Abduction for Learning Smart City Rules

6 pages•Published: October 19, 2017

Nikolaj Bjorner, Maria-Cristina Marinescu and Mooly Sagiv

Abstract

We propose using abduction for inferring implicit rules for Smart City ontologies.
We show how we can use Z3 to extract candidate abducers from partial ontologies and leverage them in an iterative process of evolving an ontology by refining relations and restrictions, and populating relations.

Our starting point is a Smart City initiative of the city of Barcelona, where a substantial ontology is being developed to support processes such as city planning, social services, or improving the quality of the data concerning (for instance) legal entities, whose incompleteness may sometimes hide fraudulent behavior. In our scenario we are supporting semantic queries over heterogeneous and noisy data. The approach we develop would allow evolving ontologies in an iterative fashion as new relations and restrictions are discovered.

Keyphrases: ontologies, smart cities, smt

In: Christoph Benzmüller, Christine Lisetti and Martin Theobald (editors). GCAI 2017. 3rd Global Conference on Artificial Intelligence, vol 50, pages 233-238.

Links:	https://easychair.org/publications/paper/8CWl
	https://doi.org/10.29007/8jfk

BibTeX entry

@inproceedings{GCAI2017:Abduction_Learning_Smart_City,
  author    = {Nikolaj Bjorner and Maria-Cristina Marinescu and Mooly Sagiv},
  title     = {Abduction for Learning Smart City Rules},
  booktitle = {GCAI 2017. 3rd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/8CWl},
  doi       = {10.29007/8jfk},
  pages     = {233-238},
  year      = {2017}}

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