Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Design challenges and misconceptions in named entity recognition
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
On the complexity of dealing with inconsistency in description logic ontologies
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
NERD: a framework for unifying named entity recognition and disambiguation extraction tools
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
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Named entity extractors can be used to enrich both text and Web documents with semantic annotations. While originally focused on a few standard entity types, the ecosystem of annotators is becoming increasingly diverse, with recognition capabilities ranging from generic to specialised entity types. Both the overlap and the diversity in annotator vocabularies motivate the need for managing and integrating semantic annotations: allowing users to see the results of multiple annotations and to merge them into a unified solution. We demonstrate ROSEANN, a system for the management of semantic annotations. ROSEANN provides users with a unified view over the opinion of multiple independent annotators both on text and Web documents. It allows users to understand and reconcile conflicts between annotations via ontology-aware aggregation. ROSEANN incorporates both supervised aggregation, appropriate when representative training data is available, and an unsupervised method based on the notion of weighted-repair.