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Complex logical query answering (CLQA) is a recently emerged task of graphmachine learning that goes beyond simple one-hop link prediction and solves afar more complex task of multi-hop logical reasoning over massive, potentiallyincomplete graphs in a latent space. The task received a significant tractionin the community; numerous works expanded the field along theoretical andpractical axes to tackle different types of complex queries and graphmodalities with efficient systems. In this paper, we provide a holistic surveyof 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 aNeural Graph Storage and a Neural Graph Engine. Inside Neural Graph Storage, wedesign a graph store, a feature store, and further embed information in alatent embedding store using an encoder. Given a query, Neural Query Enginelearns how to perform query planning and execution in order to efficientlyretrieve the correct results by interacting with the Neural Graph Storage.Compared with traditional graph DBs, NGDBs allow for a flexible and unifiedmodeling of features in diverse modalities using the embedding store. Moreover,when the graph is incomplete, they can provide robust retrieval of answerswhich a normal graph DB cannot recover. Finally, we point out promisingdirections, unsolved problems and applications of NGDB for future research.

Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases