Multilanguage hierarchical logics, or: how we can do without modal logics
Artificial Intelligence
A survey of paraconsistent semantics for logic programs
Handbook of defeasible reasoning and uncertainty management systems
Answer Set Programming: A Primer
Reasoning Web. Semantic Technologies for Information Systems
Equilibria in heterogeneous nonmonotonic multi-context systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Preference-based inconsistency assessment in multi-context systems
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
Semantics and complexity of recursive aggregates in answer set programming
Artificial Intelligence
Strategies for contextual reasoning with conflicts in ambient intelligence
Knowledge and Information Systems
Relational information exchange and aggregation in multi-context systems
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Inconsistency tolerance in P2P data integration: an epistemic logic approach
DBPL'05 Proceedings of the 10th international conference on Database Programming Languages
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
The representation of inconsistent knowledge in advanced knowledge based systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
| Hi-index | 0.00 |
Inconsistency in heterogeneous knowledge-integration systems with non-monotonic information exchange is a major concern as it renders systems useless at its occurrence. For the knowledge-integration framework of Multi-Context Systems, the problem of finding all possible resolutions to inconsistency has been addressed previously and some basic steps have been proposed to find most preferred resolutions. Here, we refine the techniques of finding preferred resolutions of inconsistency in two directions. First, we extend available qualitative methods using domain knowledge on the intention and category of information exchange to minimize the number of categories that are affected by a resolution. Second, we present a quantitative inconsistency measure for inconsistency resolutions, being suitable for scenarios where no further domain knowledge is available.