Machine Learning and Extracting Knowledge from Big Data

The Resource Description Framework is essentially an application of Extensible Markup Language (XML) that helps describe Internet resources like a website and its content. RDF descriptions are called metadata since they are typically data about data like the particular site map or date of page updating. RDF is based on the idea of a model that is developed between statements and web resources. It is essential because the framework makes it easier for developers that build a product using that metadata.

A study by Casteleiro et al. (2016) explored the ability to disturbed work functions from machine learning algorithms with the terms of Cardiovascular Disease Ontology. This study was critical because it demonstrated the benefits of using terms from ontology classes to obtain other term variants. The study opened up the research of the feasibility of different methods that can scale with big data and enable automation of machine learning analysis.

 Sajjad, Bajwa, and Kazmi’s (2019) research was already looking at rule engines and producing rules in the era of big data. They proposed a method to work with the semantic complexity in the rules and then do an automated generation of the RDF model of rules to help in analyzing big data.  Specifically, they used a machine learning technique to classify the Semantic of Business Vocabularies and Rules (SBVR) rule and map it to the RDF model. A challenge for the research included the automatic parsing of the rules as well as the semantic interpretation. Also, mapping the vocabulary to the RDF syntax to verify the RDF schema proven successful, but challenging. However, their work did show that it was possible to have consistency in checking a set of big data rules through automated tools. However, these scholars also found a need for a method to semantically analyze rules to help with the testing and validating as it relates to rule changes. Their particular system makes an ontology model that can be useful in the interpretation of a set of rules. This research supports both the semantic understanding of rules, but also generates the RFP model of rules that provides support for querying.

#MachineLearning #Knowledge #BigData #RDF #XML

References

Casteleiro, M. A., Demetriou, G., Read, W. J., Prieto, M. J. F., Maseda-Fernandez, D., Nenadic, G., … & Stevens, R. (2016). Deep Learning meets Semantic Web: A feasibility study with the Cardiovascular Disease Ontology and PubMed citations. In ODLS (pp. 1-6).

Sajjad, R., Bajwa, I. S., & Kazmi, R. (2019). Handling Semantic Complexity of Big Data using Machine Learning and RDF Ontology Model. Symmetry11(3), 309.