# Query Embedding on Hyper-relational Knowledge Graphs arXiv: https://arxiv.org/abs/2106.08166 ยท HTML: https://mlsanity.com/paper/2106.08166/ ## Key facts - Cited by 23 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 33 references that are in the map. - First posted on arXiv 15 Jun 2021. ## Builds on (most cited first, 16 of 16) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017) - [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020) - [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020) - [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019) - [Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings](https://mlsanity.com/paper/2002.05969/) (2020) - [Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs](https://mlsanity.com/paper/2010.11465/) (2020) - [Embedding Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/1806.01445/) (2018) - [Complex Query Answering with Neural Link Predictors](https://mlsanity.com/paper/2011.03459/) (2020) - [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020) - [Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework](https://mlsanity.com/paper/2006.13365/) (2020) - [Answering Complex Queries in Knowledge Graphs with Bidirectional Sequence Encoders](https://mlsanity.com/paper/2004.02596/) (2020) - [End-to-End Entity Classification on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2003.12383/) (2020) ## Built on by (most cited first, 23 of 23) - [Weisfeiler and Leman Go Relational](https://mlsanity.com/paper/2211.17113/) (2022) - [Inductive Logical Query Answering in Knowledge Graphs](https://mlsanity.com/paper/2210.08008/) (2022) - [GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs](https://mlsanity.com/paper/2210.15578/) (2022) - [Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport](https://mlsanity.com/paper/2305.04034/) (2023) - [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023) - [A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2203.07544/) (2022) - [A Foundation Model for Zero-shot Logical Query Reasoning](https://mlsanity.com/paper/2404.07198/) (2024) - [EFOk-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation](https://mlsanity.com/paper/2307.13701/) (2023) - [NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2211.13469/) (2022) - [Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs](https://mlsanity.com/paper/2410.20321/) (2024) - [Conditional Logical Message Passing Transformer for Complex Query Answering](https://mlsanity.com/paper/2402.12954/) (2024) - [A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs](https://mlsanity.com/paper/2310.04598/) (2023) - [Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs](https://mlsanity.com/paper/2402.14609/) (2024) - [Cardinality Estimation on Hyper-relational Knowledge Graphs](https://mlsanity.com/paper/2405.15231/) (2024) - [RConE: Rough Cone Embedding for Multi-Hop Logical Query Answering on Multi-Modal Knowledge Graphs](https://mlsanity.com/paper/2408.11526/) (2024) - [Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling](https://mlsanity.com/paper/2411.06191/) (2024) - [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025) - [Approximate Answering of Graph Queries](https://mlsanity.com/paper/2308.06585/) (2023) - [Counting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation](https://mlsanity.com/paper/2511.22565/) (2025) - [HEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edges](https://mlsanity.com/paper/2602.18897/) (2026) - [AlertStar: Path-Aware Alert Prediction on Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2604.03104/) (2026) - [InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs](https://mlsanity.com/paper/2607.07422/) (2026) - [Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability](https://mlsanity.com/paper/2607.18368/) (2026) ## Abstract (arXiv) 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.