Ontology-based Modelling of Related Work Sections in Research Articles: Using CRFs for Developing Semantic Data based Information Retrieval Systems

摘要

Research articles are an important form of scientific communication. However, currently there are hardly any systems which exploit the content of research articles for information retrieval. The paper describes our work carried out in developing ontology-based information retrieval system using information extracted about sentences in research articles. We present results of a supervised learning mechanism using conditional random fields for context identification and sentence classification of sentences in the related work section of research articles. The labelling of sentences is carried out based on a classification framework, which we propose for classifying sentences in these sections. We proceed to develop a sentence context ontology for modelling the classified data obtained through CRFs. We also show how the ontology is further used for creating RDF data. Finally, we describe the user interface developed using SEWESE tags and SPARQL for querying the developed RDF data.