# Triple Trustworthiness Measurement for Knowledge Graph

arXiv: https://arxiv.org/abs/1809.09414 · HTML: https://mlsanity.com/paper/1809.09414/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [KGGen: Extracting Knowledge Graphs from Plain Text with Language Models](https://mlsanity.com/paper/2502.09956/) (2025)
- [Contrastive Knowledge Graph Error Detection](https://mlsanity.com/paper/2211.10030/) (2022)
- [Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs](https://mlsanity.com/paper/2501.15791/) (2025)
- [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://mlsanity.com/paper/2310.12011/) (2023)
- [Knowledge Graph Error Detection with Contrastive Confidence Adaption](https://mlsanity.com/paper/2312.12108/) (2023)
- [Knowledge Graph Curation: A Practical Framework](https://mlsanity.com/paper/2208.08130/) (2022)
- [ADKGD: Anomaly Detection in Knowledge Graphs with Dual-Channel Training](https://mlsanity.com/paper/2501.07078/) (2025)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability](https://mlsanity.com/paper/2408.00872/) (2024)
- [Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation](https://mlsanity.com/paper/2206.13255/) (2022)
- [Knowledge Graph Quality Evaluation under Incomplete Information](https://mlsanity.com/paper/2212.00994/) (2022)
- [Conflict Detection for Temporal Knowledge Graphs:A Fast Constraint Mining Algorithm and New Benchmarks](https://mlsanity.com/paper/2312.11053/) (2023)
- [Type Information-Assisted Self-Supervised Knowledge Graph Denoising](https://mlsanity.com/paper/2503.09916/) (2025)
- [A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models](https://mlsanity.com/paper/2505.19286/) (2025)
- [Knowledge Homophily in Large Language Models](https://mlsanity.com/paper/2509.23773/) (2025)
- [WiCleanData: Guaranteeing the Type Consistency of Wikidata by Taxonomy Refinement and Constraint Enforcement](https://mlsanity.com/paper/2609.20057/) (2026)

## Abstract (arXiv)

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conflicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. The model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output confidence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. The experimental results showed that compared with other models, our model achieved significant and consistent improvements.