- From: Kingsley Idehen <kidehen@openlinksw.com>
- Date: Tue, 12 Apr 2011 08:36:08 -0400
- To: Norman Gray <norman@astro.gla.ac.uk>
- CC: glenn mcdonald <gmcdonald@furia.com>, "public-lod@w3.org" <public-lod@w3.org>
On 4/12/11 3:49 AM, Norman Gray wrote: > Glenn and all, greetings. > > On 2011 Apr 9, at 03:10, glenn mcdonald wrote: > >> I don't think data quality is an amorphous, aesthetic, hopelessly subjective >> topic. Data "beauty" might be subjective, and the same data may have >> different applicability to different tasks, but there are a lot of obvious >> and straightforward ways of thinking about the quality of a dataset >> independent of the particular preferences of individual beholders. Here are >> just some of them: > This is an excellent list. I think only a minority of these qualities could be scored precisely, but I think all of them could be scored on some awful-to-excellent scale, so that while they may not be quite objective metrics, they're at least clearly debatable. > > Complete objectivity is probably impossible here -- inevitable in a world where the concept of 'Rome' means significantly different things to the local authority, the ancient historian, and the tourist board. But 'solves my problem well' is a pretty good substitute. > > Best wishes, > > Norman > > Norman, Great insight! Glenn: this is why my demos are oriented towards enabling the beholder disambiguate his/her/its quest via filtering applied to entity types and other properties. My entire focus in on this very point outlined by Norman i.e., dealing with it at massive scales. You cannot enforce anything on the beholder of data. There are many scenarios where subjectively bad data is extremely good data. -- Regards, Kingsley Idehen President& CEO OpenLink Software Web: http://www.openlinksw.com Weblog: http://www.openlinksw.com/blog/~kidehen Twitter/Identi.ca: kidehen
Received on Tuesday, 12 April 2011 12:36:32 UTC