Abstract: In recent years, the dramatic increase in academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using this research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work on developing intelligent information systems that exploit online article databases. 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 reports supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification. The paper also describes a Semantic Web Application that provides various citation context based information services.
Keywords: Semantic publishing models, sentence context ontology, linked data application, conditional random fields, maximum entropy Markov models, citation classification, sentence context identification