diff --git a/doc/report/applications.tex b/doc/report/applications.tex index 17a2c8c8f9907372c9881f44afc6ccbb0300605d..1c18b1c5d7df376ffc558e8244cea6f95b2ed43a 100644 --- a/doc/report/applications.tex +++ b/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