LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
This addresses a gap in knowledge graph querying for real-world applications like Wikidata, where numeric data is common but previously neglected, though it is an incremental extension of existing methods.
The authors tackled the problem of answering complex, multi-hop queries on incomplete knowledge graphs that include numeric literal values, proposing LitCQD to handle queries with numerical answers or constraints, and evaluated it on an extended FB15k-237 dataset.
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.