# Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1810.05682 · HTML: https://mlsanity.com/paper/1810.05682/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 17 of 17)

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- [The Role of Semantic Parsing in Understanding Procedural Text](https://mlsanity.com/paper/2302.06829/) (2023)
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- [Tracking entities in technical procedures -- a new dataset and baselines](https://mlsanity.com/paper/2104.07378/) (2021)
- [Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension](https://mlsanity.com/paper/2204.02566/) (2022)
- [Order-Based Pre-training Strategies for Procedural Text Understanding](https://mlsanity.com/paper/2404.04676/) (2024)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
- [CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural Text](https://mlsanity.com/paper/2310.14326/) (2023)
- [Learning Complementary Action Modeling from Automotive Maintenance Instructions](https://mlsanity.com/paper/2606.27808/) (2026)

## Abstract (arXiv)

We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in downstream question answering tasks to improve machine comprehension of text, as we demonstrate empirically. On two comprehension tasks from the recently proposed PROPARA dataset (Dalvi et al., 2018), our model achieves state-of-the-art results. We further show that our model is competitive on the RECIPES dataset (Kiddon et al., 2015), suggesting it may be generally applicable. We present some evidence that the model's knowledge graphs help it to impose commonsense constraints on its predictions.