DAGE: DAG Query Answering via Relational Combinator with Logical Constraints

Yunjie He, Bo Xiong, Daniel Hernández, Yuqicheng Zhu, Evgeny Kharlamov, Steffen Staab

Track: Semantics and knowledge

Keywords: Description Logics, Knowledge Graph

TL;DR: DAG query answering on incomplete knowledge graphs

Abstract: Predicting answers to queries over knowledge graphs is called a complex reasoning task because answering a query requires subdividing it into subqueries. Existing query embedding methods use this decomposition to compute the embedding of a query as the combination of the embedding of the subqueries. This requirement limits the answerable queries to queries having a single free variable and being decomposable, which are called tree-form queries and correspond to the $SROI^-$ description logic. In this paper, we define a more general set of queries, called DAG queries, formulate a description logic corresponding to them, called DAG-DL, propose a query embedding method for them, called DAGE, and a new benchmark to evaluate query embeddings on them. Given the computational graph of a DAG query, DAGE combines the possibly multiple paths between two nodes into a single path with a trainable operator that represents the intersection of relations and learns DAG-DL tautologies. We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the outstanding improvement of our method over the results of vanilla methods evaluated in tree-form queries that result in relaxing the DAG queries of our proposed benchmark.

Submission Number: 1951