# Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

arXiv: https://arxiv.org/abs/1504.06658 · HTML: https://mlsanity.com/paper/1504.06658/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2015.

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

- [Context-aware Entity Typing in Knowledge Graphs](https://mlsanity.com/paper/2109.07990/) (2021)
- [Link Prediction on Latent Heterogeneous Graphs](https://mlsanity.com/paper/2302.10432/) (2023)
- [Discovering Fine-Grained Semantics in Knowledge Graph Relations](https://mlsanity.com/paper/2202.08917/) (2022)
- [The X Types -- Mapping the Semantics of the Twitter Sphere](https://mlsanity.com/paper/2409.14584/) (2024)
- [Cluster-based Mention Typing for Named Entity Disambiguation](https://mlsanity.com/paper/2109.11389/) (2021)

## Abstract (arXiv)

Most of previous work in knowledge base (KB) completion has focused on the problem of relation extraction. In this work, we focus on the task of inferring missing entity type instances in a KB, a fundamental task for KB competition yet receives little attention. Due to the novelty of this task, we construct a large-scale dataset and design an automatic evaluation methodology. Our knowledge base completion method uses information within the existing KB and external information from Wikipedia. We show that individual methods trained with a global objective that considers unobserved cells from both the entity and the type side gives consistently higher quality predictions compared to baseline methods. We also perform manual evaluation on a small subset of the data to verify the effectiveness of our knowledge base completion methods and the correctness of our proposed automatic evaluation method.