Post #288

Message Passing for Hyper-Relational Knowledge Graphs This is a guest post by Michael Galkin about their recently accepted paper at EMNLP. Traditionally, knowledge graphs (KGs) use triples to encode their facts, eg subject, predicate, object Albert Einstein, educated at, ETH Zurich Simple and straighforward, triple-based KG are extensively used in a plethora of NLP and CV tasks. But can triples effectively encode richer facts when we need them? If we have the two facts: Albert Einstein, educated at, ETH Zurich Albert Einstein, educated at, University of Zurich what can we say about Einstein's education? Did he attend two universities at the same time? 🤨 It is a common problem of triple-based KGs when we want to assign more attributes to each typed edge. Luckily, the KG community has two good ways to do that: with RDF* and Labeled Property Graphs (LPGs). With RDF* we could instantiate each fact with qualifiers: ( Albert_Einstein educated_at ETH_Zurich ) academic_degree Bachelor ; academic_major Maths . ( Albert_Einstein educated_at University_of_Zurich ) academic_degree Doctorate ; academic_major Physics. We call such KGs as hyper-relational KGs. Wikidata follows the same model, here is Einstein's page where you'd find statements (hyper-relational facts) with qualifiers (those additional key-value edge attributes). Interestingly, there is pretty much nothing 🕳 in the Graph ML field for hyper-relational graphs. We have a bunch of GNN encoders for directed, multi-relational, triple-based KGs (like R-GCN or CompGCN), and nothing for hyper-relational ones. In our new paper, we design StarE ⭐️, a GNN encoder for hyper-relational KGs (like RDF* or LPG) where each edge might have unlimited amount of qualifier pairs (relation, entity). Moreover, those entities and relations do not need to be qualifier-specific, they can be used in the main triples as well! In addition, we carefully constructed WD50K, a new Wikidata-based dataset for link predicion on hyper-relational KGs, and its 3 decendants for various setups. Experiments show that qualifiers greatly improve subject/object prediction accuracy, sometimes reaching a whopping 25 MRR points gap. More applications and tasks are to appear in the future work! Paper: https://arxiv.org/abs/2009.10847 Blog: Medium friends link Code: Github