Stanford & Intel AI Lab | 神经图推理: 复杂逻辑查询回答与图数据库的结合

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作者：H Ren, M Galkin, M Cochez, Z Zhu, J Leskovec
[Stanford University & Intel AI Lab]

http://hyren.me/

总结:
提供了一个综合性的调查报告，介绍了复杂逻辑查询回答(CLQA)领域的最新进展和发展趋势，以及引入了神经图数据库(NGDB)的概念，提供了一种新方法用于处理这种任务。

要点:

• 动机：CLQA是一种新兴任务，需要进行多跳逻辑推理，但目前缺乏综合性的研究报告，同时传统的图数据库也存在着一些限制，因此需要一种新的方法来解决这个问题。
• 方法：提供了一个综合性的调查报告，介绍了CLQA领域的最新进展和发展趋势，同时提出了神经图数据库(NGDB)的概念，该方法包括了神经图存储和神经查询引擎两个部分，可以用于处理CLQA任务。
• 优势：所提出的神经图数据库(NGDB)方法可以处理多跳逻辑推理，提供了一种新的方法来解决这个问题。

https://arxiv.org/abs/2303.14617

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.

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