--- a/rdf-mt/index.html	Thu Jun 20 01:54:03 2013 -0500
+++ b/rdf-mt/index.html	Thu Jun 20 22:36:33 2013 -0500
@@ -421,7 +421,7 @@
 <p>In later sections these notions will be adapted to other classes of interpretations, but throughout this section 'entailment' should be interpreted as meaning simple entailment.
 </p>
 
-<p class="technote">We do not define a notion of entailment between sets of graphs. To determine whether a set of graphs entails a graph, the graphs in the set must first be combined into one graph, either by taking the union or the merge of the graphs. Unions preserve the common meaning of shared blank nodes, while merging effectively ignores any sharing of blank nodes. </p>
+<p class="technote">We do not define a notion of entailment between sets of graphs. To determine whether a set of graphs entails a graph, the graphs in the set must first be combined into one graph, either by taking the union or the merge of the graphs. Unions preserve the common meaning of shared blank nodes, while merging effectively ignores any sharing of blank nodes. Merging the set of graphs produces the same definition of entailment by a set that was defined in the 2004 RDF 1.0 specification.</p>
 
     <p><a id="defvalid">Any process which constructs a graph E from
     some other graph S is (simply) <dfn>valid</dfn> if S
@@ -1062,6 +1062,7 @@
 </section>
 </section>
 <section><h2>RDF Datasets</h2>
+<p class="issue">This section needs editing and probably some explanatory prose added. Exactly what depends upon the outcome of current email discussions.</p>
 <p>An RDF <a class="externalDFN">dataset</a> (see [[!RDF11-CONCEPTS]]) is a finite set of RDF graphs each paired with an IRI or blank node called the <strong>graph name</strong>, plus a <strong>default graph</strong>, without a name. Graphs in a single dataset may share blank nodes. The association of graph name IRIs with graphs is used by SPARQL [[RDF-SPARQL-QUERY]] to allow queries to be directed against particular graphs.</p>
 
 <p>Graph names in a dataset may refer to something other than the graph they are paired with. This allows IRI referring to other kinds of entities, such as persons, to be used in a dataset to <a>identify</a> graphs of information relevant to the entity <a>denote</a>d by the graph name IRI.</p>
@@ -1176,9 +1177,9 @@
 <section class="appendix" class="informative"><h2  id="finite_interpretations">Finite interpretations (Informative)</h2>
 <p>To keep the exposition simple, the RDF semantics has been phrased in a way which requires interpretations to be larger than absolutely necessary. For example, all interpretations are required to interpret the whole IRI vocabulary, and the universes of all D-interpretations must contain all possible strings and therefore be infinite. This appendix sketches, without proof, how to re-state the semantics using smaller semantic structures, without changing any entailments. </p>
 
-<p>Basically, it is only necessary for an interpretation structure to interpret the <a>name</a>s actually used in the graphs whose entailment is being considered, and to consider interpretations whose universes are at most as big as the number of names and blank nodes in the graphs.  More formally, we can define a <dfn>pre-interpretation</dfn> over a <a>vocabulary</a> V to be a structure I similar to a <a>simple interpretation</a> but with a mapping only from V to its universe IR.  Then when determining whether G entails E, consider only pre-interpretations over the finite vocabulary of <a>name</a>s actually used in G union E. The universe of such a pre-interpretation can be restricted to the cardinality N+B, where N is the size of the vocabulary and B is the number of blank nodes in the graphs. Any such pre-interpretation may be extended to <a>simple interpretation</a>s, all of which which will give the same truth values for any triples in G or E. Satisfiability, entailment and so on can then be defined with respect to these finite pre-interpretations, and shown to be identical to the ideas defined in the body of the specification.</p>
+<p>Basically, it is only necessary for an interpretation structure to interpret the <a>name</a>s actually used in the graphs whose entailment is being considered, and to consider interpretations whose universes are at most as big as the number of names and blank nodes in the graphs.  More formally, we can define a <dfn>pre-interpretation</dfn> over a <a>vocabulary</a> V to be a structure I similar to a <a>simple interpretation</a> but with a mapping only from V to its universe IR.  Then when determining whether G entails E, consider only pre-interpretations over the finite vocabulary of <a>name</a>s actually used in G union E. The universe of such a pre-interpretation can be restricted to the cardinality N+B+1, where N is the size of the vocabulary and B is the number of blank nodes in the graphs. Any such pre-interpretation may be extended to <a>simple interpretation</a>s, all of which which will give the same truth values for any triples in G or E. Satisfiability, entailment and so on can then be defined with respect to these finite pre-interpretations, and shown to be identical to the ideas defined in the body of the specification.</p>
 
-<p>When considering D-entailment, pre-interpretations may be kept finite by weakening the semantic conditions for datatyped literals so that IR need contain literal values only for literals which actually occur in G or E, and the size of the universe restricted to (N+B)×(D+1), where D is the number of recognized datatypes. (A tighter bound is possible.) For RDF entailment, only the finite part of the RDF vocabulary which includes those container membership properties which actually occur in the graphs need to be interpreted, and the second RDF semantic condition is weakened to apply only to values which are values of literals which actually occur in the vocabulary. For RDFS interpretations, again only that finite part of the infinite container membership property vocabulary which actually occurs in the graphs under consideration needs to be interpreted. In all these cases, a pre-interpretation of the vocabulary of a set of graphs may be extended to a full interpretation of the appropriate type without changing the truth-values of any triples in the graphs.</p>
+<p>When considering D-entailment, pre-interpretations may be kept finite by weakening the semantic conditions for datatyped literals so that IR need contain literal values only for literals which actually occur in G or E, and the size of the universe restricted to (N+B)×(D+1), where D is the number of recognized datatypes. (A tighter bound is possible.) For RDF entailment, only the finite part of the RDF vocabulary which includes those container membership properties which actually occur in the graphs need to be interpreted, and the second RDF semantic condition is weakened to apply only to values which are values of literals which actually occur in the vocabulary. For RDFS interpretations, again only that finite part of the infinite container membership property vocabulary which actually occurs in the graphs under consideration needs to be interpreted. In all these cases, a pre-interpretation of the vocabulary of a graph may be extended to a full interpretation of the appropriate type without changing the truth-values of any triples in the graphs.</p>
 
 <p>The whole semantics could be stated in terms of pre-interpretations, yielding the same entailments, and allowing finite RDF graphs to be interpreted in finite structures, if the <em>finite model property</em> is considered important.