Why you need to understand Wikidata, no matter what field you’re in

Matt Vetter is Associate Professor of English and affiliate faculty in the Composition and Applied Linguistics Phd Program at Indiana University of Pennsylvania. A veteran instructor with WIki Education, Vetter has been teaching with Wikipedia since 2011 and has published extensively on Wikipedia-based education. His recent book, Wikipedia and the Representation of Reality, co-authored with Zach McDowell, is open access and available from Routledge.

Full disclosure? I’m not a GLAM or IT professional; In fact, I don’t have any formal training in structured data. And while I am well-versed in other Wikimedia projects, especially Wikipedia, my understanding of Wikidata (and structured open data in general) was fairly superficial just a few months ago. All of that changed when I enrolled in Wiki Education’s Wikidata Institute for a three-week class in October of 2021. 

Despite the fact that I have roughly a decade of experience teaching and researching Wikipedia and Wikipedia-based education, almost everything I learned was new. I began the course with a good understanding of Wikipedia, its parent foundation Wikimedia, and a few of its sister projects, but little knowledge of the how’s and why’s of Wikidata. Completing the course, I gained confidence and competence in the processes and elements of Wikidata, which helped me to better understand the Wikimedia ecosystem as a whole, as well as make contributions to this open knowledge database. But perhaps more importantly, I also learned how and why Wikidata is important to my professional work as a researcher and professor of rhetoric and writing studies, a subfield of English Studies that sits at the intersection of the humanities and social sciences. 

Wiki Education’s professional development courses are valuable in that participants not only learn about and apply technical knowledge in Wikimedia projects, we’re also provided an opportunity to network with others across professional sectors. In this way, taking a course from Wiki Education is a little bit like attending a series of workshops at an interdisciplinary professional conference — a unique opportunity for sure. In fact, hearing from other participants in the class about their own experience with particular tools or uses of Wikidata became one of the most helpful aspects of the Institute — especially as a way to better understand how professionals saw value in Wikidata for different types of work. 

Bringing my own experience as an academic researcher and professor in English Studies meant that, while I didn’t have much of a background in structured data (like other participants who might be working at a library, for instance), I was able to broaden my perspective and the perspectives of others simply by sharing my experience and goals surrounding Wikidata. My initial goals were fairly simple, and align well with the major educational outcomes outlined by Wiki Education: 

1. Identify properties and add qualifiers, ranks, and citations

2. Communicate effectively with the Wikidata community

3. Communicate about issues of equity and systemic bias facing Wikidata

In short, I wanted to better understand how Wikidata works, practice editing and adding items, and learn more about the social element of this community, all while being able to translate some of my understanding of Wikipedia’s systemic biases to this sister project. 

This wasn’t the first time I had taken a professional development course with Wiki Education, however, nor was it my first experience working with them. In the spring of 2021, I completed a separate Wiki Scholars & Scientists course focused on editing Wikipedia articles related to COVID-19. As someone who has been editing and teaching others to edit for nearly a decade (with the help of Wiki Education’s Student Program), much of the material in this previous course was, for me, review. The main value added was getting the chance to see someone else teach Wikipedia editing, as well as being able to set professional time aside to do important editorial work in a professional community. 

The Wikidata Institute was very different, of course, because I had little familiarity with the processes and concepts covered. While I initially felt like a bit of an outsider, this feeling dissipated as I learned to edit and add new Wikidata items, focusing especially on adding information relevant to research and researchers in writing studies. As I became more comfortable editing and adding items, we were also introduced to Wikidata’s SPARQL based query service that lets both humans and bots ask questions about…well just about anything. Learning to author queries in SPARQL was perhaps the most challenging part of this course, but also one of the most rewarding, because it was through the query service that I was able to make a more direct connection to my academic work. 

As an educator and researcher in English interested in Wikipedia and other OERs, I’m a contributing member of the CCCC Wikipedia Initiative, an organization who has made it their goal to “expand Wikipedia’s coverage of topics related to writing research and pedagogy to be comprehensive and current with major conversations in published scholarship.” As part of this work, the Initiative has also formed a related Wikiproject: WikiProject Writing.

Coming into the course, I knew that Wikidata would be important to my work with WikiProject Writing, because I had seen examples of other WikiProjects successfully integrating Wikidata for assessment and project management. However, it wasn’t until I learned how to build Wikidata queries that I realized just how useful Wikidata could be for understanding specific knowledge gaps and biases related to the representation of my field. Two of my more successful queries yielded the following lists of Wikidata items, which I link to below (while also providing the SPARQL). 

Q1.  Instance of human whose main field of study is rhetoric

SELECT DISTINCT ?item ?itemLabel WHERE {
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE]". }
  {
    SELECT DISTINCT ?item WHERE {
      ?item p:P31 ?statement0.
      ?statement0 (ps:P31/(wdt:P279*)) wd:Q5.
      ?item p:P101 ?statement1.
      ?statement1 (ps:P101/(wdt:P279*)) wd:Q81009.
    }
    LIMIT 100
  }
}

Q2. Instance of human whose main field of study is composition studies

SELECT DISTINCT ?item ?itemLabel WHERE {
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE]". }
  {
    SELECT DISTINCT ?item WHERE {
      ?item p:P31 ?statement0.
      ?statement0 (ps:P31/(wdt:P279*)) wd:Q5.
      ?item p:P101 ?statement1.
      ?statement1 (ps:P101/(wdt:P279*)) wd:Q2791145.
    }
    LIMIT 100
  }
}

 

While both queries could perhaps be modified to retrieve a larger or more relevant set of data, what surprised me the most about the results was their incompleteness. As someone deeply familiar with writing studies’ history and research, I could immediately see the glaring gap in the numbers of scholars represented in Wikidata. This was a turning point in my understanding because I realized just how much work there is to be done in terms of adding items and improving taxonomies in Wikidata. More importantly, I also realized how academics, scientists, and others with privileged access to research (no matter what field they’re in) need to do a better job at actually sharing that research. Wikidata, like Wikipedia and its other sister projects, is an important method for making that mission more possible.

Interested in taking the Wikidata Institute course Matt took? Visit wikiedu.org/wikidata to enroll.

Image credits: Armineaghayan, CC BY-SA 4.0, via Wikimedia Commons; Matthewvetter, CC BY-SA 4.0, via Wikimedia Commons