# Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs

arXiv: https://arxiv.org/abs/2206.08709 · HTML: https://mlsanity.com/paper/2206.08709/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Entity Linking in 100 Languages](https://mlsanity.com/paper/2011.02690/) (2020)
- [Aligning Cross-Lingual Entities with Multi-Aspect Information](https://mlsanity.com/paper/1910.06575/) (2019)
- [A Study of the Quality of Wikidata](https://mlsanity.com/paper/2107.00156/) (2021)
- [Facilitating Terminology Translation with Target Lemma Annotations](https://mlsanity.com/paper/2101.10035/) (2021)
- [Design Challenges in Named Entity Transliteration](https://mlsanity.com/paper/1808.02563/) (2018)
- [USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition](https://mlsanity.com/paper/2203.03216/) (2022)
- [Assessing the quality of sources in Wikidata across languages: a hybrid approach](https://mlsanity.com/paper/2109.09405/) (2021)

## Built on by (most cited first, 2 of 2)

- [ParaNames 1.0: Creating an Entity Name Corpus for 400+ Languages using Wikidata](https://mlsanity.com/paper/2405.09496/) (2024)
- [Demographic Attributes Prediction from Speech Using WavLM Embeddings](https://mlsanity.com/paper/2502.12007/) (2025)

## Abstract (arXiv)

Knowledge bases such as Wikidata amass vast amounts of named entity information, such as multilingual labels, which can be extremely useful for various multilingual and cross-lingual applications. However, such labels are not guaranteed to match across languages from an information consistency standpoint, greatly compromising their usefulness for fields such as machine translation. In this work, we investigate the application of word and sentence alignment techniques coupled with a matching algorithm to align cross-lingual entity labels extracted from Wikidata in 10 languages. Our results indicate that mapping between Wikidata's main labels stands to be considerably improved (up to 20 points in F1-score) by any of the employed methods. We show how methods relying on sentence embeddings outperform all others, even across different scripts. We believe the application of such techniques to measure the similarity of label pairs, coupled with a knowledge base rich in high-quality entity labels, to be an excellent asset to machine translation.