A Rule-based Framework for Creating Instance Data from OpenStreetMap

Thomas Eiter, Jeff Z. Pan, Patrik Schneider, Mantas Šimkus, Guohui Xiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Reasoning engines for ontological and rule-based knowledge bases are becoming increasingly important in areas like the Semantic Web or information integration. It has been acknowledged however that judging the performance of such reasoners and their underlying algorithms is difficult due to the lack of publicly available datasets with large amounts of (real-life) instance data. In this paper we describe a framework and a toolbox for creating such datasets, which is based on extracting instances from the publicly available OpenStreetMap (OSM) geospatial database. To this end, we give a formalization of OSM and present a rule-based language to specify the rules to extract instance data from OSM data. The declarative nature of the approach in combination with external functions and parameters allows one to create several variants of the dataset via small modifications of the specification. We describe a highly flexible toolbox to extract instance data from a given OSM map and a given set of rules. We have employed our tools to create benchmarks that have already been fruitfully used in practice.
Original languageEnglish
Title of host publicationWeb Reasoning and Rule Systems
EditorsBalder ten Cate, Alessandra Mileo
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages12
ISBN (Electronic)978-3-319-22002-4
ISBN (Print)978-3-319-22001-7
Publication statusPublished - 22 Jul 2015
EventThe 9th International Conference on Web Reasoning and Rule Systems - Berlin, Germany
Duration: 4 May 20155 May 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 9th International Conference on Web Reasoning and Rule Systems
Abbreviated titleRR 2015
Internet address


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