Racial Inequalities in America: Examining Socieoeconomic Statistics Using the Semantic Web

The visualization of recent episodes regarding apparently unjustifiable deaths of minorities, caused by police and federal law enforcement agencies, has been amplified through today's social media and television networks. Such events may seem to imply that issues concerning racial inequalities in America are getting worse. However, we do not know whether such indications are factual; whether this is a recent phenomenon, whether racial inequality is escalating relative to earlier decades, or whether it is better in certain regions of the nation compared to others. We have built a semantic engine for the purpose of querying statistics on various metropolitan areas, based on a database of individual deaths. Separately, we have built a database of demographic data on poverty, income, education attainment, and crime statistics for the top 25 most populous metropolitan areas. These data will ultimately be combined with government data to evaluate this hyp othesis, and provide a tool for predictive analytics. In this thesis, we will provide preliminary results in that direction. The methodology in our research consisted of multiple steps. We initially described our requirements and drew data from numerous datasets, which contained information on the 23 highest populated Metropolitan Statistical Areas in the United States. After all of the required data was obtained we decomposed the Metropolitan Statistical Area records into domain components and created an Ontology/Taxonomy via Protege to determine an hierarchy level of nouns towards identifying significant keywords throughout the datasets to use as search queries. Next, we used a Semantic Web implementation accompanied with Python programming language, and FuXi to build and instantiate a vocabulary. The Ontology was then parsed for the entered search query and returned corresponding results providing a semantically organized a nd relevant output in RDF/XML format.