- From: M. Scott Marshall <marshall@science.uva.nl>
- Date: Wed, 13 Feb 2008 23:14:49 +0100
- To: Matt Williams <matthew.williams@cancer.org.uk>
- CC: Alan Ruttenberg <alanruttenberg@gmail.com>, public-semweb-lifesci hcls <public-semweb-lifesci@w3.org>
Dear Matt, I see 'trust' as a 'view' that can be produced by running a filter over the data (provenance). The filter would implement my trust policy, or one of them. In other words, my trust in a given 'agent' can be due to the fact that it produces data using a certain algorithm. I also place a certain level of trust in the instrumentation that produced the data, the p-values of an analysis in the processing pipeline, human operators involved, etc. So, the weights or confidence measures that you are describing and that Alan is qualifying would be the *output* of such a trust policy or filter. I would not besmirch the data with my own personal trust models nor easily trust those of others. ;) I guess that what I'm trying to say is equivalent to Alan's point: I would prefer to keep facts and their evidence disclosed symbolically in the data so that different 'views' can take them into account. But, before I go to build such 'views' or filters, I will wait for that sort of information to become machine-readable as data provenance. :) However, I *can* try to make that sort of information available for data that I am helping to manage or produce. It seems that having a triple store (such as Virtuoso) with named graph support would make it possible to produce several types of potentially useful data provenence. -scott -- M. Scott Marshall http://staff.science.uva.nl/~marshall http://adaptivedisclosure.org Matt Williams wrote: > > Dear Alan, > > Thank you for making my point much more clearly than I managed. I'm a > little wary of probabilities in situations like the one you describe, as > it always seems a little hard to pin down what is meant by them. At > least with the symbolic approach, you can give a short paragraph saying > what you mean. > > I'll try and find a paper on the "p-modals" (possible, probable, etc.) > and ways of combining them tomorrow and put a paragraph on the wiki. > > Matt > > Alan Ruttenberg wrote: >> I'm personally fond of the symbolic approach - I think it is more >> direct and easier to explain what is meant. It's harder to align >> people to a numerical system, I would think, and also provides a false >> sense of precision. Explanations are easier to understand as well: "2 >> sources thought this probable, and 1 thought is doubtful" can be >> grokked more easily than score: 70% >> >> -Alan >> >> On Feb 12, 2008, at 4:03 PM, Matt Williams wrote: >> >>> >>> Just a quick note that the 'trust' we place in an agent /could/ be >>> described probabilistically, but could also be described logically. >>> I'm assuming that the probabilities that the trust annotations are >>> likely to subjective probabilities (as we're unlikely to have enough >>> data to generate objective probabilities for the degree of trust). >>> >>> If you ask people to annotate with probabilities, the next thing you >>> might want to do is to define a set of common probabilities (10 - 90, >>> in 10% increments, for example). >>> >>> The alternative is that one could annotate a source, or agent, with >>> our degree of belief, chosen from some dictionary of options >>> (probable, possible, doubtful, implausible, etc.). >>> >>> Although there are some formal differences, the two approaches end up >>> as something very similar. There is of course a great deal of work on >>> managing conflicting annotations and levels of belief in the literature. >>> >>> Matt >>> >>> --http://acl.icnet.uk/~mw >>> http://adhominem.blogsome.com/ >>> +44 (0)7834 899570 >>> >> >
Received on Wednesday, 13 February 2008 22:15:00 UTC