Expressive stream reasoning with laser

Hamid R. Bazoobandi, Harald Beck*, Jacopo Urbani

*Corresponding author for this work

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

Abstract

An increasing number of use cases require a timely extraction of non-trivial knowledge from semantically annotated data streams, especially on the Web and for the Internet of Things (IoT). Often, this extraction requires expressive reasoning, which is challenging to compute on large streams. We propose Laser, a new reasoner that supports a pragmatic, non-trivial fragment of the logic LARS which extends Answer Set Programming (ASP) for streams. At its core, Laser implements a novel evaluation procedure which annotates formulae to avoid the re-computation of duplicates at multiple time points. This procedure, combined with a judicious implementation of the LARS operators, is responsible for significantly better runtimes than the ones of other state-of-the-art systems like C-SPARQL and CQELS, or an implementation of LARS which runs on the ASP solver Clingo. This enables the application of expressive logic-based reasoning to large streams and opens the door to a wider range of stream reasoning use cases.

Original languageEnglish
Title of host publicationThe Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings
PublisherSpringer/Verlag
Pages87-103
Number of pages17
Volume10587 LNCS
ISBN (Print)9783319682877
DOIs
Publication statusPublished - 1 Jan 2017
Event16th International Semantic Web Conference, ISWC 2017 - Vienna, Austria
Duration: 21 Oct 201725 Oct 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Semantic Web Conference, ISWC 2017
CountryAustria
CityVienna
Period21/10/1725/10/17

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