mlsanityData to 14 Oct 2026

Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

arXiv 2207.14094 ↗First posted 28 Jul 2022cs.CL cs.AIcited by 6

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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.

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

20 of 43 references.

References · 20

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8 more
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Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.