# CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information

arXiv: https://arxiv.org/abs/1902.00172 · HTML: https://mlsanity.com/paper/1902.00172/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 28 of 28)

- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [A Survey on Neural Open Information Extraction: Current Status and Future Directions](https://mlsanity.com/paper/2205.11725/) (2022)
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- [Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge](https://mlsanity.com/paper/2211.10997/) (2022)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://mlsanity.com/paper/2208.08690/) (2022)
- [mOKB6: A Multilingual Open Knowledge Base Completion Benchmark](https://mlsanity.com/paper/2211.06959/) (2022)
- [OKGIT: Open Knowledge Graph Link Prediction with Implicit Types](https://mlsanity.com/paper/2106.12806/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [Multi-View Clustering for Open Knowledge Base Canonicalization](https://mlsanity.com/paper/2206.11130/) (2022)
- [Joint Open Knowledge Base Canonicalization and Linking](https://mlsanity.com/paper/2212.01207/) (2022)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [Knowledge Base Completion Meets Transfer Learning](https://mlsanity.com/paper/2108.13073/) (2021)
- [CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction](https://mlsanity.com/paper/2106.00793/) (2021)
- [Discovering Fine-Grained Semantics in Knowledge Graph Relations](https://mlsanity.com/paper/2202.08917/) (2022)
- [COMBO: A Complete Benchmark for Open KG Canonicalization](https://mlsanity.com/paper/2302.03905/) (2023)
- [Exact and Approximate Hierarchical Clustering Using A*](https://mlsanity.com/paper/2104.07061/) (2021)
- [A Library Perspective on Nearly-Unsupervised Information Extraction Workflows in Digital Libraries](https://mlsanity.com/paper/2205.00716/) (2022)
- ["Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds](https://mlsanity.com/paper/2210.13039/) (2022)
- [Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics](https://mlsanity.com/paper/2406.03242/) (2024)
- [Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning](https://mlsanity.com/paper/2211.03950/) (2022)
- [Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining](https://mlsanity.com/paper/2303.14425/) (2023)
- [Open Knowledge Base Canonicalization with Multi-task Unlearning](https://mlsanity.com/paper/2310.16419/) (2023)
- [Pre-training and Diagnosing Knowledge Base Completion Models](https://mlsanity.com/paper/2401.15439/) (2024)
- [Open Knowledge Base Canonicalization with Multi-task Learning](https://mlsanity.com/paper/2403.14733/) (2024)
- [Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction](https://mlsanity.com/paper/2601.15037/) (2026)
- [Curate Before You Connect: Identity and Ontology Tagging in a Production Knowledge Graph](https://mlsanity.com/paper/2608.10644/) (2026)

## Abstract (arXiv)

Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manuallydefined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) - a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI's effectiveness.