Generating Rule Sets from Model Trees
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
SemTag and seeker: bootstrapping the semantic web via automated semantic annotation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Survey of semantic annotation platforms
Proceedings of the 2005 ACM symposium on Applied computing
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Linked Data
Knowledge base population: successful approaches and challenges
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic semantic web annotation of named entities
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
Evaluating Entity Linking with Wikipedia
Artificial Intelligence
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Semantic annotation is the process of identifying expressions in texts and linking them to some semantic structure. In particular, Linked data-based Semantic Annotators are now becoming the new Holy Grail for meaning extraction from unstructured documents. This paper presents an evaluation of the main linked data-based annotators available with a focus on domain topics and named entities. In particular, we compare the ability of each tool to annotate relevant domain expressions in text. The paper also proposes a combination of annotators through voting methods and machine learning. Our results show that some linked-data annotators, especially Alchemy, can be considered as a useful resource for topic extraction. They also show that a substantial increase in recall can be achieved by combining the annotators with a weighted voting scheme. Finally, an interesting result is that by removing Alchemy from the combination, or by combining only the more precise annotators, we get a significant increase in precision, at the cost of a lower recall.