# Joint Open Knowledge Base Canonicalization and Linking

arXiv: https://arxiv.org/abs/2212.01207 · HTML: https://mlsanity.com/paper/2212.01207/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 55 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://mlsanity.com/paper/1902.00172/) (2019)
- [EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/1801.03825/) (2018)
- [Open Knowledge Enrichment for Long-tail Entities](https://mlsanity.com/paper/2002.06397/) (2020)
- [Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)](https://mlsanity.com/paper/1907.00083/) (2019)

## Built on by (most cited first, 5 of 5)

- [DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge Graphs](https://mlsanity.com/paper/2601.20311/) (2026)
- [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)
- [Open Knowledge Base Canonicalization with Multi-task Learning](https://mlsanity.com/paper/2403.14733/) (2024)
- [Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling](https://mlsanity.com/paper/2605.27023/) (2026)

## Abstract (arXiv)

Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts. To address this problem, there are two related tasks: OKB canonicalization (i.e., convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and RPs with their corresponding entities and relations in a curated Knowledge Base (e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit significantly from the other. However, they have been studied in isolation so far. In this paper, we explore the task of joint OKB canonicalization and linking for the first time, and propose a novel framework JOCL based on factor graph model to make them reinforce each other. JOCL is flexible enough to combine different signals from both tasks, and able to extend to fit any new signals. A thorough experimental study over two large scale OIE triple data sets shows that our framework outperforms all the baseline methods for the task of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).