Year: 2026
Type: conference-paper
Abstract: Knowledge Graphs (KGs) provide explicit relational structure, while Large Language Models (LLMs) encode rich semantic knowledge.We propose a lightweight linguistic initialization strategy for heterogeneous link prediction that improves robustness under sparsity and imbalance.For each node, we construct a compact textual view combining intrinsic description and local neighborhood context, encode it with a pre-trained language model, and use the resulting embeddings to initialize a relation-aware GNN.This design preserves standard message passing while providing early semantically meaningful representations.Across multiple imbalance regimes and strict entity-to-entity cold-start settings, the proposed initialization consistently improves over random initialization and reduces degree-dependent degradation.Our results show that semantic grounding can be integrated into heterogeneous GNN pipelines with minimal architectural changes and strong empirical benefits.
Authors: Daniele Pasquini, Danilo Croce, Roberto Basili
Institution: University of Rome Tor Vergata
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166590942