Contextual information retrieval in research articles: Semantic publishing tools for the research community

Abstract

Over the last few years, the voluminous increase in the academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using the research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work carried out for developing intelligent information systems, exploiting the research content. We present in this paper, a linked data application which uses a new semantic publishing model for providing value added information services for the research community. The paper presents a conceptual framework for modelling contexts associated with sentences in research articles and discusses the Sentence Context Ontology, which is used to convert the information extracted from research documents into machine-understandable data. The paper also reports on supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification.