# KAE: A Property-based Method for Knowledge Graph Alignment and Extension

arXiv: https://arxiv.org/abs/2407.05320 · HTML: https://mlsanity.com/paper/2407.05320/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [Connecting Embeddings for Knowledge Graph Entity Typing](https://mlsanity.com/paper/2007.10873/) (2020)
- [Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs](https://mlsanity.com/paper/2107.13257/) (2021)
- [Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities](https://mlsanity.com/paper/1701.02025/) (2017)
- [Background Knowledge in Schema Matching: Strategy vs. Data](https://mlsanity.com/paper/2107.00001/) (2021)
- [Entity Type Recognition using an Ensemble of Distributional Semantic Models to Enhance Query Understanding](https://mlsanity.com/paper/1604.00933/) (2016)
- [DKP-AOM: results for OAEI 2015](https://mlsanity.com/paper/1510.01659/) (2015)

## Built on by (most cited first, 3 of 3)

- [Crowdsourcing of Real-world Image Annotation via Visual Properties](https://mlsanity.com/paper/2604.14449/) (2026)
- [Cross-Source Supervision for Bone Infection Segmentation in Dual-Modality PET-CT](https://mlsanity.com/paper/2605.16373/) (2026)
- [RAG4Outcome: A Retrieval-Augmented Multimodal Framework for Prognostic Prediction in Chronic Osteomyelitis](https://mlsanity.com/paper/2605.22833/) (2026)

## Abstract (arXiv)

A common solution to the semantic heterogeneity problem is to perform knowledge graph (KG) extension exploiting the information encoded in one or more candidate KGs, where the alignment between the reference KG and candidate KGs is considered the critical procedure. However, existing KG alignment methods mainly rely on entity type (etype) label matching as a prerequisite, which is poorly performing in practice or not applicable in some cases. In this paper, we design a machine learning-based framework for KG extension, including an alternative novel property-based alignment approach that allows aligning etypes on the basis of the properties used to define them. The main intuition is that it is properties that intentionally define the etype, and this definition is independent of the specific label used to name an etype, and of the specific hierarchical schema of KGs. Compared with the state-of-the-art, the experimental results show the validity of the KG alignment approach and the superiority of the proposed KG extension framework, both quantitatively and qualitatively.