Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs

TL;DR

This paper introduces hyper-relational temporal knowledge graphs (HTKGs) with explicit timestamps, new benchmark datasets, and a reasoning model to improve temporal fact reasoning over complex, multi-faceted knowledge graphs.

Contribution

It proposes HTKG as a new data structure, creates benchmark datasets Wiki-hy and YAGO-hy, and develops a reasoning model tailored for hyper-relational temporal facts.

Findings

Successfully modeled temporal facts with explicit timestamps.

Provided benchmark datasets for future research.

Developed an efficient reasoning model for HTKGs.

Abstract

Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide additional key-value pairs (i.e., qualifiers) for each KG fact that help to better restrict the fact validity. In recent years, there has been an increasing interest in studying graph reasoning over HKGs. Meanwhile, as discussed in recent works that focus on temporal KGs (TKGs), world knowledge is ever-evolving, making it important to reason over temporal facts in KGs. Previous mainstream benchmark HKGs do not explicitly specify temporal information for each HKG fact. Therefore, almost all existing HKG reasoning approaches do not devise any module specifically for temporal reasoning. To better study temporal fact reasoning over HKGs, we propose a new type of data structure named hyper-relational TKG (HTKG). Every fact in an HTKG is coupled with a timestamp explicitly indicating its time validity. We develop two new benchmark HTKG datasets, i.e., Wiki-hy and YAGO-hy, and propose an HTKG reasoning model that efficiently models hyper-relational temporal facts. To support future research on this topic, we open-source our datasets and model.