Link Prediction for Event Logs in the Process Industry

TL;DR

This paper presents a novel record linking model for event logs in the process industry, combining NLP techniques to improve data connectivity and support better decision-making.

Contribution

It adapts cross-document coreference resolution with NLI and semantic similarity for link prediction in fragmented process industry logs, outperforming baseline models.

Findings

The model outperformed baselines by over 27% in link prediction accuracy.

Combining NLP tasks improves data quality in German process industry logs.

The approach enhances knowledge management and operational decision-making.

Abstract

In the era of graph-based retrieval-augmented generation (RAG), link prediction is a significant preprocessing step for improving the quality of fragmented or incomplete domain-specific data for the graph retrieval. Knowledge management in the process industry uses RAG-based applications to optimize operations, ensure safety, and facilitate continuous improvement by effectively leveraging operational data and past insights. A key challenge in this domain is the fragmented nature of event logs in shift books, where related records are often kept separate, even though they belong to a single event or process. This fragmentation hinders the recommendation of previously implemented solutions to users, which is crucial in the timely problem-solving at live production sites. To address this problem, we develop a record linking model, which we define as a cross-document coreference resolution (CDCR) task. Record linking adapts the task definition of CDCR and combines two state-of-the-art CDCR models with the principles of natural language inference (NLI) and semantic text similarity (STS) to perform link prediction. The evaluation shows that our record linking model outperformed the best versions of our baselines, i.e., NLP and STS, by 28% (11.43 p) and 27.4% (11.21 p), respectively. Our work demonstrates that common NLP tasks can be combined and adapted to a domain-specific setting of the German process industry, improving data quality and connectivity in shift logs.