Type-Less yet Type-Aware Inductive Link Prediction with Pretrained Language Models

ACL ARR 2025 May Submission6841 Authors

Abstract:

Inductive link prediction is emerging as a key paradigm for real-world knowledge graphs (KGs), where new entities frequently appear and models must generalize to them without retraining. Predicting links in a KG faces the challenge of guessing previously unseen entities by leveraging generalizable node features such as subgraph structure, type annotations, and ontological constraints. However, explicit type information is often lacking or incomplete.
Even when available, type information in most KGs is often coarse-grained, sparse, and prone to errors due to human annotation.
In this work, we explore the potential of pre-trained language models (PLMs) to enrich node representations with implicit type signals. We introduce TyleR, a Type-less yet type-awaRe approach for subgraph-based inductive link prediction that leverages PLMs for semantic enrichment. Experiments on standard benchmarks demonstrate that TyleR outperforms state-of-the-art baselines in scenarios with scarce type annotations and sparse graph connectivity. To ensure reproducibility, we share our code at https://anonymous.4open.science/r/tyler-7C2C/ .

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Machine Learning for NLP, Information Extraction, NLP Applications

Contribution Types: Publicly available software and/or pre-trained models, Theory

Languages Studied: English

Submission Number: 6841