A Compherence Approach to Collaborative Academic Paper’s Ontology Based on Existing Linking Graph Prediction

The current study describes the technological and methodological approach to collaborative ontology development in inter-organizational settings. It depends on formalisation of ontology development cooperation by means of an explicit editorial process, coordinating change proposals between ontology editors in a flexible manner. Added to this is the presence of novel distributed change management of ontologies style, models, and methods. We introduce the Academic Paper Citation Ontology (APCO) as an new layer-style approach to presenting ontologies towards highest independence of the underlying language of the ontologies. We also have attendant manipulation, versioning, capture, storage, and maintenance approaches and methods that exist and which rely on existing notions that are at the cutting-edge. Additionally, we provide a suite of change propagation techniques for supporting the consistency maintenance of distributed replicas of the same ontology. Finally, to increase the domain coverage of FOAF, we have extended it by extracting social interaction facts and relationships from emerging ontology. One specific problem that arises from time to time in enriching and merging ontologies is what this paper is all about: choosing which of the several ontologies available best relates to a particular piece of text associated with an input domain. Artificial Neural Networks (ANNs), more specifically their application in the research field of Natural Language Processing (NLP), are the foundation of the approach proposed. Consider calculating the ontologies' relevance score by combining neural networks and natural language processing.