A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach

TL;DR

This paper introduces HyperRED, a large dataset for hyper-relational extraction, and CubeRE, a cube-filling model that effectively captures complex relation-qualifier structures in knowledge graphs.

Contribution

The paper presents a new dataset for hyper-relational extraction and a novel cube-filling model that considers interactions between relations and qualifiers.

Findings

CubeRE outperforms existing baselines in hyper-relational extraction.

The cube-pruning method improves model scalability and reduces class imbalance.

HyperRED enables more comprehensive knowledge graph construction.

Abstract

Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). Hence, we propose the task of hyper-relational extraction to extract more specific and complete facts from text. To support the task, we construct HyperRED, a large-scale and general-purpose dataset. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. To improve model scalability and reduce negative class imbalance, we further propose a cube-pruning method. Our experiments show that CubeRE outperforms strong baselines and reveal possible directions for future research. Our code and data are available at github.com/declare-lab/HyperRED.