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.
|Titel des Sammelwerks||Proceedings of the Seventh International Workshop on the Visualization and Interaction for Ontologies and Linked Data|
|Untertitel des Sammelwerks||co-located with the 21st International Semantic Web Conference (ISWC 2022)|
|Herausgeber*innen||Bo Fu, Patrick Lambrix, Catia Pesquita|
|Publikationsstatus||Veröffentlicht - 2022|
|Veranstaltung||7th International Workshop on the Visualization and Interaction for Ontologies and Linked Data, VOILA! 2022 - Virtual, Hangzhou, China|
Dauer: 23 Okt. 2022 → …
|Reihe||CEUR Workshop Proceedings|
|Konferenz||7th International Workshop on the Visualization and Interaction for Ontologies and Linked Data, VOILA! 2022|
|Zeitraum||23/10/22 → …|
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