Rewrite or not rewrite ml- ased algorithm selection for datalog query answering on knowledge graphs

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Query-driven reasoning techniques with Datalog rules, like Magic Sets (MS), are ideal for implementing query answering on Knowledge Graphs (KGs). For some queries, executing a rewriting procedure like MS is the best choice, but for others a non-rewriting procedure like Query-subquery (QSQ) can be faster. Choosing beforehand which procedure should be used is not trivial and mistakes can be costly. To address this problem, we describe a first-of-its-kind method that builds a Machine Learning (ML) model to predict whether a query should be answered with MS or with QSQ. Experiments on several well-known KGs show that our method can return accurate predictions, and this leads to a significant reduction of the response time of query answering.

Original languageEnglish
Title of host publicationECAI 2020
Subtitle of host publication24th European Conference on Artificial Intelligence, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Proceedings
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
PublisherIOS Press BV
Pages792-799
Number of pages8
ISBN (Electronic)9781643681016
ISBN (Print)9781643681009
DOIs
Publication statusPublished - 24 Aug 2020
Event24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020 - Santiago de Compostela, Online, Spain
Duration: 29 Aug 20208 Sep 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume325
ISSN (Print)0922-6389

Conference

Conference24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
CountrySpain
CitySantiago de Compostela, Online
Period29/08/208/09/20

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