# Open Knowledge Enrichment for Long-tail Entities

arXiv: https://arxiv.org/abs/2002.06397 · HTML: https://mlsanity.com/paper/2002.06397/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2020.

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

- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search](https://mlsanity.com/paper/2311.07237/) (2023)
- [Attributing Culture-Conditioned Generations to Pretraining Corpora](https://mlsanity.com/paper/2412.20760/) (2024)
- [Dense-ATOMIC: Towards Densely-connected ATOMIC with High Knowledge Coverage and Massive Multi-hop Paths](https://mlsanity.com/paper/2210.07621/) (2022)
- [Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox](https://mlsanity.com/paper/2307.08235/) (2023)
- [Community Question Answering Entity Linking via Leveraging Auxiliary Data](https://mlsanity.com/paper/2205.11917/) (2022)
- [Joint Open Knowledge Base Canonicalization and Linking](https://mlsanity.com/paper/2212.01207/) (2022)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [Data Collection and Labeling Techniques for Machine Learning](https://mlsanity.com/paper/2407.12793/) (2024)
- [An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains](https://mlsanity.com/paper/2411.14551/) (2024)
- [A Web Scale Entity Extraction System](https://mlsanity.com/paper/2110.00423/) (2021)
- [GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs](https://mlsanity.com/paper/2505.24036/) (2025)
- [Understanding the Essence: Delving into Annotator Prototype Learning for Multi-Class Annotation Aggregation](https://mlsanity.com/paper/2508.02123/) (2025)
- [AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling](https://mlsanity.com/paper/2607.18465/) (2026)

## Abstract (arXiv)

Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especially lacking facts of long-tail entities, e.g., less famous persons. Existing approaches enrich KBs mainly on completing missing links or filling missing values. However, they only tackle a part of the enrichment problem and lack specific considerations regarding long-tail entities. In this paper, we propose a full-fledged approach to knowledge enrichment, which predicts missing properties and infers true facts of long-tail entities from the open Web. Prior knowledge from popular entities is leveraged to improve every enrichment step. Our experiments on the synthetic and real-world datasets and comparison with related work demonstrate the feasibility and superiority of the approach.