Re: PageRank approaches

Michael,

The challenges I see are the same challenges that come with the traditional
PageRank model.

First, spam. Whatever model you use has to have balances in place that
measure trust and credibility using multiple signals or you potentially
risk people trying to manipulate your ranking system. I'm assuming for a
hobby or even an academic project, this is less of a concern, but anything
where there's the potential for revenue or traffic is subject to
manipulation.

Second, context. Along the lines of what Nathan was saying, what resources
count as important are somewhat relational to what questions people are
asking. I'd also expect that they're relational to particular biases that
the searcher has. FOAF and the social graph provide some answers, but I
haven't seen a lot of applications use it effectively outside of walled,
proprietary silos.

Third, which you pointed out, computing capabilities. It's hard enough
indexing standard web resources without having to follow the large number
of links that would come when you factored in triples.
--
http://www.boomajoom.com



On Fri, May 11, 2012 at 7:59 AM, Michael Brunnbauer <brunni@netestate.de>wrote:

>
> Hello Jörn,
>
> On Fri, May 11, 2012 at 04:10:06PM +0200, Jörn Hees wrote:
> > Just some from my related work (alphabetically):
>
> Thanks! I'll have a look.
>
> > If you know more I'd be interested ;)
>
> Probably not :-) I guess this one has been superceded by one in your list ?
>
>  Aidan Hogan, Andreas Harth, Stefan Decker. "ReConRank: A Scalable Ranking
> Method for Semantic Web Data with Context". In the Proceedings of the
> Second International Workshop on Scalable Semantic Web Knowledge Base
> Systems (SSWS 2006), Athens, GA, USA. November 5, 2006.
>  http://sw.deri.org/2005/07/n3rank/paper/paper.pdf
>
> Regards,
>
> Michael Brunnbauer
>
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