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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Enhancing TransE to Predict Process Behavior in Temporal Knowledge Graphs
verfasst von : Aleksei Karetnikov, Lisa Ehrlinger, Verena Geist
Erschienen in: Database and Expert Systems Applications - DEXA 2022 Workshops
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Abstract
Temporal knowledge graphs allow to store process data in a natural way since they also model the time aspect. An example for such data are registration processes in the area of intellectual property protection. A common question in such settings is to predict the future behavior of a (yet unfinished) process. However, traditional process mining techniques require structured data, which is typically not available in this form in such communication-intensive domains. In addition, there exists a number of knowledge graph embedding methods based on neural networks, which are too performance-demanding for large real-world graphs. In this paper, we propose several extensions for preprocessing process data that will be embedded in the traditional triple-based TransE knowledge graph embedding model to predict process behavior in temporal knowledge graphs. We evaluate our approach by means of a real-world trademark registration process in a patent office and show its improved performance compared to the TransE base model.