European Materials and Modelling Ontology (EMMO) has recently been advanced in the computational molecular engineering and multiscale modelling communities as a top-level ontology, aiming to support semantic interoperability and data integration solutions, e.g., for research data infrastructures. The present work explores how top-level ontologies that are based on the same paradigm - the same set of fundamental postulates - as the EMMO can be applied to models of physical systems and their use in computational engineering practice. This paradigm, which combines mereology (in its extension as mereotopology) and semiotics (following Peirce's approach), is here referred to as mereosemiotics. Multiple conceivable ways of implementing mereosemiotics are compared, and the design space consisting of the possible types of top-level ontologies following this paradigm is characterized.

Martin Thomas Horsch, Silvia Chiacchiera, Michael A. Seaton et al.

The Virtual Materials Marketplace (VIMMP) project, which develops an open platform for providing and accessing services related to materials modelling, is presented with a focus on its ontology development and data technology aspects. Within VIMMP, a system of marketplace-level ontologies is developed to characterize services, models, and interactions between users; the European Materials and Modelling Ontology (EMMO) is employed as a top-level ontology. The ontologies are used to annotate data that are stored in the ZONTAL Space component of VIMMP and to support the ingest and retrieval of data and metadata at the VIMMP marketplace frontend.