# Extracting Semantic Process Information from the Natural Language in Event Logs

arXiv: https://arxiv.org/abs/2103.11761 · HTML: https://mlsanity.com/paper/2103.11761/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Extracting Symptoms and their Status from Clinical Conversations](https://mlsanity.com/paper/1906.02239/) (2019)

## Built on by (most cited first, 2 of 2)

- [Large Language Models can accomplish Business Process Management Tasks](https://mlsanity.com/paper/2307.09923/) (2023)
- [Procedural Text Mining with Large Language Models](https://mlsanity.com/paper/2310.03376/) (2023)

## Abstract (arXiv)

Process mining focuses on the analysis of recorded event data in order to gain insights about the true execution of business processes. While foundational process mining techniques treat such data as sequences of abstract events, more advanced techniques depend on the availability of specific kinds of information, such as resources in organizational mining and business objects in artifact-centric analysis. However, this information is generally not readily available, but rather associated with events in an ad hoc manner, often even as part of unstructured textual attributes. Given the size and complexity of event logs, this calls for automated support to extract such process information and, thereby, enable advanced process mining techniques. In this paper, we present an approach that achieves this through so-called semantic role labeling of event data. We combine the analysis of textual attribute values, based on a state-of-the-art language model, with a novel attribute classification technique. In this manner, our approach extracts information about up to eight semantic roles per event. We demonstrate the approach's efficacy through a quantitative evaluation using a broad range of event logs and demonstrate the usefulness of the extracted information in a case study.