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Top1. Introduction
Knowledge of the natural world is limited not just by the complexity of natural entities and processes, but also by the complexity of the data that describe them. Enhanced understanding of the natural world depends on our capacity to access and integrate data from the biological, physical, and social sciences; mine those data for new knowledge; and conveys new insights to decision-makers and the general public. The integration of scientific data has proven to be particularly challenging as in (Walls et al., 2014), e.g., due to the inherent complexity and variety of scientific data, and the “hidden” implicit semantics, which often are known only to domain experts or to the scientists who created the data. Standardization of domain knowledge of biodiversity provides an effective remedy for the problem of non-relevance and increases the participation of experts. The Semantic Web has emerged out as a dynamic field which is not only powerful in terms of the transformation it can bring in the web world, but also the realization of its capabilities can give a new face to the web structure. The Semantic Web adds semantics to the data of current web and thus transforms it into machine understandable and processable form. Whatever is the application, the web puts up to, the access to knowledge is always required and it all depends upon the representation and storage of knowledge structures in a manner that no piece of knowledge in the said domain remains untouched, elucidated or unspecified.
Semantic Technologies support management, reasoning, sharing and gathering of data from heterogeneous sources. This leads to the concept of ontology in which resources are defined in formal way. Ontology is a jargon from meta- physics which means- existence of being as such. In the domain of Artificial Intelligence, Ontology is the formalization of an abstract notion of domain knowledge (Mishra et al., 2021). It is explicit specification of the manifestation of the domain related concept, called conceptualization. Ontology engineering is the modeling of the domain related facts and knowledge. W3C has standardized Web Ontology Language (OWL) for the description of the facts in Resource Description Framework (RDF) which can be formatted with syntaxes like N- triple, TURTLE, RDF/XML, RDF/OWL etc. Ontology engineering takes following things into account: defining concepts in the domain, arranging the concepts in a hierarchy (superclass and subclass), defining attributes and properties, defining individual, defining the axioms, filling the value of attribute/ property.
Relevance of ontology:
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Consensus Understanding: Ontology is proven to be a best way of sharing the common understanding of the domain related knowledge among people and among processing agents.
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Domain Knowledge Reusability: Ontology enables the reuse of abstract knowledge of the domain. As introducing standards and the concept of upper ontology, interoperability can be achieved. Hence, avoids ‘re-inventing the wheel’.
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Inferring Implicit Knowledge: The power of inference in ontology can be used to unleash the implicit knowledge, through SWRL i.e. Semantic Web Rule Language, from the explicit knowledge provided in knowledgebase through ontology representation.
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Abstract Domain Knowledge Conceptualization: The manifestation of the tangible and intangible (abstract) knowledge can be well conceptualized or formalized with aid of ontology.
Therefore, nowadays ontologies are utilized in various applications to fulfill the need of the user. However, in the context of Indian biodiversity, available ontologies are lacking to provide meaningful information. Hence, it is required to build the semantic structure for Indian biodiversity that describes biodiversity data in all its dimension. This article has the following two contributions:
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The development of a comprehensive knowledge base for Indian Biodiversity (InBiodiv-O) based on the proposed methodology.
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The evaluation of the InBiodiv-O ontology using different evaluation approaches.