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.
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Acknowledgements
The research reported in this paper has been funded by BMK, BMDW, and the State of Upper Austria in the frame of the COMET Programme managed by FFG, and in particular by the FFG BRIDGE project KnoP-2D (grant no. 871299) and the COMET module S3AI (grant no. 872172).
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Karetnikov, A., Ehrlinger, L., Geist, V. (2022). Enhancing TransE to Predict Process Behavior in Temporal Knowledge Graphs. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_34
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