On Knowledge Dependence in Weighted Description Logic

13 pages•Published: December 10, 2019

Pietro Galliani, Oliver Kutz, Daniele Porello, Guendalina Righetti and Nicolas Troquard

Abstract

We study a family of operators (called ‘Tooth’ operators) that combine Description Logic concepts via weighted sums. These operators are intended to capture the notion of instances satisfy- ing “enough” of the concept descriptions given. We examine two variants of these operators: the “knowledge-independent” one, that evaluates the concepts with respect to the current interpretation, and the “knowledge-dependent” one that instead evaluates them with respect to a specified knowledge base, comparing and contrasting their properties. We furthermore discuss the connections between these operators and linear classification models.

Keyphrases: description logic, linear models, machine learning, threshold operators

In: Diego Calvanese and Luca Iocchi (editors). GCAI 2019. Proceedings of the 5th Global Conference on Artificial Intelligence, vol 65, pages 68-80.

Links:	https://easychair.org/publications/paper/s17p
	https://doi.org/10.29007/hjt1

BibTeX entry

@inproceedings{GCAI2019:Knowledge_Dependence_Weighted_Description,
  author    = {Pietro Galliani and Oliver Kutz and Daniele Porello and Guendalina Righetti and Nicolas Troquard},
  title     = {On Knowledge Dependence in Weighted Description Logic},
  booktitle = {GCAI 2019. Proceedings of the 5th Global Conference on Artificial Intelligence},
  editor    = {Diego Calvanese and Luca Iocchi},
  series    = {EPiC Series in Computing},
  volume    = {65},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/s17p},
  doi       = {10.29007/hjt1},
  pages     = {68-80},
  year      = {2019}}

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