Zero-shot Logical Query Answering on Any Knowledge Graph

Core Concepts

ULTRAQUERY, a single model that can zero-shot answer complex logical queries on any knowledge graph, even with new entities and relations, by using inductive relation projections and non-parametric logical operators.

Abstract

The paper presents ULTRAQUERY, a novel approach for complex logical query answering (CLQA) on knowledge graphs (KGs) that can generalize to any KG in a zero-shot fashion, even with new entities and relations.

Key highlights:

• Existing CLQA methods are transductive and tailored for specific KGs, unable to generalize to new entities and relations.
• ULTRAQUERY uses an inductive relation projection operator based on ULTRA, which can dynamically build relation representations without relying on a fixed vocabulary.
• ULTRAQUERY implements logical operators (conjunction, disjunction, negation) using non-parametric fuzzy logics, which are also vocabulary-independent.
• The authors curate a novel suite of 11 inductive (e, r) datasets to evaluate zero-shot CLQA performance, where training and inference graphs have completely different entity and relation vocabularies.
• Experiments show that a single ULTRAQUERY model outperforms specialized baselines by 50% (relative MRR) on both EPFO and negation queries, while retaining desirable properties like faithfulness and cardinality estimation.
• The key challenge is the multi-source propagation issue, which the authors address by fine-tuning the pre-trained projection operator on complex queries.

Stats

"The training and inference graphs (and queries) have different entity and relation vocabularies."
"We curate a novel suite of 11 inductive (e, r) datasets to evaluate zero-shot CLQA performance."
"Averaged across the datasets, a single ULTRAQUERY model outperforms by 50% (relative MRR) the best reported baselines in the literature (often tailored to specific graphs) on both EPFO queries and queries with negation."

Quotes

"ULTRAQUERY, an inductive reasoning model that can zero-shot answer logical queries on any KG."
"The core idea of ULTRAQUERY is to derive both projections and logical operations as vocabulary-independent functions which generalize to new entities and relations in any KG."
"ULTRAQUERY in the zero-shot inference mode shows competitive or better query answering performance than best available baselines and sets a new state of the art on 14 of them."

Key Insights Distilled From

Zero-shot Logical Query Reasoning on any Knowledge Graph

by Mikhail Galk... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07198.pdf

Deeper Inquiries

How can ULTRAQUERY be extended to handle more complex query patterns beyond simple trees, such as queries without anchor nodes, hyper-relational queries, or queries with numerical literals?

ULTRAQUERY can be extended to handle more complex query patterns by incorporating additional components and mechanisms tailored to these specific query types. For queries without anchor nodes, the model can be modified to allow for starting the query from any point in the graph, enabling more flexible and diverse query structures. Hyper-relational queries, which involve multiple layers of relationships between entities, can be addressed by enhancing the logical operators to capture and reason over these intricate connections. Introducing specialized modules to handle numerical literals in queries would enable ULTRAQUERY to interpret and process numerical information within the knowledge graph context, expanding its capabilities to handle a wider range of query types.

How can the insights from ULTRAQUERY's inductive relation projections and non-parametric logical operators be applied to other knowledge-intensive tasks beyond CLQA, such as commonsense reasoning or multi-hop question answering?

The insights from ULTRAQUERY's inductive relation projections and non-parametric logical operators can be leveraged in various knowledge-intensive tasks beyond CLQA. For commonsense reasoning, these mechanisms can help in capturing and reasoning over implicit relationships and contextual information present in knowledge graphs, enabling the model to make more informed and contextually relevant decisions. In multi-hop question answering, the inductive relation projections can facilitate traversing multiple layers of relationships to find answers, while the non-parametric logical operators can aid in combining and aggregating information from different sources in a coherent manner. By adapting these insights to different tasks, models can enhance their reasoning capabilities and generalize better to diverse and complex scenarios in the realm of knowledge-intensive applications.