This paper explores the use of module extraction for incremental reasoning of knowledge bases (KB) based on description logics
(DLs). The main objective is to evaluate the different approaches that incrementally solve logical inference problems (tasks or services) based on modularization process in order to identify different strategies for implementing this process in future incremental reasoning algorithms. Three algorithms were found that use an incremental approach to solve the logical inference task of classification based on module extraction
of which two are implemented and tested in this paper. The evaluation results show how the incremental reasoning based on modularization enhances the reasoning eficiency due to a modification in DLs-based KB, because this update affects only a small number of components in the KB structure.
%0 Conference Paper
%1 liudmila2014exploring
%A Reyes-Álvarez, Liudmila
%A Molina-Morales, Danny
%A Hidalgo-Delgado, Yusniel
%A Roldán-Garcıa, Marıa del Mar
%A Aldana-Montes, José F.
%B 1st Cuban Workshop on Semantic Web
%D 2014
%E Amed, Leiva-Mederos
%E Yusniel, Hidalgo-Delgado
%I CEUR
%K Incremental Reasoning Semantic Web myown
%P 1-12
%T Exploring Incremental Reasoning Approaches based on Module Extraction
%U http://ceur-ws.org/Vol-1219/paper1.pdf
%V 1219
%X This paper explores the use of module extraction for incremental reasoning of knowledge bases (KB) based on description logics
(DLs). The main objective is to evaluate the different approaches that incrementally solve logical inference problems (tasks or services) based on modularization process in order to identify different strategies for implementing this process in future incremental reasoning algorithms. Three algorithms were found that use an incremental approach to solve the logical inference task of classification based on module extraction
of which two are implemented and tested in this paper. The evaluation results show how the incremental reasoning based on modularization enhances the reasoning eficiency due to a modification in DLs-based KB, because this update affects only a small number of components in the KB structure.
@inproceedings{liudmila2014exploring,
abstract = {This paper explores the use of module extraction for incremental reasoning of knowledge bases (KB) based on description logics
(DLs). The main objective is to evaluate the different approaches that incrementally solve logical inference problems (tasks or services) based on modularization process in order to identify different strategies for implementing this process in future incremental reasoning algorithms. Three algorithms were found that use an incremental approach to solve the logical inference task of classification based on module extraction
of which two are implemented and tested in this paper. The evaluation results show how the incremental reasoning based on modularization enhances the reasoning eficiency due to a modification in DLs-based KB, because this update affects only a small number of components in the KB structure.},
added-at = {2014-09-10T00:15:28.000+0200},
author = {Reyes-Álvarez, Liudmila and Molina-Morales, Danny and Hidalgo-Delgado, Yusniel and Roldán-Garcıa, Marıa del Mar and Aldana-Montes, José F.},
biburl = {https://www.bibsonomy.org/bibtex/25735bb99ea8b81bf1c123b5c25fb83e1/yhdelgado},
booktitle = {1st Cuban Workshop on Semantic Web},
editor = {Amed, Leiva-Mederos and Yusniel, Hidalgo-Delgado},
interhash = {f64a430ff4180602b38cbd52502722c3},
intrahash = {5735bb99ea8b81bf1c123b5c25fb83e1},
keywords = {Incremental Reasoning Semantic Web myown},
pages = {1-12},
publisher = {CEUR },
timestamp = {2014-09-10T00:17:15.000+0200},
title = {Exploring Incremental Reasoning Approaches based on Module Extraction},
url = {http://ceur-ws.org/Vol-1219/paper1.pdf},
volume = 1219,
year = 2014
}