We’ve been developing a prototype of the ‘Salami Layer’ idea first mooted a while back as a result of the University of Nottingham’s Salami project. This is all about linking data together to make useful services for people, and to provide more nodes in a growing network of interoperable data.
Salami focused on labour market information. We’ve been taking it forward in the MUSAPI (MUSKET-SALAMI Pilots) project with a view to producing a hybrid service (or services) that use both the MUSKET text description comparison technology and the SALAMI layer material to link together courses and job profiles.
Salami HTML Demo
Thanks to the skill of our newest member of staff at APS (Jennifer Denton), we now have a demonstrator here: http://188.8.131.52/Salami/salami. It uses recently published XCRI-CAP feeds from The Open University, Courtauld Institute and the University of Leicester as the source of its courses information (noting that these are not necessarily comprehensive feeds). Job Profile information has come from Graduate Prospects, from the National Careers Service and Target Jobs.
The purpose of the demonstrator is to show how we can link together subject concepts that are used to find courses with occupation concepts used to find job profiles. It relies on classifying courses with appropriate terms, in this case JACS3, for the discovery of relevant courses, mapping subject concepts to occupation concepts and then linking in the job profiles. This last task was done by attaching them to the occupation terms (in this case CRCI – Connexions Resource Centre Index – terms), rather than by searching – that will come later. All of these bits were wrapped up in a thesaurus. We then made it all go via a MySQL database, some Java code and a web page. There are some sharp edges still as we haven’t finished cleaning up the thesaurus, but I think it shows the principles.
We haven’t used random keywords, but well known classification systems instead, so that we can develop a discovery service that produces relevant and ranked results (eventually), not just a Go0gle-style million hits listing.
The way the demonstrator works is as follows:
Select a term from the drop-down list at the top. This list consists of our thesaurus terms of a mixture of academic subjects for searching for courses and occupation terms for searching job profiles. You can start typing, and it will go to that place on the list. For example try “History of Art”.
Then click Select. This will bring up a list of Related Terms (broader, narrower and related terms with respect to your selection), Subject/Occupation Terms (if you’ve picked a subject, it will show related Occupation Terms; if you picked an occupation, it will show related Subject Terms); and Links to Further Information.
Salami Demo 1
Salami Demo 2
You can navigate around the search terms we use by clicking on the Refine button next to the entries in the Related Terms and Subject/Occupation Terms lists. For example, if you click on Refine ‘history by topic’, this changes your focus to the ‘history by topic’ subject, and you can then navigate the subject hierarchy from there. If you click on Refine ‘heritage manager’, this changes your focus to that occupation and you can further navigate around jobs about information services or various subjects.
Salami Demo 3
At the bottom of the page we have a list of links to further information. These will be either links to relevant courses or to job profiles. The former are drawn from XCRI-CAP feeds, the latter are currently hard-wired into our thesaurus – we’re currently developing a method of using live searches for both types of link. For example, for “heritage manager” we have links to Graduate Prospects and Target Jobs profiles for Heritage Manager.
The upshot of the demonstrator is that we can show how to integrate the discovery of both courses and job profiles (and later on, job opportunities) using a single search term.
The technological underpinning of this is our thesaurus, which has the following broad components.
A ‘master’ table of thesaurus terms with attached classifications (in particular JACS3 for subjects and CRCI for job profiles).
A table of occupation-subject term links (O>S)
A table of subject-occupation term links (S>O)
A table of occupation-profile links, currently for implementation of the job profile URLs.
Inclusion of JACS3 codes on the course records and occupation codes on the job profiles is key to the discovery process, so that we can focus on concepts, not string searching. This means, for example, that a search for ‘history of art’ will find courses such as ‘MA in Conservation of Wall Painting’ or ‘MA in Art History’ (Courtauld Institute and Open University respectively), even though neither of the records or web pages for these courses contains the string ‘history of art’.
Perhaps more importantly we can find out that, if we’re interested in the history of art, there are several job areas that might well be relevant, not simply work in museums and galleries, but also heritage manager – and if we browse only one step from there, we can find occupation areas in the whole world of information services, from archaeologist to social researcher, from translator to patent attorney. And all of these possibilities can be discovered without going from this service to any form of separate ‘careers search’ website.
Our Salami demonstrator suggests that this approach could be extensible to other areas. Perhaps we can link in standard information about qualifications, just a short hop from courses. Maybe we can classify competencies or competence frameworks and link these to courses via vocabularies for learning outcomes / competence / curriculum topics.
The other strand in MUSAPI is the textual description comparison work using the MUSKET technology. Even via our Salami demonstrator, your lists are bald undifferentiated lists. If we can capture a range of search concepts from the user – parameters from their current circumstances, past skills, experience, formal and informal education and training, and aspirations – then we could use the MUSKET tools against the Salami results to help to put the results in to some form of rank order. The user would then be able to refine this to produce higher quality results in relation to that individual’s needs, and our slice of salami will have stretched a long way.