TY - GEN
T1 - On Combining Ontologies and Rules
AU - Knorr, Matthias
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FCCI-COM%2F30952%2F2017/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT#
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Ontology languages, based on Description Logics, and nonmonotonic rule languages are two major formalisms for the representation of expressive knowledge and reasoning with it, that build on fundamentally different ideas and formal underpinnings. Within the Semantic Web initiative, driven by the World Wide Web Consortium, standardized languages for these formalisms have been developed that allow their usage in knowledge-intensive applications integrating increasing amounts of data on the Web. Often, such applications require the advantages of both these formalisms, but due to their inherent differences, the integration is a challenging task. In this course, we review the two formalisms and their characteristics and show different ways of achieving their integration. We also discuss an available tool based on one such integration with favorable properties, such as polynomial data complexity for query answering when standard inference is polynomial in the used ontology language.
AB - Ontology languages, based on Description Logics, and nonmonotonic rule languages are two major formalisms for the representation of expressive knowledge and reasoning with it, that build on fundamentally different ideas and formal underpinnings. Within the Semantic Web initiative, driven by the World Wide Web Consortium, standardized languages for these formalisms have been developed that allow their usage in knowledge-intensive applications integrating increasing amounts of data on the Web. Often, such applications require the advantages of both these formalisms, but due to their inherent differences, the integration is a challenging task. In this course, we review the two formalisms and their characteristics and show different ways of achieving their integration. We also discuss an available tool based on one such integration with favorable properties, such as polynomial data complexity for query answering when standard inference is polynomial in the used ontology language.
UR - http://www.scopus.com/inward/record.url?scp=85125593133&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-95481-9_2
DO - 10.1007/978-3-030-95481-9_2
M3 - Conference contribution
AN - SCOPUS:85125593133
SN - 978-3-030-95480-2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 58
BT - Reasoning Web. Declarative Artificial Intelligence
A2 - Šimkus, Mantas
A2 - Varzinczak, Ivan
PB - Springer
CY - Cham
T2 - 17th Reasoning Web International Summer School, Reasoning Web 2021
Y2 - 8 September 2021 through 15 September 2021
ER -