# Predicting Completeness in Knowledge Bases

arXiv: https://arxiv.org/abs/1612.05786 · HTML: https://mlsanity.com/paper/1612.05786/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2016.

## Built on by (most cited first, 15 of 15)

- [Knowing the No-match: Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2106.02248/) (2021)
- [I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation Learning](https://mlsanity.com/paper/2208.09828/) (2022)
- [UnCommonSense: Informative Negative Knowledge about Everyday Concepts](https://mlsanity.com/paper/2208.09292/) (2022)
- [Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey](https://mlsanity.com/paper/2305.05403/) (2023)
- [CascadER: Cross-Modal Cascading for Knowledge Graph Link Prediction](https://mlsanity.com/paper/2205.08012/) (2022)
- [Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs](https://mlsanity.com/paper/2306.02679/) (2023)
- [Finetuning Generative Large Language Models with Discrimination Instructions for Knowledge Graph Completion](https://mlsanity.com/paper/2407.16127/) (2024)
- [Predicting Document Coverage for Relation Extraction](https://mlsanity.com/paper/2111.13611/) (2021)
- [Semi-constraint Optimal Transport for Entity Alignment with Dangling Cases](https://mlsanity.com/paper/2203.05744/) (2022)
- [How Stable is Knowledge Base Knowledge?](https://mlsanity.com/paper/2211.00989/) (2022)
- [Textual Entailment for Effective Triple Validation in Object Prediction](https://mlsanity.com/paper/2401.16293/) (2024)
- [Explaining ELH Concept Descriptions through Counterfactual Reasoning](https://mlsanity.com/paper/2301.05109/) (2023)
- [Progressive Knowledge Graph Completion](https://mlsanity.com/paper/2404.09897/) (2024)
- [Guiding Catalogue Enrichment with User Queries](https://mlsanity.com/paper/2406.07098/) (2024)
- [Autoregressive Language Models for Knowledge Base Population: A case study in the space mission domain](https://mlsanity.com/paper/2503.18502/) (2025)

## Abstract (arXiv)

Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate different signals to identify the areas where a knowledge base is complete. We show that we can combine these signals in a rule mining approach, which allows us to predict where facts may be missing. We also show that completeness predictions can help other applications such as fact prediction.