A Novel Approach to Extend KM Models with Object Knowledge Model (OKM) and Kafka for Big Data and Semantic Web with Greater Semantics

Abstract

In knowledge management (KM), everything is identified by an object and the world is a collection of objects. In the context of NLP, identifying grammar is a tedious task. Every sentence is being identified as a triplet: subject, predicate and object. The circumstantial detail of different tenses representing past and future of the grammar are very difficult to analyze. To identify such diversity of data - including streaming data, we propose a model, called Object Knowledge Model (OKM). Current scenario of RDF(S) defines metadata at every stage which is static in nature and lacks flexibility. Mainly OKM intends to provide a common framework for expressing machine-processable information with greater semantics than RDF—being modeled to identify the metadata in streams built on Kafka. This paper primarily discusses OKM grammar and how metadata can be analyzed implementing OKM. With known Stanford NLP technique plus others, the proposed OKM grammar is more flexible and demonstrates to be superior. The grammar checks genuinity of framing sentences (in various languages) as each language has its roots and connotations intrigued, and we claim that this proposed model is a better fitter for NLP understanding and projecting the merits of using them in KM models.