Automating the Generation of Semantic Annotation Tools Using a Clustering Technique

Authors:
Vitór Souza;Nicola Zeni;Nadzeya Kiyavitskaya;Periklis Andritsos;Luisa Mich;John Mylopoulos
Affiliations:
Dept. of Information Engineering and Computer Science, ,;Dept. of Information Engineering and Computer Science, ,;Dept. of Information Engineering and Computer Science, ,;Dept. of Information Engineering and Computer Science, ,;Dept. of Computer and Management Sciences, University of Trento, Italy;Dept. of Information Engineering and Computer Science, ,
Venue:
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Year:
2008

Citing 3
Cited 0

Modern Information Retrieval

Modern Information Retrieval
Towards Regulatory Compliance: Extracting Rights and Obligations to Align Requirements with Regulations

RE '06 Proceedings of the 14th IEEE International Requirements Engineering Conference
Text mining through semi automatic semantic annotation

PAKM'06 Proceedings of the 6th international conference on Practical Aspects of Knowledge Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to generate semantic annotations for a collection of documents, one needs an annotation schema consisting of a semantic model (a.k.a. ontology) along with lists of linguistic indicators (keywords and patterns) for each concept in the ontology. The focus of this paper is the automatic generation of the linguistic indicators for a given semantic model and a corpus of documents. Our approach needs a small number of user-defined seeds and bootstraps itself by exploiting a novel clustering technique. The baseline for this work is the Cerno project [8] and the clustering algorithm LIMBO [2]. We also present results that compare the output of the clustering algorithm with linguistic indicators created manually for two case studies.