# Information Extraction From Co-Occurring Similar Entities

arXiv: https://arxiv.org/abs/2102.05444 · HTML: https://mlsanity.com/paper/2102.05444/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

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

- [The CaLiGraph Ontology as a Challenge for OWL Reasoners](https://mlsanity.com/paper/2110.05028/) (2021)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)
- [Hierarchical Text Classification with LLM-Refined Taxonomies](https://mlsanity.com/paper/2601.18375/) (2026)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)

## Abstract (arXiv)

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or DBpedia are, however, far from being complete. In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. In contrast to existing approaches, we do not focus on relationships within a listing (e.g., between two entities in a table row) but on the relationship between a listing's subject entities and the context of the listing. To that end, we propose a descriptive rule mining approach that uses distant supervision to derive rules for these relationships based on a listing's context. Extracted from a suitable data corpus, the rules can be used to extend a knowledge graph with novel entities and assertions. In our experiments we demonstrate that the approach is able to extract up to 3M novel entities and 30M additional assertions from listings in Wikipedia. We find that the extracted information is of high quality and thus suitable to extend Wikipedia-based knowledge graphs like DBpedia, YAGO, and CaLiGraph. For the case of DBpedia, this would result in an increase of covered entities by roughly 50%.