A Review of State-of-the-Art Deep Learning Models for Knowledge Graphs

Lack of comprehensive KG-DL surveys: The paper identifies a lack of comprehensive reviews on the latest deep learning models explicitly designed for knowledge graphs.

KG domain-specific modeling challenges: The reviewed deep learning models are considered in relation to domain-specific challenges in knowledge graph applications.

Dynamic and multimodal KG challenges: The paper identifies unresolved challenges in dynamic knowledge graph updates and multimodal integration.

Lack of comprehensive KG-DL surveys: The paper identifies a lack of comprehensive reviews on the latest deep learning models explicitly designed for knowledge graphs.

KG domain-specific modeling challenges: The reviewed deep learning models are considered in relation to domain-specific challenges in knowledge graph applications.

Survey of DL models for KGs: The paper proposes a thorough review of state-of-the-art deep learning models for knowledge graphs and their applications.

DL model taxonomy for KGs: The paper provides a unified taxonomy of deep learning models for knowledge graphs organized around major paradigms.

Broad model-family coverage: The review covers translation-based, factorization, GNN, LLM, RAG, and multimodal KG models in the knowledge graph context.

Survey of DL models for KGs: The paper proposes a thorough review of state-of-the-art deep learning models for knowledge graphs and their applications.

DL model taxonomy for KGs: The paper provides a unified taxonomy of deep learning models for knowledge graphs organized around major paradigms.

Comprehensive KG-DL overview:

Model effectiveness synthesis:

Trends and open challenges identified: