One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs

Krzysztof Olejniczak, Xingyue Huang, Mikhail Galkin, Ismail Ilkan Ceylan

Keywords: Complex Query Answering, Graph Neural Network

TL;DR: We propose ANYCQ, a model that can classify answers to any conjunctive query on any knowledge graph

Abstract:

Motivated by the incompleteness of modern knowledge graphs, a new setup for query answering has emerged, where the goal is to predict answers that do not necessarily appear in the knowledge graph, but are present in its *completion*. In this paper, we formally introduce and study two query answering problems, namely, *query answer classification* and *query answer retrieval*.
To solve these problems, we propose ANYCQ, a model that can classify answers to *any* conjunctive query on *any* knowledge graph.
At the core of our framework lies a graph neural network trained using a reinforcement learning objective to answer Boolean queries.
Trained only on simple, small instances, ANYCQ generalizes to *large queries* of *arbitrary* structure, reliably classifying and retrieving answers to queries that existing approaches fail to handle.
This is empirically validated through our newly proposed, challenging benchmarks.
Finally, we empirically show that ANYCQ can effectively transfer to *completely novel* knowledge graphs when equipped with an appropriate link prediction model, highlighting its potential for querying incomplete data.

Submission Type: Full paper proceedings track submission (max 9 main pages).

Software: https://github.com/kolejnyy/ANYCQ/

Submission Number: 14