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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.

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.

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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.

Random Notes

 

 

 

This past week I attended a workshop the Evolution and variation of classification systems organized by the Knowescape EU project. The project studies how knowledge evolves and makes cool maps like this one:

The aim of the workshop was to discuss how knowledge organization systems and classification systems change.  By knowledge organization systems, we mean things like the Universal Decimal Classification system or the Wikipedia Category Structure. My interest here is the interplay between the change in data and the change in the organization system used for that data. For example, I may use a certain vocabulary or ontology to describe a dataset (i.e. the columns), how does that impact data analysis procedures when that organization’s meaning changes.  Many of our visualizations decisions and analysis are based on how we categorize (whether mechanical or automatically) data according to such organizational structures. Albert Meroño-Peñuela gave an excellent example of that with his work on dutch historical census data. Furthermore, the organization system used may impact the ability to repurpose and combine data.

Interestingly, even though we’ve seen highly automated approaches emerge for search and other information analysis tasks Knowledge Organization Systems (KOSs) still often provide extremely useful information. For example, we’ve see how scheme.org and wikipedia structure have been central to the emergence of knowledge graphs. Likewise, extremely adaptable organization systems such as hashtags have been foundational for other services.

At the workshop, I particularly enjoyed Joesph Tennis keynote on the diversity and stability of KOSs. He’s work on ontogeny is starting to measure that change. He demonstrated this by looking at the Dewey Decimal System but others have shown that the change is apparent in other KOSs (1, 2, 3, 4). Understanding this change could help in constructing better and more applicable organization systems.

From both Joseph’s talk as well as the talk Richard Smiraglia (one of the leaders in the Knowledge Organization), it’s clear that as with many other sciences our ability to understand information systems can now become much more deeply empirical. Because the objects of study (e.g. vocabularies, ontologies, taxonomies, dictionaries) are available on the Web in digital form we can now analyze them. This is the promise of Web Observatories. Indeed, that was an interesting outcome of the workshop was that the construction of KOSs observatory was not that far fetched and could be done using aggregators such as Linked Open Vocabularies and Taxonomy Warehouse. I’ll be interested to see if this gets built.

Finally, it occurred to me that there is a major lack of studies on the evolution of the urban dictionary as a KOS. Somewhat ought to do something about it 🙂

Random Notes

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