Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases

• URL: http://arxiv.org/abs/2303.14617v1
• Date: Sun, 26 Mar 2023 04:03:37 GMT
• Title: Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
• Authors: Hongyu Ren, Mikhail Galkin, Michael Cochez, Zhaocheng Zhu, Jure Leskovec
• Abstract summary: Complex logical query answering (CLQA) is a recently emerged task of graph machine learning. We introduce the concept of Neural Graph Database (NGDBs) NGDB consists of a Neural Graph Storage and a Neural Graph Engine.
• Score: 63.96793270418793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs [17.93455358818447]
  Most neuro-symbolic query processors are constrained to tree-like graph pattern queries. We introduce a framework for answering arbitrary graph pattern queries over incomplete knowledge graphs.
  arXiv  Detail & Related papers  (2023-10-06T21:31:17Z)
• Learning to Count Isomorphisms with Graph Neural Networks [16.455234748896157]
  Subgraph isomorphism counting is an important problem on graphs. In this paper, we propose a novel graph neural network (GNN) called Count-GNN for subgraph isomorphism counting.
  arXiv  Detail & Related papers  (2023-02-07T05:32:11Z)
• Neural Methods for Logical Reasoning Over Knowledge Graphs [14.941769519278745]
  We focus on answering multi-hop logical queries on Knowledge Graphs (KGs) Most previous works have been unable to create models that accept full First-Order Logical (FOL) queries. We introduce a set of models that use Neural Networks to create one-point vector embeddings to answer the queries.
  arXiv  Detail & Related papers  (2022-09-28T23:10:09Z)
• An Empirical Study of Retrieval-enhanced Graph Neural Networks [48.99347386689936]
  Graph Neural Networks (GNNs) are effective tools for graph representation learning. We propose a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs.
  arXiv  Detail & Related papers  (2022-06-01T09:59:09Z)
• Automatic Relation-aware Graph Network Proliferation [182.30735195376792]
  We propose Automatic Relation-aware Graph Network Proliferation (ARGNP) for efficiently searching GNNs. These operations can extract hierarchical node/relational information and provide anisotropic guidance for message passing on a graph. Experiments on six datasets for four graph learning tasks demonstrate that GNNs produced by our method are superior to the current state-of-the-art hand-crafted and search-based GNNs.
  arXiv  Detail & Related papers  (2022-05-31T10:38:04Z)
• Neural-Symbolic Models for Logical Queries on Knowledge Graphs [17.290758383645567]
  We propose Graph Neural Network Query Executor (GNN-QE), a neural-symbolic model that enjoys the advantages of both worlds. GNN-QE decomposes a complex FOL query into relation projections and logical operations over fuzzy sets. Experiments on 3 datasets show that GNN-QE significantly improves over previous state-of-the-art models in answering FOL queries.
  arXiv  Detail & Related papers  (2022-05-16T18:39:04Z)
• Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search [96.31315520244605]
  Arch-Graph is a transferable NAS method that predicts task-specific optimal architectures. We show Arch-Graph's transferability and high sample efficiency across numerous tasks. It is able to find top 0.16% and 0.29% architectures on average on two search spaces under the budget of only 50 models.
  arXiv  Detail & Related papers  (2022-04-12T16:46:06Z)
• Interactive Visual Pattern Search on Graph Data via Graph Representation Learning [20.795511688640296]
  We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation. We also propose a novel GNN for node-alignment called NeuroAlign to facilitate easy validation and interpretation of the query results.
  arXiv  Detail & Related papers  (2022-02-18T22:30:28Z)
• Edge-featured Graph Neural Architecture Search [131.4361207769865]
  We propose Edge-featured Graph Neural Architecture Search to find the optimal GNN architecture. Specifically, we design rich entity and edge updating operations to learn high-order representations. We show EGNAS can search better GNNs with higher performance than current state-of-the-art human-designed and searched-based GNNs.
  arXiv  Detail & Related papers  (2021-09-03T07:53:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.