The ontology of the City of L.A. dataset was very structured and organized into five main data categories titled categories, view types, department, tags, and federated domains. This dataset’s ontology was made in a way to entice anyone who looked at it. In other words, I believe that this dataset’s ontology was created in a way to promote living in the city of Los Angeles. Prospective families or individuals who are looking to live in Los Angeles would find this data most useful in my opinion. Sectoring the data into subcategories such as “A Livable and Sustainable City” would entice any person interested in conserving natural resources and a subcategory such as “A Safe City” would entice maybe a prospective family who was looking to move to a place where safety was ensured. This dataset’s ontology delves deeply into each governmental department as well, which a prospective Los Angeles resident would want to research on. Furthermore, the category “Tags” has subcategories that relate directly to buildings, construction, and safety—all aspects of a city a potential resident wants to know. However, I also think this dataset’s ontology was geared towards people who were debating to open a business in the Los Angeles area. A lot of material covered in the dataset had the tag “finance” and lists of all the businesses in a certain sector; for example under the subcategory “A Prosperous City” a handful of data articles were titled “Active Businesses in the City of Los Angeles Council District #.” This dataset firmly claims that Los Angeles is safe, well-run, livable, sustainable, and prosperous; clearly, this ontology is not meant to disparage the reputation of the city. Los Angeles is generally not known to be a safe city, but by delving into the data regarding safety I realized a majority of the examples were about safety regulations for businesses and buildings—not safety of a community. If a viewer merely looked at the ontology of this dataset, they would have received a vastly different perspective than a viewer who inspected the data. I think overall, the spirit of Los Angeles is left out. Much of this is due to the fact that one would not be able to grasp the spirit of LA with just a digitized dataset, but this dataset is completely devoid of it. Personally, if I were considering a city to move to, I would like to know about the ambiance and culture of a location rather than the type of buildings and businesses in it. Lastly, if I could start over with data collection and create a different ontology, I would choose one that focuses on businesses solely. I would make categories of the different types of businesses and trends of what type of business prospers in a certain region of LA. Furthermore, I would solely focus on legislation regarding business and safety administrations. After analyzing this dataset, I can truly see that an ontology has a large role in altering any viewer’s perspective.