# Connecting Embeddings for Knowledge Graph Entity Typing

arXiv: https://arxiv.org/abs/2007.10873 · HTML: https://mlsanity.com/paper/2007.10873/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 17 of 17)

- [Type-aware Embeddings for Multi-Hop Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2205.00782/) (2022)
- [Transformer-based Entity Typing in Knowledge Graphs](https://mlsanity.com/paper/2210.11151/) (2022)
- [Context-aware Entity Typing in Knowledge Graphs](https://mlsanity.com/paper/2109.07990/) (2021)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding](https://mlsanity.com/paper/2112.10067/) (2021)
- [HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations](https://mlsanity.com/paper/2108.05774/) (2021)
- [Entity Type Prediction Leveraging Graph Walks and Entity Descriptions](https://mlsanity.com/paper/2207.14094/) (2022)
- [PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning](https://mlsanity.com/paper/2204.13957/) (2022)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [Towards Semantically Enriched Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2308.00081/) (2023)
- [KAE: A Property-based Method for Knowledge Graph Alignment and Extension](https://mlsanity.com/paper/2407.05320/) (2024)
- [Recognizing Entity Types via Properties](https://mlsanity.com/paper/2304.07910/) (2023)
- [AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations](https://mlsanity.com/paper/2308.16055/) (2023)
- [Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs](https://mlsanity.com/paper/2310.12008/) (2023)
- [Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs](https://mlsanity.com/paper/2412.17438/) (2024)
- [RAPS: A Novel Few-Shot Relation Extraction Pipeline with Query-Information Guided Attention and Adaptive Prototype Fusion](https://mlsanity.com/paper/2210.08242/) (2022)
- [COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing](https://mlsanity.com/paper/2405.13602/) (2024)

## Abstract (arXiv)

Knowledge graph (KG) entity typing aims at inferring possible missing entity type instances in KG, which is a very significant but still under-explored subtask of knowledge graph completion. In this paper, we propose a novel approach for KG entity typing which is trained by jointly utilizing local typing knowledge from existing entity type assertions and global triple knowledge from KGs. Specifically, we present two distinct knowledge-driven effective mechanisms of entity type inference. Accordingly, we build two novel embedding models to realize the mechanisms. Afterward, a joint model with them is used to infer missing entity type instances, which favors inferences that agree with both entity type instances and triple knowledge in KGs. Experimental results on two real-world datasets (Freebase and YAGO) demonstrate the effectiveness of our proposed mechanisms and models for improving KG entity typing. The source code and data of this paper can be obtained from: https://github.com/ Adam1679/ConnectE