Can we use Linked Data Semantic Annotators for the Extraction of Domain-Relevant Expressions?

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.