124 citations
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6 papers · 1 filter
Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithm…
Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification
Md Shajalal, Sebastian Denef, Md. Rezaul Karim +2
Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-a…
A Framework for Explainable Concept Drift Detection in Process Mining
Jan Niklas Adams, Sebastiaan J. van Zelst, Lara Quack +3
Rapidly changing business environments expose companies to high levels of uncertainty. This uncertainty manifests itself in significant changes that tend to occur over the lifetime…
Structured Query Construction via Knowledge Graph Embedding
Ruijie Wang, Meng Wang, Jun Liu +2
In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language ques…
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
Martina Garofalo, Maria Angela Pellegrino, Abdulrahman Altabba +1
Industry is evolving towards Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality and improved productivity. A core actor of this growth…
A First Experiment on Including Text Literals in KGloVe
Michael Cochez, Martina Garofalo, Jérôme Lenßen +1
Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based…