Publication: RIO: Minimizing User Interaction in Ont...
Master data
| Title: | RIO: Minimizing User Interaction in Ontology Debugging |
| Subtitle: | |
| Abstract: | Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon some meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact on the performance in the bad case. The problem is that assessment of the meta information is only possible a-posteriori. Consequently, as long as the actual fault is unknown, there is always some risk of suboptimal interactive diagnoses discrimination. As an alternative, one might prefer to rely on a tool which pursues a no-risk strategy. In this case, however, possibly well-chosen meta information cannot be exploited, resulting again in inefficient debugging actions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable a-priori fault estimates are difficult to obtain. Using a corpus of incoherent real-world ontologies from the field of ontology matching, we show that the proposed risk-aware query strategy outperforms both meta information based approaches and no-risk strategies on average in terms of required amount of user interaction. |
| Keywords: |
| Publication type: | Article in compilation (Authorship) |
| Publication date: | 2013 (Print) |
| Published by: |
Web Reasoning and Rule Systems
Web Reasoning and Rule Systems
(
Springer Verlag GmbH;
W. Faber,
)
to publication |
| Title of the series: | - |
| Volume number: | - |
| First publication: | Yes |
| Page: | pp. 153 - 167 |
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| Keine Version vorhanden |
| Publication date: | 2013 |
| ISBN: | - |
| ISSN: | - |
| Homepage: | - |
Authors
| Patrick Rodler (internal) |
| Kostyantyn Shchekotykhin (internal) |
| Philipp Fleiß (internal) |
| Gerhard Friedrich (internal) |
Assignment
| Organisation | Address | ||||
|---|---|---|---|---|---|
|
Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
|
AT - A-9020 Klagenfurt |
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