Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
This addresses the cumbersome curation needed for knowledge graph completion, offering a model-agnostic solution for zero-shot relational learning.
The paper tackles the problem of expanding knowledge graphs without needing training examples for new relations by using zero-shot learning, where semantic features are learned from text descriptions to recognize unseen relations, and achieves performance improvements on NELL and Wiki datasets.
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.