Logic on MARS: Ontologies for generalised property graphs

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Logic on MARS: Ontologies for generalised property graphs

Maximilian MarxMaximilian Marx,  Markus KrötzschMarkus Krötzsch,  Veronika ThostVeronika Thost
Logic on MARS: Ontologies for generalised property graphs


Slides: Logic on MARS: Ontologies for generalised property graphs

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, 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: CfaedDIAMONDHAEC B08
  • Forschungsgruppe:Research Group: Wissensbasierte Systeme
@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},
  pages     = {1188-1194},
  doi       = {10.24963/ijcai.2017/165}
}