# A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts

arXiv: https://arxiv.org/abs/2004.12057 · HTML: https://mlsanity.com/paper/2004.12057/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training](https://mlsanity.com/paper/2201.08081/) (2022)
- [Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension](https://mlsanity.com/paper/2204.02566/) (2022)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
- [Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding](https://mlsanity.com/paper/2210.06444/) (2022)

## Abstract (arXiv)

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner. Experimental results on a benchmark dataset show that the injection of various types of knowledge improves a strong neural network baseline. An additional benefit of our approach is that the graph itself naturally serves as a rational behind the decision making.