Knowledge Hypergraph Embedding Meets Relational Algebra
This work addresses a limitation in knowledge hypergraph reasoning for AI applications, offering a novel method to represent procedural rules, though it is incremental in building on existing embedding techniques.
The authors tackled the problem of link prediction in knowledge hypergraphs by proposing ReAlE, an embedding-based model that captures relational algebra operations, and showed it outperforms state-of-the-art models in completion tasks.
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying the relations in the graph. We propose a simple embedding-based model called ReAlE that performs link prediction in knowledge hypergraphs (generalized knowledge graphs) and can represent high-level abstractions in terms of relational algebra operations. We show theoretically that ReAlE is fully expressive and provide proofs and empirical evidence that it can represent a large subset of the primitive relational algebra operations, namely renaming, projection, set union, selection, and set difference. We also verify experimentally that ReAlE outperforms state-of-the-art models in knowledge hypergraph completion, and in representing each of these primitive relational algebra operations. For the latter experiment, we generate a synthetic knowledge hypergraph, for which we design an algorithm based on the Erdos-R'enyi model for generating random graphs.