Last Call Review of draft-ohye-canonical-link-relation-
review-ohye-canonical-link-relation-genart-lc-carpenter-2011-12-13-00

Request Review of draft-ohye-canonical-link-relation
Requested rev. no specific revision (document currently at 05)
Type Last Call Review
Team General Area Review Team (Gen-ART) (genart)
Deadline 2011-12-22
Requested 2011-12-08
Authors Maile Ohye, Joachim Kupke
Draft last updated 2011-12-13
Completed reviews Genart Last Call review of -?? by Brian Carpenter
Assignment Reviewer Brian Carpenter 
State Completed
Review review-ohye-canonical-link-relation-genart-lc-carpenter-2011-12-13
Review completed: 2011-12-13

Review
review-ohye-canonical-link-relation-genart-lc-carpenter-2011-12-13

Please see attached review.




I am the assigned Gen-ART reviewer for this draft. For background on
Gen-ART, please see the FAQ at
< 

http://wiki.tools.ietf.org/area/gen/trac/wiki/GenArtfaq>.

Please wait for direction from your document shepherd
or AD before posting a new version of the draft. 

Document: draft-ohye-canonical-link-relation-04.txt
Reviewer: Brian Carpenter
Review Date: 2011-12-14
IETF LC End Date: 2011-12-29
IESG Telechat date: 2012-01-05

Summary:  Almost ready (LC comments not addressed)
--------

Minor issue:
------------

> 1.  Introduction
>
>   The canonical link relation specifies the preferred URI from a set of
>   URIs that return identical or vastly similar content, ...

I don't understand the phrase "vastly similar". I don't understand what algorithm
tests for vast similarity. 

The same applies to "extremely similar" in section 3 and "similar to" in section 5.
Also, "similar to" is weaker than "vastly similar" or "extremely similar"; so does
section 5 intend to weaken the earlier text? It seems that exactly the same phrase
should be used in each case (not just a vastly similar phrase).

This doesn't matter so much when it's a human designating the canonical relation.
But it can only be a matter of time before code starts to do this (which page is
canonical among a set of generated pages?). A vague word like "similar" turns this
into an AI problem.