Multi-hop logical reasoning on knowledge graphs is a pivotal task in natural
language processing, with numerous approaches aiming to answer First-Order
Logic (FOL) queries. Recent geometry (e.g., box, cone) and probability (e.g.,
beta distribution)-based methodologies have effectively addressed complex FOL
queries. However, a common challenge across these methods lies in determining
accurate geometric bounds or probability parameters for these queries. The
challenge arises because existing methods rely on linear sequential operations
within their computation graphs, overlooking the logical structure of the query
and the relation-induced information that can be gleaned from the relations of
the query, which we call the context of the query. To address the problem, we
propose a model-agnostic methodology that enhances the effectiveness of
existing multi-hop logical reasoning approaches by fully integrating the
context of the FOL query graph. Our approach distinctively discerns (1) the
structural context inherent to the query structure and (2) the relation-induced
context unique to each node in the query graph as delineated in the
corresponding knowledge graph. This dual-context paradigm helps nodes within a
query graph attain refined internal representations throughout the multi-hop
reasoning steps. Through experiments on two datasets, our method consistently
enhances the three multi-hop reasoning foundation models, achieving performance
improvements of up to 19.5%. Our code is available at
this https URL

通过全面整合 FOL 查询图的上下文，我们提出了一种模型不可知的方法，增强了现有的多跳逻辑推理方法，在两个数据集上的实验中，我们的方法始终提升了三个多跳推理基准模型的性能，最高可达 19.5%。

提升知识图谱中多跳逻辑推理的上下文感知查询表示学习

Improving Multi-hop Logical Reasoning in Knowledge Graphs with  Context-Aware Query Representation Learning

Complex logical query answering (CLQA) is a recently emerged task of graph
machine learning that goes beyond simple one-hop link prediction and solves a
far more complex task of multi-hop logical reasoning over massive, potentially
incomplete graphs in a latent space. The task received a significant traction
in the community; numerous works expanded the field along theoretical and
practical axes to tackle different types of complex queries and graph
modalities with efficient systems. In this paper, we provide a holistic survey
of CLQA with a detailed taxonomy studying the field from multiple angles,
including graph types (modality, reasoning domain, background semantics),
modeling aspects (encoder, processor, decoder), supported queries (operators,
patterns, projected variables), datasets, evaluation metrics, and applications.
Refining the CLQA task, we introduce the concept of Neural Graph Databases
(NGDBs). Extending the idea of graph databases (graph DBs), NGDB consists of a
Neural Graph Storage and a Neural Graph Engine. Inside Neural Graph Storage, we
design a graph store, a feature store, and further embed information in a
latent embedding store using an encoder. Given a query, Neural Query Engine
learns how to perform query planning and execution in order to efficiently
retrieve the correct results by interacting with the Neural Graph Storage.
Compared with traditional graph DBs, NGDBs allow for a flexible and unified
modeling of features in diverse modalities using the embedding store. Moreover,
when the graph is incomplete, they can provide robust retrieval of answers
which a normal graph DB cannot recover. Finally, we point out promising
directions, unsolved problems and applications of NGDB for future research.

本文综述了复杂逻辑查询应答（CLQA）的最新技术，提出了神经图数据库（NGDB）的概念，介绍了其与传统图数据库的不同之处，并探讨了其未来的研究方向。

神经图推理：复杂的逻辑查询与图数据库相遇

Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases

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.

本文介绍了如何利用 Graph Neural Networks 来嵌入和回答超关系连接查询，以处理新型复杂查询，同时提出了一种提高查询效率的方法，并在实验证明 Qualifier 可以在多种查询模式下提高查询效果。