Knowledge Graph Embedding for Hierarchical Entities Based on Auto-Embedding Size.

P Zhang, X Zhang, Y Fang, J Liao… - Mathematics (2227 …, 2024 - search.ebscohost.com
Abstract Knowledge graph embedding represents entities and relations as low-dimensional
continuous vectors. Recently, researchers have attempted to leverage the potential semantic …

[HTML][HTML] TravelRAG: A Tourist Attraction Retrieval Framework Based on Multi-Layer Knowledge Graph

S Song, C Yang, L Xu, H Shang, Z Li… - … International Journal of …, 2024 - mdpi.com
A novel framework called TravelRAG is introduced in this paper, which is built upon a large
language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with …

Large-scale knowledge graph representation learning

M Badrouni, C Katar, W Inoubli - Knowledge and Information Systems, 2024 - Springer
The knowledge graph emerges as powerful data structures that provide a deep
representation and understanding of the knowledge presented in networks. In the pursuit of …

[HTML][HTML] Universal Knowledge Graph Embedding Framework Based on High-Quality Negative Sampling and Weighting

P Zhang, H Peng, Y Fang, Z Yang, Y Hu, Z Tan, W Xiao - Mathematics, 2024 - mdpi.com
The traditional model training approach based on negative sampling randomly samples a
portion of negative samples for training, which can easily overlook important negative …

Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering

C Zhang, Z Peng, J Zheng, L Wang, R Shi… - arXiv preprint arXiv …, 2024 - arxiv.org
Complex Logical Query Answering (CLQA) over incomplete knowledge graphs is a
challenging task. Recently, Query Embedding (QE) methods are proposed to solve CLQA by …