SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval

TL;DR

This paper introduces SpherE, a novel knowledge graph embedding model that uses spherical embeddings to effectively perform set retrieval tasks, capturing complex relations with high interpretability and accuracy.

Contribution

SpherE is the first model to embed entities as spheres for set retrieval, enhancing expressiveness and interpretability over traditional vector-based methods.

Findings

SpherE effectively models many-to-many relations.

SpherE achieves high accuracy in set retrieval tasks.

The model maintains good predictive performance for missing facts.

Abstract

Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expressive modeling and predictive embedding of KGs, most of the current KG representation learning methods, where each entity is embedded as a vector in the Euclidean space and each relation is embedded as a transformation, follow an entity ranking protocol. On one hand, such an embedding design cannot capture many-to-many relations. On the other hand, in many retrieval cases, the users wish to get an exact set of answers without any ranking, especially when the results are expected to be precise, e.g., which genes cause an illness. Such scenarios are commonly referred to as "set retrieval". This work presents a pioneering study on the KG set retrieval problem. We show that the set retrieval highly depends on expressive modeling of many-to-many relations, and propose a new KG embedding model SpherE to address this problem. SpherE is based on rotational embedding methods, but each entity is embedded as a sphere instead of a vector. While inheriting the high interpretability of rotational-based models, our SpherE can more expressively model one-to-many, many-to-one, and many-to-many relations. Through extensive experiments, we show that our SpherE can well address the set retrieval problem while still having a good predictive ability to infer missing facts. The code is available at https://github.com/Violet24K/SpherE.