# Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

arXiv: https://arxiv.org/abs/2207.14094 · HTML: https://mlsanity.com/paper/2207.14094/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets](https://mlsanity.com/paper/1811.03761/) (2018)
- [Connecting Embeddings for Knowledge Graph Entity Typing](https://mlsanity.com/paper/2007.10873/) (2020)
- [RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings](https://mlsanity.com/paper/2009.07659/) (2020)
- [End-to-End Entity Classification on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2003.12383/) (2020)
- [Putting RDF2vec in Order](https://mlsanity.com/paper/2108.05280/) (2021)
- [Corpus-level Fine-grained Entity Typing](https://mlsanity.com/paper/1708.02275/) (2017)
- [Entity Extraction from Wikipedia List Pages](https://mlsanity.com/paper/2003.05146/) (2020)
- [KGvec2go -- Knowledge Graph Embeddings as a Service](https://mlsanity.com/paper/2003.05809/) (2020)
- [Community Aware Random Walk for Network Embedding](https://mlsanity.com/paper/1710.05199/) (2017)
- [Walk this Way! Entity Walks and Property Walks for RDF2vec](https://mlsanity.com/paper/2204.02777/) (2022)
- [Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities](https://mlsanity.com/paper/1701.02025/) (2017)
- [Investigating Extensions to Random Walk Based Graph Embedding](https://mlsanity.com/paper/2002.07252/) (2020)
- [Entity Type Prediction in Knowledge Graphs using Embeddings](https://mlsanity.com/paper/2004.13702/) (2020)

## Built on by (most cited first, 6 of 6)

- [LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models](https://mlsanity.com/paper/2504.00752/) (2025)
- [The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing](https://mlsanity.com/paper/2404.08313/) (2024)
- [Towards Semantically Enriched Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2308.00081/) (2023)
- [Enriching Taxonomies Using Large Language Models](https://mlsanity.com/paper/2602.22213/) (2025)
- [The X Types -- Mapping the Semantics of the Twitter Sphere](https://mlsanity.com/paper/2409.14584/) (2024)
- [Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs](https://mlsanity.com/paper/2412.17438/) (2024)

## Abstract (arXiv)

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents textitGRAND, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results.