# OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference

arXiv: https://arxiv.org/abs/1904.12606 · HTML: https://mlsanity.com/paper/1904.12606/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 9 of 9)

- [Refined Commonsense Knowledge from Large-Scale Web Contents](https://mlsanity.com/paper/2112.04596/) (2021)
- [LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction](https://mlsanity.com/paper/2101.11177/) (2021)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [SQUiD: Synthesizing Relational Databases from Unstructured Text](https://mlsanity.com/paper/2505.19025/) (2025)
- [CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction](https://mlsanity.com/paper/2106.00793/) (2021)
- [Mapping and Cleaning Open Commonsense Knowledge Bases with Generative Translation](https://mlsanity.com/paper/2306.12766/) (2023)
- [Multi-facet Universal Schema](https://mlsanity.com/paper/2103.15339/) (2021)
- [Enriching Relation Extraction with OpenIE](https://mlsanity.com/paper/2212.09376/) (2022)
- [Rules still work for Open Information Extraction](https://mlsanity.com/paper/2403.10758/) (2024)

## Abstract (arXiv)

In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level inference relying on embedding for (subject, object) pairs, thus cannot handle pairs absent in any existing triples; or they perform predicate-level mapping and completely ignore background evidence from individual entities, thus cannot achieve satisfying quality. We propose OpenKI to handle sparsity of OpenIE extractions by performing instance-level inference: for each entity, we encode the rich information in its neighborhood in both KB and OpenIE extractions, and leverage this information in relation inference by exploring different methods of aggregation and attention. In order to handle unseen entities, our model is designed without creating entity-specific parameters. Extensive experiments show that this method not only significantly improves state-of-the-art for conventional OpenIE extractions like ReVerb, but also boosts the performance on OpenIE from semi-structured data, where new entity pairs are abundant and data are fairly sparse.