Automatically Annotating Text with Linked Open Data.

Abstract

paper presents and evaluates two existing word sense disambiguation approaches which are adapted to annotate text with several popular Linked Open Data datasets. One of the algorithms is based on relationships between resources, while the other one takes advantage of resource definitions provided by the datasets. The aim is to test their applicability when annotating text with resources from WordNet, OpenCyc and DBpedia. The experiments expose several shortcomings related to the current approaches, which are mostly connected to overfitting the datasets. Based on the findings, we indicate future work directions regarding text annotation with Linked Open Data resources, which can bridge these shortcomings.