My first attempt at building a language-independent means of representing article topic -- i.e. grouping article page views into categories regardless of which Wikipedia language edition that article was read in -- was to map each page view to its Wikidata item and then represent the item based on its instance-of / subclass-of properties. An example of how this might work is below [1]. The goal is to build a set of higher-level categories to which any Wikidata item with an instance-of property can be mapped to. This is similar to the 14 categories in the Wikidata Concepts Monitor but ideally with no overlap and full coverage -- i.e. all items map deterministically to a single category.

In practice, a few challenges led to abandoning this pure Wikidata instance-of approach: many items have many instance-of properties and superclasses that make sense from an ontological perspective but not from a "what is this article about" perspective. This makes it challenging to weight the relative importance of a given instance-of property and the resulting "topics" do not always make sense. For example, Tesla Model 3 is an instance-of an automobile model, electric car, sedan, compact car, sports car, and battery electric vehicle. "Automobile model" then is a subclass of "vehicle model" which is a subclass of "model", which is shared across any items such as Rubik's cubes. Using page views as a means of determining how far to aggregate up this subclass network generally leads to these overly general categories that do not tell us much about the likely more pertinent topics (i.e. car-related).

[1] Example Wikidata taxonomy:

I'm currently exploring how to expand the ORES drafftopic model to languages besides English. The approach I have taken to this is to build a model that predicts the ~40 categories used by the ORES drafttopic model.

Write-up: https://meta.wikimedia.org/wiki/Research_talk:Characterizing_Wikipedia_Reader_Behaviour/Demographics_and_Wikipedia_use_cases/Work_log/2019-09-11

See an early exploration of this: http://paws-public.wmflabs.org/paws-public/User:Isaac_(WMF)/Wikidata%20Topic%20Model/fasttext_drafttopic_wikidata.ipynb

Overview of approach:

• Iterate through recent dump of English Wikipedia
• Gather all templates from article talk pages that include "wikiproject" or "wp" in their name (case-insensitive)
• For articles whose talk pages had potential WikiProject templates, look through Wikidata JSON dump and gather their QIDs and claims (based on exact matching of title)
• Map templates to mid-level-categories via existing drafttopic code. Well...this isn't entirely true, I've actually made a large suite of small adjustments:
  • I made some adjustments to the code to clean up the Geographical part of the hierarchy (e.g., WikiProjects about the Americas were mapping to "Cities" instead of "Americas")
  • I remove some duplicates (all WikiProjects related to Music were also mapped to Performing Arts, so I set them to only map to Music as the more specific category)
  • I add in a few WikiProjects that were previously skipped due to inconsistencies in the WikiProjects Directory.
  • I ignore the Assistance category (which is largely around Wikipedia maintenance)
  • I also hand-mapped template names to WikiProjects for those that did not automatically match and had at least 5000 occurrences. For instance, while WikiProject Medicine has a template with the same name, the template WPMED is also widely used and I hardcode in as equivalent to WikiProject Medicine.
• Use fastText to train a model that predicts mid-level categories (multilabel) based on a bag of words approach with the properties and values in a Wikidata's item