Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases (2303.14617v1)

Abstract: 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.

• The paper presents a neural graph reasoning framework that integrates multi-hop logical query answering with advanced encoding and processing mechanisms.
• It examines varied graph modalities, including hyper-relational graphs, to leverage enriched semantics and address challenges of incomplete data.
• The study highlights the importance of scalable encoders, efficient processors, and adaptable decoders for generalizing to unseen entities while optimizing query performance.

Neural Graph Reasoning: A Deep Dive into Complex Logical Query Answering

Introduction

In the field of graph machine learning, the task of complex logical query answering (CLQA) has emerged as a pivotal challenge, pushing the boundaries of how we solve multifaceted tasks over massive, potentially incomplete, graphs. This task is distinguished from simple link prediction by its focus on multi-hop logical reasoning, which requires the synthesis of relationships across several entities to answer complex queries. The essence of CLQA lies in its ability to navigate through the latent spaces of huge graphs, uncovering information that is not directly observable through single-hop relations.

Graph Considerations

Modality

Graphs, the foundation upon which CLQA is built, can differ significantly in their structure. From standard triple-based Knowledge Graphs (KGs) to more intricate hyper-relational and hypergraph KGs, each modality presents unique challenges and opportunities for query answering. A notable advancement is the consideration of hyper-relational graphs, which incorporate richer semantic relationships through entity-relation qualifiers on edges. Nevertheless, the exploration of hypergraph KGs and the integration of multimodal data within graph structure remains largely untapped, presenting an exciting avenue for future research.

Reasoning Domain

The reasoning domain of a graph determines the scope of queries it can support. While current methods excel in discrete domains, the capacity to reason over temporal and continuous data remains underexplored. Expanding the reasoning domain to include such data types is crucial for answering a wider array of real-world queries, particularly those involving temporal dynamics or quantitative attributes.

Background Semantics

The presence of background semantics, such as class hierarchies and complex axioms within a graph, enriches the potential for logical reasoning. By incorporating higher-order relationships and formal semantics, query answering systems can leverage a deeper understanding of entity roles and relationships. Current efforts have begun to scratch the surface of this potential, yet fully realizing the power of complex axioms in reasoning remains a significant challenge.

Modeling Details

Encoders

The development of encoders capable of generating inductive representations is pivotal for generalizing to unseen entities and relations. This advancement not only facilitates query answering over evolving graphs but also aligns with the pretrain-finetune paradigm, enhancing model adaptability to diverse graphs with custom relational schemas.

Processors

Achieving an expressive query processor network is vital for executing a broader range of logical operators, akin to those available in declarative graph query languages. Enhancing the processor's sample efficiency could substantially improve training times without sacrificing predictive performance.

Decoders

Extending the decoder's functionality to support continuous outputs would mark a significant leap forward, enabling the system to address queries that go beyond discrete entity retrieval and encompass numerical predictions.

Datasets and Evaluation Protocols

The creation of larger, more diverse benchmarks is imperative for evaluating query answering models across a broader spectrum of graph modalities, query semantics, and operators. Furthermore, developing a more comprehensive evaluation framework will ensure a holistic assessment of model performance, covering various aspects of the query answering workflow.

Conclusion

The quest for advanced Neural Graph Databases and Neural Query Engines to handle complex logical query answering represents an exciting frontier in graph machine learning. By addressing the outlined challenges, future advancements can unlock the full potential of neural reasoning over graphs, paving the way for novel applications and deeper insights into the intricate web of relationships that characterize complex data landscapes.