Discussion today included the difficulties of teaching coding and computer literacy. Graphical user interfaces (GUI) have enabled non-programmers to become more familiar with computers and their possibilities. However, these GUI (sometimes pronounced “gooey”) interfaces also change frequently depending on the application and its design aesthetics. Innovations in hardware, such as the trackpad and touch screens, can also effect computer literacy and usage. The command line interface (CLI) seems to be more timeless, with the logic of the program perhaps becoming more apparent through the typed commands themselves. Our instructor Jim mentioned that people learning to code for the first time have often spent years writing and communicating in academia or business, with sentences as the basic structure. For some though (including myself), writing sentences for computers instead of people is terribly difficult, despite the many similarities in structure and language. Programming classes for humanists, whether RDF and Linked Open Data, Ruby on Rails, or Python, can help scholars learn the language and logic of computers, which helps in using our own devices in new ways and also for understanding how social media and the wider Internet operate.
Our class project for the week was a demonstration of RDF and Linked Open data that was hand-written on construction paper. We chose the Women’s World Cup as our subject, with the Canadian team as our primary node. From the Canadian team outwards, we connected various bits of information in a series of triples, or three part data references. These triples were then written in Turtle to abbreviate the code. We used dbpedia.org as our authoritative ontology, circling the nodes in blue that would reference its database. For example, in the triple [“Canadian team” (subject) <--> “sponsored by” (predicate) <--> “Umbro” (object)], Umbro would link out to its dbpedia.org page: http://dbpedia.org/page/Umbro This gives the Umbro node a definitive reference, and the other information on the page, such as Umbro’s website, their brands, and location would also be accessible through the link. A query could then be run, “Is the Canadian women’s soccer or futbol team sponsored by any European companies.” Even though “Europe” was not on our node worksheet, the dbpedia.org reference would allow an extension of the query into the wider web, resulting in an answer of, “Yes, by Umbro in the UK.” This is a simple example of linked data, but with the further extensions provided by authoritative ontologies, more complex queries would certainly be possible.