A Naive Method For Ontology Construction

Ontologies are being used to organize information in many domains like artificial intelligence, information science, semantic web, library science. Ontologies of an entity having different information can be merged to create more knowledge of that particular entity. Ontologies today are powering more accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0, also termed as the semantic web, ontologies will play a more important role. Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can yield basic information about an entity. This paper proposes an automated method for ontology creation, using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning. Concepts drawn from these domains help in designing more accurate ontologies represented using the XML format. This paper uses document classification using classification algorithms for assigning labels to documents, document similarity to cluster similar documents to the input document, together, and summarization to shorten the text and keep important terms essential in making the ontology. The module is constructed using the Python programming language and NLTK (Natural Language Toolkit). The ontologies created in XML will convey to a lay person the definition of the important term's and their lexical relationship