Query Embedding on Hyper-relational Knowledge Graphs
- URL: http://arxiv.org/abs/2106.08166v2
- Date: Thu, 17 Jun 2021 13:53:13 GMT
- Title: Query Embedding on Hyper-relational Knowledge Graphs
- Authors: Dimitrios Alivanistos and Max Berrendorf and Michael Cochez and
Mikhail Galkin
- Abstract summary: Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs.
We extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries.
- Score: 0.4779196219827507
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-hop logical reasoning is an established problem in the field of
representation learning on knowledge graphs (KGs). It subsumes both one-hop
link prediction as well as other more complex types of logical queries.
Existing algorithms operate only on classical, triple-based graphs, whereas
modern KGs often employ a hyper-relational modeling paradigm. In this paradigm,
typed edges may have several key-value pairs known as qualifiers that provide
fine-grained context for facts. In queries, this context modifies the meaning
of relations, and usually reduces the answer set. Hyper-relational queries are
often observed in real-world KG applications, and existing approaches for
approximate query answering cannot make use of qualifier pairs. In this work,
we bridge this gap and extend the multi-hop reasoning problem to
hyper-relational KGs allowing to tackle this new type of complex queries.
Building upon recent advancements in Graph Neural Networks and query embedding
techniques, we study how to embed and answer hyper-relational conjunctive
queries. Besides that, we propose a method to answer such queries and
demonstrate in our experiments that qualifiers improve query answering on a
diverse set of query patterns.
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