LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals
Demir, Caglar, Wiebesiek, Michel, Lu, Renzhong, Ngomo, Axel-Cyrille Ngonga, Heindorf, Stefan
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Apr-28-2023
- Country:
- North America > United States > New York (0.44)
- Genre:
- Research Report > Promising Solution (0.34)
- Overview > Innovation (0.34)
- Technology: