This competency framework survey was developed by Matt Moore for the Innovations in Knowledge Organisation Conference 2015. It is intended to provide a simple self-assessment tool for practitioners working with Knowledge Organisation Systems (KOS) to identify their areas of strength and opportunities for improvement. It is indicative rather than exhaustive and we expect it to be developed further over time. We estimate it will take 10-20 minutes to complete.

Once you have completed your self-assessment, we suggest that you identify the area you are most interested in developing over the coming 6 months, and develop a simple action plan. Feel free to contact us if you have any questions or suggestions.

We will anonymise all responses and - provided you leave your email address - we'll send you a set of the findings, so that you can benchmark yourselves against others. We will not use your email address in the survey for any other purposes.

Here's a nice piece from HP about the importance of distinguishing between Hadoop and Big Data, although Big Data often provides a rationale for deploying Hadoop. Echoing a theme from the IKO Conference, there are also important skills and competency issues: “The talent gap is huge,” says Maguire. “What you need is somebody who knows 15 different technologies—a machine learning guru who also happens to know Python, Perl, distributed processing, Hive, Pig, SQL, and so on. That drives up the TCO—Hadoop comes with a big hidden cost.”

What do knowledge organisation professionals do? One thing was obvious from the 2015 IKO conference - KO professionals are engaged in a wide variety of activities. I could list all 15 case studies, as each had something valuable to impart but just a few give a flavour of this diversity:

• The BBC and the National Library Board of Singapore are building frameworks with Linked Data to not only open up their content to the general public but connect their content to other sources to provide a richer and more holistic user experience.

• Search Explained and Flax are applying search technologies both inside and outside the enterprise to make content more accessible for their clients.

• PebbleRoad were out observing how users actually do their jobs rather than just assuming the problems they faced were obvious, and designing apps that connected them to the data needed to support their tasks

• Straits Knowledge was working with graph databases to develop knowledge maps capable of being analysed for taxonomy-building

• Synaptica was building a Linked Data-ready image annotation system so that rich knowledge bases could be built around detailed images.

One way of understanding this complexity is to map out the skills that practitioners require to successfully implement projects like these. Any such mapping attempt will be always be provisional and subject to change. Such a mapping will have to find a balance between between having too little detail to be useful and too much detail to be manageable. And such a mapping faces the risks of any taxonomy that is being applied to taxonomists, such as differences in terminology and organising frameworks. Read the rest of this article here.

As interest in Linked Data (LD) continues to grow, many organizations—publishers, corporations, universities, libraries—are increasingly interested in strategies to jump-start LD initiatives. Any organisation that has an existing taxonomy (or other controlled vocabulary) can expedite the move to LD by leveraging its existing semantic structures as a bridge to an advanced LD-based semantic strategy. Read the rest of this article here.

The phrase “text analytics” might conjure up images of a group of computational linguists poring over complicated algorithms to reveal truth in language through some mixture of language study, advanced computer science, and alchemy. There is some accuracy to this. Because of this perceived complexity, non-academics may shy away from investigating the use and practical application of text analytics unless they are in very technical areas, e.g., business intelligence. Read the rest of this article here.

We have published a post conference newsletter for our attendees, containing additional articles and resources from our speakers, a link to an online competency framework, and links to all the conference videos and resources in one convenient location. If you'd like to stay in touch and are not already on our mailing list, do register here!

Here are the video files for our three keynotes, together with the supporting slides.

Patrick Lambe – The New Frontier in Knowledge Organisation: integrating methods, technologies and standards to deliver extraordinary results - the supporting slides are here
Charlie Hull – The Future of Search: fishing the streams of Big Data - the supporting slides are here
Matt Moore – Competence and Knowledge Organisation - the supporting slides are here

I have been working for some time on a National Science Foundation funded project with the American Institutes for Research to examine how machine classification working with taxonomies can be used to give greater insight into science and engineering activities, outputs and impact. An update on our project has just been published in the UK research periodical Research Fortnight, written with Evgeny Klochikin of the American Institutes of Research. RF460_p21_Klochikhin_Lambe.pdf