Last week, I was at in Malta for a small workshop on building or thinking about the need for observatories for knowledge organization systems (KOSs). Knowledge organization systems are things like taxonomies, classification schemes, ontologies or concept maps. The event was hosted by the EU COST action KNOWeSCAPE, which focuses on understanding the dynamics of knowledge through their analysis and importantly visualization.
— Joseph T. Tennis (@josephttennis) February 1, 2017
This was a follow-up to a previous workshop I attended on KOS evolution. Inspired by that workshop, I began to think with my colleague Mike Lauruhn about how the process of constructing KOS is changing with the incorporation of software agents and non-professional contributors (e.g. crowdsourcing). In particular, we wanted to try and get a handle on what a manager of a KOS should think about when dealing with its inevitable evolution especially with the introduction of these new factors. We wrote about this in our article Sources of Change for Modern Knowledge Organization Systems. Knowl. Org. 43(2016)No.8. (preprint).
In my talk (slides below), I presented our article in the context of building large knowledge graphs at Elsevier. The motivating slides were taken from Brad Allen’s keynote from the Dublin Core conference on metadata in the machine age. My aim was to motivate the need for KOS observatories in order to provide empirical evidence for how to deal with changing KOS.
Both Joseph Tennis and Richard P. Smiraglia gave excellent views on the current state-of-the-art of KOS ontogeny in information systems. In particular, I think the definitional terms introduced by Tennis are useful. He had the clearest motivation for the need for an observatory – we need to have a central dataset that is collected overtime in order to go beyond case study analysis (e.g. 1 or two KOS) to a population based approach.
I really enjoyed Shenghui Wang‘s talk on her and Rob Koopman’s experiments embeddings to start to try and detect concept drift within journal articles. Roughly put they used different vector spaces for each time duration and were able to see how particular terms changed with respect to other terms in those vector spaces. I’m looking forward to seeing how this work progresses.
The workshop was co-organized with the Wikimedia Community Malta so there was good representation from various members of the community. I particular enjoyed meeting John Cummings who is a Wikimedian in Residence at UNESCO. He told me about one of his project to help create high-quality wikipedia pages from UNESCO reports and other open access documents. It’s really cool seeing how deep research based content can be used to expand Wikipedia and the ramifications that has on its evolution. Another Wikipedian Rebecca O’Neill gave a fascinating talk about her rethinking the relationship between citizen curators and traditional memory institutions. Lot’s of stuff at her site so check it out.
Overall, the event confirmed my belief that there’s lots more that knowledge organization studies can do with respect to large scale knowledge graphs and also those building these graphs can learn from the field.
- Are KOS useful for information retrieval?
- LODLaudromat might be a good mechanism for building a KOS Observatory
- Fantastic to meet Kalpana Shankar whose research on data repository sustainability is incredibly important.