# Link Prediction for Event Logs in the Process Industry

arXiv: https://arxiv.org/abs/2508.09096 · HTML: https://mlsanity.com/paper/2508.09096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [German's Next Language Model](https://mlsanity.com/paper/2010.10906/) (2020)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://mlsanity.com/paper/2104.05022/) (2021)
- [Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora](https://mlsanity.com/paper/2011.12249/) (2020)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://mlsanity.com/paper/2106.04192/) (2021)
- [Pairwise Representation Learning for Event Coreference](https://mlsanity.com/paper/2010.12808/) (2020)
- [CD2CR: Co-reference Resolution Across Documents and Domains](https://mlsanity.com/paper/2101.12637/) (2021)
- [XCoref: Cross-document Coreference Resolution in the Wild](https://mlsanity.com/paper/2109.05252/) (2021)
- [Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications](https://mlsanity.com/paper/2306.16143/) (2023)
- [Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles](https://mlsanity.com/paper/2404.08949/) (2024)
- [Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information](https://mlsanity.com/paper/2406.15990/) (2024)
- [Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Language](https://mlsanity.com/paper/2412.10008/) (2024)
- [Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language](https://mlsanity.com/paper/2504.19856/) (2025)
- [CEREC: A Corpus for Entity Resolution in Email Conversations](https://mlsanity.com/paper/2105.10606/) (2021)
- [Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons](https://mlsanity.com/paper/2107.00955/) (2021)

## Built on by (most cited first, 1 of 1)

- [Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry](https://mlsanity.com/paper/2510.04631/) (2025)

## Abstract (arXiv)

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.