Exploring natural language processing techniques to extract semantics from unstructured dataset which will aid in effective semantic interlinking
Abstract
Designing efficacious semantics for the dynamic interaction and searches has proven to be concretely challenging because of the dynamically of the semantic searches, method of browsing and visualization interfaces for high volume information. This has a direct impact on enhancing the capabilities of the web. To surmount the challenges of providing meaning to high volume unstructured datasets, Natural language processing techniques and implements have been proven to be propitious, however, the reactivity of these techniques should be studied and predicated on the objective of providing meaning to the unstructured data. This paper demonstrates the working of five NLP techniques namely, bag-of-words, TF-IDF, NER, LSA, and LDA. The experiment provides the kindred attribute accomplishment or the identification of the meaning of this unstructured data varies from one technique to another. However, NLP techniques can be efficient as they provide insights into the data and make it human-readable. This will in turn avail in building better human–machine intractable browsing and applications.
