Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
This addresses the challenge of generating meaningful negatives for knowledge graph completion, which is crucial for training embedding models, though it appears incremental as it revamps and extends an existing strategy.
The paper tackles the problem of synthetic negative generation for knowledge graph embedding models by developing a strategy that respects relation domains and ranges, resulting in substantial improvements including +10% MRR on standard benchmarks and over +150% MRR on a larger ontology-backed dataset.
Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Knowledge Graphs (KGs). One of the key aspects of training most of these models is teaching to discriminate between true statements positives and false ones (negatives). However, the way in which negatives can be defined is not trivial, as facts missing from the KG are not necessarily false and a set of ground truth negatives is hardly ever given. This makes synthetic negative generation a necessity. Different generation strategies can heavily affect the quality of the embeddings, making it a primary aspect to consider. We revamp a strategy that generates corruptions during training respecting the domain and range of relations, we extend its capabilities and we show our methods bring substantial improvement (+10% MRR) for standard benchmark datasets and over +150% MRR for a larger ontology-backed dataset.