Handling Semantic Complexity of Big Data using Machine Learning and RDF Ontology Model

Abstract

Business information required for applications and business processes is extracted using systems like business rule engines. Since the advent of Big Data, such rule engines are producing rules in a big quantity whereas more rules lead to more complexity in semantic analysis and understanding. This paper introduces a method to handle semantic complexity in rules and support automated generation of Resource Description Framework (RDF) metadata model of rules and such model is used to assist in querying and analysing Big Data. Practically, the dynamic changes in rules can be a source of conflict in rules stored in a repository. It is identified during the literature review that there is a need of a method that can semantically analyse rules and help business analysts in testing and validating the rules once a change is made in a rule. This paper presents a robust method that not only supports semantic analysis of rules but also generates RDF metadata model of rules and provide support of querying for the sake of semantic interpretation of the rules. The results of the experiments manifest that consistency checking of a set of big data rules is possible through automated tools.

Detection of Sensitive Data to Counter Global Terrorism

“…The CountVectorizer algorithm selects top-frequency words to form vocabulary and generates a sparse representation for the documents over the vocabulary [53]. CountVectorizer is chosen against HashingTF, as HashingTF is dependent on hashing of terms, and collision may occur between terms while hashing.…”