# Detecting Cross-Language Plagiarism using Open Knowledge Graphs

arXiv: https://arxiv.org/abs/2111.09749 · HTML: https://mlsanity.com/paper/2111.09749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 62 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019)
- [UsingWord Embedding for Cross-Language Plagiarism Detection](https://mlsanity.com/paper/1702.03082/) (2017)
- [A Resource-Light Method for Cross-Lingual Semantic Textual Similarity](https://mlsanity.com/paper/1801.06436/) (2018)
- [Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations](https://mlsanity.com/paper/1906.11761/) (2019)
- [Forms of Plagiarism in Digital Mathematical Libraries](https://mlsanity.com/paper/1905.03322/) (2019)

## Built on by (most cited first, 1 of 1)

- [Survey on Plagiarism Detection in Large Language Models: The Impact of ChatGPT and Gemini on Academic Integrity](https://mlsanity.com/paper/2407.13105/) (2024)

## Abstract (arXiv)

Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application to Web-scale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA's performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available.