# Falcon 2.0: An Entity and Relation Linking Tool over Wikidata

arXiv: https://arxiv.org/abs/1912.11270 · HTML: https://mlsanity.com/paper/1912.11270/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era](https://mlsanity.com/paper/2412.18702/) (2024)
- [A Two-Stage Approach towards Generalization in Knowledge Base Question Answering](https://mlsanity.com/paper/2111.05825/) (2021)
- [Generative Relation Linking for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.07337/) (2021)
- [BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations](https://mlsanity.com/paper/2205.04438/) (2022)
- [Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking](https://mlsanity.com/paper/2202.13404/) (2022)
- [HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations](https://mlsanity.com/paper/2108.05774/) (2021)
- [Survey on English Entity Linking on Wikidata](https://mlsanity.com/paper/2112.01989/) (2021)
- [MarkQA: A large scale KBQA dataset with numerical reasoning](https://mlsanity.com/paper/2310.15517/) (2023)
- [Instilling Type Knowledge in Language Models via Multi-Task QA](https://mlsanity.com/paper/2204.13796/) (2022)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding](https://mlsanity.com/paper/2407.01626/) (2024)
- [Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines](https://mlsanity.com/paper/2102.10966/) (2021)
- [Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering](https://mlsanity.com/paper/2409.15902/) (2024)
- [How Contentious Terms About People and Cultures are Used in Linked Open Data](https://mlsanity.com/paper/2311.10757/) (2023)
- [Evaluation of LLMs on Long-tail Entity Linking in Historical Documents](https://mlsanity.com/paper/2505.03473/) (2025)
- [MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations](https://mlsanity.com/paper/2505.14101/) (2025)
- [EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines](https://mlsanity.com/paper/2112.07493/) (2021)
- [Semantic Answer Type and Relation Prediction Task (SMART 2021)](https://mlsanity.com/paper/2112.07606/) (2021)
- [An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph](https://mlsanity.com/paper/2312.02334/) (2023)
- [Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts](https://mlsanity.com/paper/2402.05536/) (2024)
- [The benefits of query-based KGQA systems for complex and temporal questions in LLM era](https://mlsanity.com/paper/2507.11954/) (2025)

## Abstract (arXiv)

The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, still, there are limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, first joint entity, and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is public and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository. We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.