# Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI

arXiv: https://arxiv.org/abs/2210.15248 · HTML: https://mlsanity.com/paper/2210.15248/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [EventBERT: A Pre-Trained Model for Event Correlation Reasoning](https://mlsanity.com/paper/2110.06533/) (2021)
- [Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition](https://mlsanity.com/paper/2010.03746/) (2020)
- [SciNLI: A Corpus for Natural Language Inference on Scientific Text](https://mlsanity.com/paper/2203.06728/) (2022)

## Built on by (most cited first, 3 of 3)

- [KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation](https://mlsanity.com/paper/2401.08376/) (2024)
- [MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference](https://mlsanity.com/paper/2404.08066/) (2024)
- [HGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature](https://mlsanity.com/paper/2603.23136/) (2026)

## Abstract (arXiv)

With the advance of natural language inference (NLI), a rising demand for NLI is to handle scientific texts. Existing methods depend on pre-trained models (PTM) which lack domain-specific knowledge. To tackle this drawback, we introduce a scientific knowledge graph to generalize PTM to scientific domain. However, existing knowledge graph construction approaches suffer from some drawbacks, i.e., expensive labeled data, failure to apply in other domains, long inference time and difficulty extending to large corpora. Therefore, we propose an unsupervised knowledge graph construction method to build a scientific knowledge graph (SKG) without any labeled data. Moreover, to alleviate noise effect from SKG and complement knowledge in sentences better, we propose an event-centric knowledge infusion method to integrate external knowledge into each event that is a fine-grained semantic unit in sentences. Experimental results show that our method achieves state-of-the-art performance and the effectiveness and reliability of SKG.