The symbiotic combination of sub-symbolic and symbolic AI techniques is a significant trend in AI, leading to the fast-paced development of various techniques that integrate these paradigms to build intelligent systems. However, the wealth of heterogeneous architectural options for combining the paradigms into Neurosymbolic AI (NeSy-AI) systems poses significant challenges. In particular, there is currently no standardized way to design, engineer, and document such systems that encompass visual and formal notations. Existing works aim to address this challenge by systematically modelling NeSy-AI systems as design patterns that include process, data, and human interactions. However, these works focus on capturing specific views of the system rather than aiming to support the broad process of AI system engineering. This paper outlines a vision of pattern-based AI Systems engineering, aiming to support the engineering process of NeSy-AI systems with tasks such as system documentation and artefact generation through interlinked visual and formal notations with Knowledge Graphs at its core.

In many countries worldwide, including Austria, the environmental impact of production facilities is strongly regulated leading to authorities issuing a large number of legal permits on this topic. The access of interested parties to these permits is typically supported by search systems that present a structured view of the permits along their key elements, such as issuing authority or their legal basis. In this paper, we present a real-life use case from Austria's Environment Agency, where the extraction of such key elements represents a non-trivial task for laypersons with limited legal knowledge: the heterogeneity of data, complex language, and implicit information hinder the manual data extraction process and can lead to poor quality in data management. Based on an analysis of the use case's main requirements, we propose an architecture for a system to support the extraction of key elements from legal permits by laypersons. The system combines methods and techniques based on Knowledge Graphs / Semantic Web and Machine Learning technologies and aims to be auditable in terms of its operation.

Abstract This paper introduces an evolving cybersecurity knowledge graph that integrates and links critical information on real-world vulnerabilities, weaknesses and attack patterns from various publicly available sources. Cybersecurity constitutes a particularly interesting domain for the development of a domain-specific public knowledge graph, particularly due to its highly dynamic landscape characterized by time-critical, dispersed, and heterogeneous information. To build and continually maintain a knowledge graph, we provide and describe an integrated set of resources, including vocabularies derived from well-established standards in the cybersecurity domain, an ETL workflow that updates the knowledge graph as new information becomes available, and a set of services that provide integrated access through multiple interfaces. The resulting semantic resource offers comprehensive and integrated up-to-date instance information to security researchers and professionals alike. Furthermore, it can be easily linked to locally available information, as we demonstrate by means of two use cases in the context of vulnerability assessment and intrusion detection.

The growth of Linked Data has created a promising environment for data exploration and a growing number of tools allow users to interactively integrate data from various sources. Eliciting the reliability of the results of such ad-hoc integration processes, consistently recreating those results, and identifying changes upon re-execution, however, can be difficult. Automated process provenance trail creation can provide major benefits in this context, because (i) it enables users to trace the contribution of individual sources and processing steps to the final outcome and judge whether the result can be trusted; (ii) it ensures repeatability and raises the trustworthiness of results; (iii) it ideally enables reconstruction of Linked Data integration processes from the provenance information embedded in the final result. In this paper, we present a provenance model that facilitates automatic generation of semantic provenance information for generic Linked Data integration processes. We implement the generic model in a collaborative mashup environment and evaluate it by means of an example application. We find that the model provides a solid foundation for verifiability and contributes towards making Linked Data integration processes more open, transparent, and reusable, which is crucial in domains where the origin of data is essential, such as, for instance, statistical analyses, scientific research, and data journalism.