Readings in nonmonotonic reasoning
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Reputation-based framework for high integrity sensor networks
Proceedings of the 2nd ACM workshop on Security of ad hoc and sensor networks
A survey of trust and reputation systems for online service provision
Decision Support Systems
Automatically refining the wikipedia infobox ontology
Proceedings of the 17th international conference on World Wide Web
Ontology Evolution with Evolva
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Evolva: A Comprehensive Approach to Ontology Evolution
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Strategies for the Evaluation of Ontology Learning
Proceedings of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Forgetting concepts in DL-lite
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
SCARLET: semantic relation discovery by harvesting online ontologies
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Ontology augmentation: combining semantic web and text resources
Proceedings of the sixth international conference on Knowledge capture
Building a highly consumable semantic model for smarter cities
Proceedings of the AI for an Intelligent Planet
Efficient searching top-k semantic similar words
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
An on-line algorithm for semantic forgetting
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Ontology evolution continues to be an important problem that needs further research. Key challenges in ontology evolution and creation of a highly consumable ontology include accomodating: (a) the subtle changes in the meaning of a model element over time, (b) the changing relevance of various parts of the model to the user, and (c) the complexity in representing time-varying semantics of model elements in a dynamic domain. In this work, we address the challenge of evolving an ontology to keep up with the domain changes while focusing on the utility of its content for relevance and imposing constraints for performance. We propose a novel evidence accumulation framework as a principled approach for ontology evolution, which is sufficiently expressive and semantically clear. Our approach classifies model elements (e.g., concepts) into three categories: definitely relevant (that must be included in the ontology), potentially relevant (that can be kept as backup), and irrelevant (that should be removed). Further, our approach dynamically re-classifies models based on external triggers like evidence or internal triggers, like the age of a model in the ontology. As a result, users will have an ontology which is both effective and efficient. We evaluate our approach based on two measures - ontology concept retention and ontology concept placement. This comprehensive evaluation in a single framework is novel and we show that our approach yields promising results.