Logic on MARS: Ontologies for generalised property graphs
From International Center for Computational Logic
Logic on MARS: Ontologies for generalised property graphs
Maximilian MarxMaximilian Marx, Markus KrötzschMarkus Krötzsch, Veronika ThostVeronika Thost
Maximilian Marx, Markus Krötzsch, Veronika Thost
Logic on MARS: Ontologies for generalised property graphs
In Carles Sierra, eds., Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 1188-1194, August 2017. International Joint Conferences on Artificial Intelligence
Logic on MARS: Ontologies for generalised property graphs
In Carles Sierra, eds., Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 1188-1194, August 2017. International Joint Conferences on Artificial Intelligence
- KurzfassungAbstract
Graph-structured data is used to represent large information collections, called knowledge graphs, in many applications. Their exact format may vary, but they often share the concept that edges can be annotated with additional information, such as validity time or provenance information. Property Graph is a popular graph database format that also provides this feature. We give a formalisation of a generalised notion of Property Graphs, called multi-attributed relational structures (MARS), and introduce a matching knowledge representation formalism, multi-attributed predicate logic (MAPL). We analyse the expressive power of MAPL and suggest a simpler, rule-based fragment of MAPL that can be used for ontological reasoning on Property Graphs. To the best of our knowledge, this is the first approach to making Property Graphs and related data structures accessible to symbolic AI. - Projekt:Project: DIAMOND, HAEC B08, Cfaed
- Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems
@inproceedings{MKT2017,
author = {Maximilian Marx and Markus Kr{\"{o}}tzsch and Veronika Thost},
title = {Logic on {MARS:} Ontologies for generalised property graphs},
editor = {Carles Sierra},
booktitle = {Proceedings of the 26th International Joint Conference on
Artificial Intelligence (IJCAI'17)},
publisher = {International Joint Conferences on Artificial Intelligence},
year = {2017},
month = {August},
pages = {1188-1194},
doi = {10.24963/ijcai.2017/165}
}