Building Dynamic Knowledge Graphs from Text-based Games
Not on the map. Why?
Abstract (arXiv)
We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data points). We conduct experiments and discuss the results.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
- 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14
- 2022Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environmentscited by 1
- 2023Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Gamescited by 1
- 2023Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games3 Nov 2023
- 2022Affordance Extraction with an External Knowledge Database for Text-Based Simulated Environments1 Jul 2022
- 2021A Survey of Text Games for Reinforcement Learning informed by Natural Language20 Sep 2021
- 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environments8 Jul 2021