Uncertainty Analysis in Ontology-Based Knowledge Representation

There are various real-world applications and areas, knowledge that handles with ambiguity, imperfect, or partial are difficult to capture. Such situations cause problems to discover new knowledge while dealing with decision-making and information retrieval. Ontologies are description-based logic but not able to handle the uncertain or incomplete knowledge in specific application domains. Therefore, it is very important to deal with uncertainty in ontologies. Moreover, we need to handle uncertainty for organizing the web data, so that machines can easily understand and retrieve the desired information efficiently and accurately. In this paper, we have identified various uncertainties in ontology/ies to achieve above objectives, based on different classification of ontology like intra-ontology, inter-ontology, and external ontology using different operations. Furthermore, we have carried out impact analysis of uncertainty using different context and situations to ontology and its operations for how and where uncertainties have to be represented and what semantics are considerable. In the literature, we have found that various researchers have been working on identifying the uncertainties in different domains of application like vague, inaccurate, missing, etc. Despite of identification of these uncertainties, we have also mapped the various situations and context related to ontology which is missing in the literature.