TWO BIRDS, ONE STONE: AN EQUIVALENT TRANS-FORMATION FOR HYPER-RELATIONAL KNOWLEDGE GRAPH MODELING Anonymous authors Paper under double-blind review

Abstract

By representing knowledge in a primary triple associated with additional attributevalue qualifiers, hyper-relational knowledge graph (HKG) that generalizes triplebased knowledge graph (KG) has been attracting research attention recently. Compared with KG, HKG is enriched with the semantic difference between the primary triple and additional qualifiers as well as the structural connection between entities in hyper-relational graph structure. However, to model HKG, existing studies mainly focus on either semantic information or structural information therein, fail to capture both simultaneously. To tackle this issue, in this paper, we propose an equivalent transformation for HKG modeling, referred to as TransEQ. Specifically, the equivalent transformation transforms a HKG to a KG, which considers both semantic and structural characteristics. Then a generalized encoder-decoder framework is developed to bridge the modeling research between KG and HKG. In the encoder part, KG-based graph neural networks are leveraged for structural modeling; while in the decoder part, various HKG-based scoring functions are exploited for semantic modeling. Especially, we design the sharing embedding mechanism in the encoder-decoder framework with semantic relatedness captured. We further theoretically prove that TransEQ preserves complete information in the equivalent transformation, and also achieves full expressivity. Finally, extensive experiments on three benchmarks demonstrate the superior performance of TransEQ in terms of both effectiveness and efficiency. On the largest benchmark WikiPeople, TransEQ significantly improves the state-of-the-art models by 15% on MRR.

1. INTRODUCTION

In the past decade, knowledge graph (KG) has been widely studied in artificial intelligence area (Ji et al., 2021) . By representing facts into a triple of (s, r, o) with subject entity s, object entity o and relation r, KG stores real-world knowledge in a graph structure. However, recent studies find that KG with simple triples provides incomplete information (Galkin et al., 2020; Rosso et al., 2020) . For example, both (Alan Turing, educated at, Cambridge) and (Alan Turing, educated at, Princeton) are true facts in KG, which might be ambiguous when the degree matters. Hence, the hyper-relational KG (HKG) (Galkin et al., 2020; Rosso et al., 2020; Yu & Yang, 2021 ), a.k.a., knowledge hypergraph (Fatemi et al., 2020; 2021) and n-ary knowledge base (Guan et al., 2019; Liu et al., 2021) , is proposed for more generalized knowledge representation. Formally, in HKG, a primary triple is augmented with additional attribute-value qualifiers for rich semantics, called the hyper-relational fact (Guan et al., 2020) . Note that the triple without qualifiers is a special case of hyper-relational facts. Taking Figure 1 as an example, both (Alan Turing, educated at, Cambridge, (degree, Bachelor)) and (Alan Turing, educated at, Princeton, (degree, PhD)) are hyper-relational facts, where (degree, Bachelor) and (degree, PhD) are qualifiers with the degree attribute considered. Such hyper-relational facts are ubiquitous that over 1/3 of the entities in Freebase (Bollacker et al., 2008) involve in them (Wen et al., 2016) . To learn from HKG and further benefit the downstream tasks, HKG modeling learns low-dimensional vector representations (embeddings) of entities and relations (Wang et al., 2021) , which designs a scoring function (SF) based on the embeddings to measure the hyper-relational fact plausibility such

