Handling Impossible Derivations During Stream Reasoning

Hamid R. Bazoobandi, Henri Bal, Frank van Harmelen, Jacopo Urbani*

*Corresponding author for this work

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

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With the rapid expansion of the Web and the advent of the Internet of Things, there is a growing need to design tools for intelligent analytics and decision making on streams of data. Logic-based frameworks like LARS allow the execution of complex reasoning on such streams, but it is paramount that the computation is completed in a timely manner before the stream expires. To reduce the runtime, we can extend the validity of inferred conclusions to the future to avoid repeated derivations, but this is not enough to avoid all sources of redundant computation. To further alleviate this problem, this paper introduces a new technique that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway. An experimental analysis on microbenchmarks shows that our technique leads to a significant reduction of the reasoning runtime.

Original languageEnglish
Title of host publicationThe Semantic Web
Subtitle of host publication17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4, 2020, Proceedings
EditorsAndreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, Michael Cochez
Number of pages17
ISBN (Electronic)9783030494612
ISBN (Print)9783030494605
Publication statusPublished - 2020
Event17th Extended Semantic Web Conference, ESWC 2020 - Heraklion, Greece
Duration: 31 May 20204 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12123 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th Extended Semantic Web Conference, ESWC 2020


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