- From: Ryan Shaw <ryanshaw@ischool.berkeley.edu>
- Date: Thu, 20 Aug 2009 16:35:39 -0700
- To: Mike Bergman <mike@mkbergman.com>
- Cc: "public-lod@w3.org community" <public-lod@w3.org>
>> It strikes me that this is the kind of thing it would be useful for >> publish as Linked Data. In other words, rather than analyzing >> instances, calculating a bunch of conditional probabilities, and then >> publishing a bunch of [ sameAs | equivalentClass | seeAlso | whatever >> ] assertions, one could publish a bunch of conditional probabilities >> or other similarity values, with some indication of the type of >> similarity measure used and links to the specific instance sets used >> to calculate the values. Others could then use these measures as they >> wished, setting their own thresholds for when to consider something an >> equivalence relation or not. >> >> Are there any vocabularies that might be used to publish such as data >> set as Linked Data? > > UMBEL has a specific vocabulary and set of properties for this. See the > umbel:withAlignment and umbel:withLikelihood properties: > > http://www.umbel.org/technical_documentation.html#vocabulary If I understand correctly, umbel:withAlignment is for class alignment and umbel:withLikelihood is for instance alignment? These seem like a good start, but I see a couple of drawbacks: 1. They rely on reification, which some in the LOD community seem to dislike[1]. 2. There is no way to distinguish different similarity measures, e.g. Jaccard coefficient vs. mutual info vs. log-likelihood. [1]http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/#avoidinLinkedDataContext
Received on Thursday, 20 August 2009 23:36:21 UTC