Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

TL;DR

This paper introduces a GAN-based zero-shot learning approach for knowledge graph completion, enabling recognition of unseen relations from text descriptions without training examples, improving performance on standard datasets.

Contribution

It proposes a novel GAN framework that generates relation embeddings from text descriptions, facilitating zero-shot learning in knowledge graphs.

Findings

Achieves consistent performance improvements on NELL and Wiki datasets.

Model-agnostic approach applicable to various KG embedding methods.

Effectively recognizes unseen relations using only text descriptions.

Abstract

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.