Knowledge graphs have been recognized in manufacturing as a suitable technology for integration of multidisciplinary knowledge from heterogeneous data sources. The effective reuse of this knowledge can better inform stakeholders in their decision making processes and consequently, establish a competitive advantage. In contrast to the utilization of knowledge graphs for autonomous decision making systems, less attention in production research has been given to the creative participation of humans in the exploration of manufacturing knowledge graphs. Exploratory search systems are a promising solution to facilitate this participation. However, most exploratory search systems focus on general knowledge graphs for which common knowledge is sufficient. We argue that within the complex environment of manufacturing, closer attention has to be paid to particular exploratory search features. In this paper, we therefore present a configurable and adaptive exploratory search system, which implements three special features. Firstly, adaptability of the system to multiple (engineering) perspectives. Secondly, visibility of provenance details about statements to simplify investigative work. And finally, a tree view for browsing deep hierarchical structures.
When reusing software architectural knowledge, such as design patterns or design decisions, software architects need support for exploring architectural knowledge collections, e.g., for finding related items. While semantic-based architectural knowledge management tools are limited to supporting lookup-based tasks through faceted search and fall short of enabling exploration, semantic-based exploratory search systems primarily focus on web-scale knowledge graphs without having been adapted to enterprise-scale knowledge graphs (EKG). We investigate how and to what extent exploratory search can be supported on EKGs of architectural knowledge. We propose an approach for building exploratory search systems on EKGs and demonstrate its use within Siemens, which resulted in the STAR system used in practice by 200–300 software architects. We found that the EKG’s ontology allows making previously implicit organisational knowledge explicit and this knowledge informs the design of suitable relatedness metrics to support exploration. Yet, the performance of these metrics heavily depends on the characteristics of the EKG’s data. Therefore both statistical and user-based evaluations can be used to select the right metric before system implementation.