Abstract
Research papers can be visualized as a networked information space that contains a collection of information entities, inter-connected by directed links, commonly known as citation graph. There is a possibility to enrich the citation graph with meaningful relations using semantic tags. We have discovered and evaluated more than 150 citations' reasons from the existing published literature to be represented as citation tags. Many of these reasons have overlapped and diffused meanings. Annotating such a large volume of citation graphs with citation's reasons manually is nearly impossible, giving rise to a need to discover the citation's reasons automatically with high accuracy. The first step towards this is developing a minimal set of citation's context and reasons that are disjoint in nature. It would be a great help to the reasoning system if these reasons are represented in a formal way in the form of an ontology. By adopting a well-defined scientific methodology to formulate an ontology of citation reasons, we have reduced 150 reasons into only eight disjoint reasons, using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions. Based on our findings and experiments, we have proposed a citation's context and reasons ontology (CCRO) that provides abstract conceptualization required to organize citations' relations. CCRO has been verified, validated, and assessed by using the well-defined procedures and tools proposed in the literature for ontology evaluation. The results show that the proposed ontology is concise, complete, and consistent. For the instantiation and mapping of ontology classes on real data, we have developed a mapping graph among the verbs with predicative complements in the English Language, the verbs extracted from the selected corpus using the NLP and CCRO classes. Using this mapping graph, the mapping of ontology classes in each citation's sentiment is explained with a complete mapping on the selected dataset.
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