LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

TL;DR

LitCQD is a novel method that enables answering complex multi-hop queries involving numeric literals in incomplete knowledge graphs, addressing a gap in existing approaches that ignore literal values.

Contribution

It introduces LitCQD, the first approach to handle multi-hop queries with numeric literals in knowledge graphs, enhancing query answering capabilities.

Findings

LitCQD effectively answers queries with numeric constraints.

It outperforms existing methods on extended FB15k-237 dataset.

The approach handles both entity and literal-based queries.

Abstract

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, all state-of-the-art approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD -- an approach to answer complex, multi-hop queries where both the query and the knowledge graph can contain numeric literal values: LitCQD can answer queries having numerical answers or having entity answers satisfying numerical constraints. For example, it allows to query (1)~persons living in New York having a certain age, and (2)~the average age of persons living in New York. We evaluate LitCQD on query types with and without literal values. To evaluate LitCQD, we generate complex, multi-hop queries and their expected answers on a version of the FB15k-237 dataset that was extended by literal values.