10.04.2022

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One paper at ICLR 2022

10th International Conference on Learning Representations (ICLR 2022). Virtual, 25.04.2022–29.04.2022

We are happy to announce that MCML researchers are represented with one paper at ICLR 2022:

D. Alivanistos, M. Berrendorf, M. Cochez and M. Galkin.
Query Embedding on Hyper-Relational Knowledge Graphs.
ICLR 2022 - 10th International Conference on Learning Representations. Virtual, Apr 25-29, 2022. URL GitHub
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.

MCML Authors
Link to website

Max Berrendorf

Dr.

* Former member


10.04.2022


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