The other day an academic colleague asked what I was working on at the moment, in my answer I included – semantic webometrics – unsurprisingly he wanted some more detail. However ‘working on’ would be a bit of an exaggeration, ‘have a few ideas but nothing on paper yet’ would have been more appropriate. As such I thought I’d write down some of my rough thoughts on semantic webometrics.
For those who may have stumbled upon this blog from a non-webometric background, Webometrics as defined by Björneborn (2004), and as used by most of the webometrics community, means the:
…study of the quantitative aspects of the construction and use of information resources, structures and technologies on the Web drawing on bibliometric and informetric approaches.
Many of these quantitative studies have focused on hyperlinks. For example, investigating whether there is a correlation between a university’s inlinks (a.k.a. backlinks) and a university’s research ranking, or whether the interconnectedness of organisations in a region (as seen through interlinking web sites) can give an indication of a region’s level of innovation [outrageous self-citation].
One of the problems with many of these link-analyses is that they include a lot of noise. For example, when counting a university’s inlinks you will be counting both those from an academic highlighting a university’s quality research, and those from the disgruntled student highlighting his most hated tutor. Traditionally we have tried to understand the extent of this noise through large scale content analysis – the extremely tedious manual classification of web links and web pages.
The semantic web
A semantic web is one where information on the web is structured so that it is meaningful to computers. Well known examples of the semantic web include FOAF ontology allowing people to express the relationships with one another (e.g., the FOAF of Tim Berners-Lee) and the use of microformats for certain types of structured content including contact details (as included at www.davidstuart.co.uk) and reviews (which are now indexed by Google as Rich Snippets). This extra information information can be used to reduce the amount noise and enable meaningful webometric studies.
So when I say semantic webometrics I mean – webometric studies that make use of the additional information included in an increasingly semantic web.
For example, a semantic webometic study of the connection between an institution’s inlinks and research ranking would take into consideration who had placed the links and the attributes that they had associated with them. A semantic webometric study of the relationships between organisations would look at the explicit relationships contained in FOAF files as well as the implicit information on web pages.
Unfortunately there is relatively little semantic information embedded in the majority of web pages/sites, and where it is widespread, e.g., with the nofollow link attribute, webometricians have yet to develop the tools to make use of them.
As such we need to take an information-centred approach to semantic webometric research rather than a problem-centred approach. Whilst still small, there is an increasing amounts of semantic data being embedded in the web all the time, webometricians need to investigate what is available and how they can use it.