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.

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community—Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.

The engineering phase of Cyber-Physical Production Systems (CPPS) is a multi-disciplinary process in which representatives of diverse engineering disciplines collaborate to deliver a complex CPPS. To ensure optimal project management as well as to avoid risks of inconsistencies between engineering models created by engineers from different disciplines, support is needed for integrating and subsequently analyzing diverse engineering data. AutomationML is an emerging data exchange format for engineering data which makes the first step towards the easier exchange of engineering data. Yet, there is a lack of tool support for integrating, making sense of and analyzing AML files. In this paper, we explore the use of Semantic Web and Linked Data technologies to provide extended functionality on top of AML that allows advanced data analytics on engineering data such as intuitive browsing of interlinked engineering models and queries for project-wide verification and validation activities. As a result of these investigations, we present the AutomationML Analyzer prototypical implementation to showcase some of the functionalities made possible by Semantic Web and Linked Data technologies in this context.

Modeling engineering knowledge explicitly and representing it by means of standardized modeling languages and in machine-understandable form enables advanced engineering processes in industrial and factory automation. This affects positively both process and product quality. In this paper we explore how the AutomationML format, an emerging data exchange standard, that supports the Industry 4.0 vision, can be represented by means of two established modeling approaches - Model-Driven Engineering (MDE) and Semantic Web. We report observed differences w.r.t. resulting model features and model creation process and, additionally, present the application possibilities of the developed models for engineering process improvement in a production system engineering context.

Modern life is increasingly made more comfortable, efficient, and sustainable by the smart systems that surround us: smart buildings monitor and adjust temperature levels to achieve occupant comfort while optimizing energy consumption; smart energy g