TY - GEN
T1 - Towards Provenance in Heterogeneous Knowledge Bases
AU - Knorr, Matthias
AU - Damásio, Carlos Viegas
AU - Gonçalves, Ricardo
AU - Leite, João
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FCCI-COM%2F30952%2F2017/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F32219%2F2017/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT#
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - A rapidly increasing amount of data, information and knowledge is becoming available on the Web, often written in different formats and languages, adhering to standardizations driven by the World Wide Web Consortium initiative. Taking advantage of all this heterogeneous knowledge requires its integration for more sophisticated reasoning services and applications. To fully leverage the potential of such systems, their inferences should be accompanied by justifications that allow a user to understand a proposed decision/recommendation, in particular for critical systems (healthcare, law, finances, etc.). However, determining such justifications has commonly only been considered for a single formalism, such as relational databases, description logic ontologies, or declarative rule languages. In this paper, we present the first approach for providing provenance for heterogeneous knowledge bases building on the general framework of multi-context systems, as an abstract, but very expressive formalism to represent knowledge bases written in different formalisms and the flow of information between them. We also show under which conditions and how provenance information in this formalism can be computed.
AB - A rapidly increasing amount of data, information and knowledge is becoming available on the Web, often written in different formats and languages, adhering to standardizations driven by the World Wide Web Consortium initiative. Taking advantage of all this heterogeneous knowledge requires its integration for more sophisticated reasoning services and applications. To fully leverage the potential of such systems, their inferences should be accompanied by justifications that allow a user to understand a proposed decision/recommendation, in particular for critical systems (healthcare, law, finances, etc.). However, determining such justifications has commonly only been considered for a single formalism, such as relational databases, description logic ontologies, or declarative rule languages. In this paper, we present the first approach for providing provenance for heterogeneous knowledge bases building on the general framework of multi-context systems, as an abstract, but very expressive formalism to represent knowledge bases written in different formalisms and the flow of information between them. We also show under which conditions and how provenance information in this formalism can be computed.
KW - Heterogeneous knowledge bases
KW - Multi-context systems
KW - Provenance
UR - http://www.scopus.com/inward/record.url?scp=85138012326&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-15707-3_22
DO - 10.1007/978-3-031-15707-3_22
M3 - Conference contribution
AN - SCOPUS:85138012326
SN - 978-3-031-15706-6
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 287
EP - 300
BT - Logic Programming and Nonmonotonic Reasoning
A2 - Gottlob, Georg
A2 - Inclezan, Daniela
A2 - Maratea, Marco
PB - Springer
CY - Cham
T2 - 16th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2022
Y2 - 5 September 2022 through 9 September 2022
ER -