review q3

doc/report/applications.tex

@@ -291,9 +291,9 @@ solve algorithmic problems.
 
 \subsubsection{Contributors and Number of References}
 
-Finally, query~$\mathcal{Q}_3$ from literature~\cite{tetra} wants to
+Finally, query~$\mathcal{Q}_3$ from literature wants to
 know ``\emph{[a]ll areas of math that {Nicolas G.\ de Bruijn} has
-  worked in and his main contributions.}''  $\mathcal{Q}_3$~is asking
+  worked in and his main contributions}''~\cite{tetra}.  $\mathcal{Q}_3$~is asking
 for works of a given author~$A$.  It also asks for their main
 contributions, for example which particularly interesting paragraphs
 or code~$A$ has authored.  We picked this particular query as it
@@ -301,19 +301,18 @@ is asking for metadata, something that should be easily serviced by
 organizational knowledge.
 
 \noindent\emph{Organizational Aspect.} ULO has no concept of authors,
-contributors, dates and so on. Rather, the idea is to take
-advantage of the Dublin Core project which provides an ontology
-for such metadata~\cite{dcreport, dcowl}. For example, Dublin Core
-provides us with the \texttt{dcterms:creator} and
-\texttt{dcterms:contributor} predicates. Servicing~$\mathcal{Q}_3$
-requires us to look for creator~$A$ and then list all associated
-objects that they have worked on. Of course this
-requires above authorship predicates to actually be in use. With
-the Isabelle and Coq exports this was hardly the case; running
-some experiments we found less than 15 unique contributors and
-creators, raising suspicion that metadata is missing in the
-original library files. Regardless, in theory ULO allows us to
-query for objects ordered by authors.
+contributors, dates and so on. Rather, the idea is to take advantage
+of the Dublin Core project which provides an ontology for such
+metadata~\cite{dcreport, dcowl}. For example, Dublin Core provides us
+with the \texttt{dcterms:creator} and \texttt{dcterms:contributor}
+predicates. Servicing~$\mathcal{Q}_3$ requires us to look for
+creator~$A$ and then list all associated objects that they have worked
+on. Of course this requires above authorship predicates to actually be
+in use. With the Isabelle and Coq exports this was hardly the case;
+running some experiments we found less than 15 unique contributors and
+creators, raising suspicion that metadata is missing in the original
+library files. Regardless, existing ULO exports allow us to query for
+objects ordered by authors.
 
 \input{applications-q3.tex}
 
@@ -324,11 +323,12 @@ of~$A$, that is those works that~$A$ authored that are the most
 important. Sorting the result by number of references might be a good
 start.  To get the main contributions, we rate each individual work by
 its number of \texttt{ulo:uses} references. Extending the previous
-{SPARQL}, we can query the database for a ordered list of works,
+{SPARQL} query, we can ask the database for an ordered list of works,
 starting with the one that has the most
-references~(Figure~\ref{fig:q2b}).  We can formulate~$\mathcal{Q}_3$
-with just one SPARQL query. Because everything is handled by the
-database, access should be about as quick as we can hope it to be.
+references~(Figure~\ref{fig:q2b}).  We see that one can
+formulate~$\mathcal{Q}_3$ with just one SPARQL query. Because
+everything is handled by the database, access should be about as quick
+as we can hope it to be.
 
 While the sparse data set available to use only returned a handful of
 results, we see that queries like~$\mathcal{Q}_3$ are easily serviced