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.

翻译：大多数真实世界的知识图谱（包括Wikidata、DBpedia和Yago）都是不完整的。在这些不完整图谱上回答查询是一个重要但具有挑战性的问题。近年来，包括复杂查询分解（CQD）在内的多种方法被提出，用于在这些图谱上回答包含合取和析取操作的复杂多跳查询。然而，所有现有先进方法都仅考虑由实体和关系构成的图谱，忽略了字面量值。本文提出LitCQD——一种能够回答复杂多跳查询的方法，其中查询和知识图谱均可包含数值字面量：LitCQD可回答具有数值答案或需满足数值约束的实体答案的查询。例如，它允许查询(1)居住于纽约且年龄特定的个体，以及(2)纽约居民的平均年龄。我们在包含和不包含字面量值的查询类型上评估了LitCQD。为评估LitCQD，我们在扩展了字面量值的FB15k-237数据集版本上生成了复杂多跳查询及其预期答案。