Abstract
It has been shown, both theoretically and empirically, that It has been shown, both theoretically and empirically, that reasoning about large and expressive ontologies is computationally hard. Moreover, due to the different reasoning algorithms and optimisation techniques employed, each reasoner may be efficient for ontologies with different characteristics. Based on recently-developed prediction models for various reasoners for reasoning performance, we present our work in developing a meta-reasoner that automatically selects from a number of state-of-the-art OWL reasoners to achieve optimal effciency. Our preliminary evaluation shows that the meta-reasoner signicantly and consistently outperforms 6 state-of-the-art reasoners and it achieves a performance close to the hypothetical gold standard reasoner.
reasoning about large and expressive ontologies is computationally
hard. Moreover, due to the dierent reasoning algorithms
and optimisation techniques employed, each reasoner
may be ecient for ontologies with dierent characteristics.
Based on recently-developed prediction models for various
reasoners for reasoning performance, we present our work in
developing a meta-reasoner that automatically selects from
a number of state-of-the-art OWL reasoners to achieve optimal
eciency. Our preliminary evaluation shows that the
meta-reasoner signicantly and consistently outperforms 6
state-of-the-art reasoners and it achieves a performance close
to the hypothetical gold standard reasoner.
reasoning about large and expressive ontologies is computationally
hard. Moreover, due to the dierent reasoning algorithms
and optimisation techniques employed, each reasoner
may be ecient for ontologies with dierent characteristics.
Based on recently-developed prediction models for various
reasoners for reasoning performance, we present our work in
developing a meta-reasoner that automatically selects from
a number of state-of-the-art OWL reasoners to achieve optimal
eciency. Our preliminary evaluation shows that the
meta-reasoner signicantly and consistently outperforms 6
state-of-the-art reasoners and it achieves a performance close
to the hypothetical gold standard reasoner.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM'14) |
| Editors | Minos Garofalakis, Ian Soboroff, Torsten Suel, Min Wang |
| Place of Publication | New York NY USA |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1935-1938 |
| Number of pages | 4 |
| ISBN (Print) | 9781450325981 |
| DOIs | |
| Publication status | Published - 2014 |
| Event | ACM International Conference on Information and Knowledge Management 2014 - Shanghai, China Duration: 3 Nov 2014 → 7 Nov 2014 Conference number: 23rd https://dl.acm.org/doi/proceedings/10.1145/2661829 |
Conference
| Conference | ACM International Conference on Information and Knowledge Management 2014 |
|---|---|
| Abbreviated title | CIKM 2014 |
| Country/Territory | China |
| City | Shanghai |
| Period | 3/11/14 → 7/11/14 |
| Internet address |