Entity Annotation WordPress Plugin using TAGME Technology

The development of internet technology makes more information can be accessed. It makes information need to be organized in order to be easily managed. One solution can be used is by using the entity annotation approach which generates tags to represent that document. In this study, TAGME technology is implemented on a WordPress plugin, which is used to manage a blog. Moreover, information on Wikipedia ‘Bahasa Indonesia’ is processed to generate an anchor dictionary which is required by the technology that is implemented. This plugin performs entity annotation by giving tag suggestion for posts in a blog. Testing is carried out by measuring the precision, recall, and  of tag suggestions given by the plugin. The result shows that the plugin can give tag suggestions with precision 0.7638, recall 0.5508, and  0.59

MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach

Entity linking has recently been the subject of a significant body of research. Currently, the best performing approaches rely on trained mono-lingual models. Porting these approaches to other languages is consequently a difficult endeavor as it requires corresponding training data and retraining of the models. We address this drawback by presenting a novel multilingual, knowledge-based agnostic and deterministic approach to entity linking, dubbed MAG. MAG is based on a combination of context-based retrieval on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data sets and in 7 languages. Our results show that the best approach trained on English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse on datasets in other languages. MAG, on the other hand, achieves state-of-the-art performance on English datasets and reaches a micro F-measure that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc