Conference item
Limiting logical violations in ontology alignment through negotiation
- Abstract:
- Ontology alignment (also called ontology matching) is the process of identifying correspondences between entities in different, possibly heterogeneous, ontologies. Traditional ontology alignment techniques rely on the full disclosure of the ontological models; however, within open and opportunistic environments, such approaches may not always be pragmatic or even acceptable (due to privacy concerns). Several studies have focussed on collaborative, decentralised approaches to ontology alignment, where agents negotiate the acceptability of single correspondences acquired from past encounters, or try to ascertain novel correspondences on the fly. However, such approaches can lead to logical violations that may undermine their utility. In this paper, we extend a dialogical approach to correspondence negotiation, whereby agents not only exchange details of possible correspondences, but also identify potential violations to the consistency and conservativity principles. We present a formal model of the dialogue, and show how agents can repair logical violations during the dialogue by invoking a correspondence repair, thus negotiating and exchanging repair plans. We illustrate this opportunistic alignment mechanism with an example and we empirically show that allowing agents to strategically reject or weaken correspondences when these cause violations does not degrade the effectiveness of the alignment computed, whilst reducing the number of residual violations.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Grant:
- Projects DBOnto
- ED3
- MaSI3
- Publisher:
- AAAI Press
- Journal:
- Principles of Knowledge Representation and Reasoning More from this journal
- Publication date:
- 2016-04-29
- Acceptance date:
- 2016-01-21
- Event location:
- Cape Town, South Africa
- Pubs id:
-
pubs:605770
- UUID:
-
uuid:42d9d145-18a6-4d54-afc2-10d9d82c36f3
- Local pid:
-
pubs:605770
- Source identifiers:
-
605770
- Deposit date:
-
2016-02-24
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2016
- Notes:
- Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This is the accepted manuscript version of the article. The final version is available online from AAAI at: [http://www.aaai.org/ocs/index.php/KR/KR16/paper/view/12893/12478]
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