GoranSMilovanovic has submitted this change and it was merged. (
https://gerrit.wikimedia.org/r/386323 )
Change subject: Semantics - t-SNE Maps
......................................................................
Semantics - t-SNE Maps
Change-Id: I9d3c5ec541ceb4fd7041dfed75907b5de8398843
---
M WDCM_OverviewDashboard/ui.R
M WDCM_SemanticsDashboard/server.R
M WDCM_SemanticsDashboard/ui.R
M WDCM_ShinyServerFrontPage/wdcm_ShinyFront.html
M WDCM_TechDocumentation/WikidataConcepts_TechDocumentation.odt
M WDCM_UsageDashboard/server.R
M WDCM_UsageDashboard/ui.R
7 files changed, 277 insertions(+), 182 deletions(-)
Approvals:
GoranSMilovanovic: Verified; Looks good to me, approved
diff --git a/WDCM_OverviewDashboard/ui.R b/WDCM_OverviewDashboard/ui.R
index 25b0bdb..7ae997d 100644
--- a/WDCM_OverviewDashboard/ui.R
+++ b/WDCM_OverviewDashboard/ui.R
@@ -4,7 +4,7 @@
### ---------------------------------------------------------------------------
### --- Setup
-rm(list=ls())
+rm(list = ls())
### --- general
library(shiny)
library(shinydashboard)
@@ -238,7 +238,9 @@
<br>
<p><font size =
2><b>N.B.</b> The current <b>Wikidata item usage statistic</b> definition is
<i>the count of the number of pages in a particular client project
where the respective
Wikidata item is used</i>. Thus, the current definition ignores the usage
aspects completely. This definition is motivated by the currently
- present constraints in
Wikidata usage tracking across the client projects. With more mature Wikidata
usage tracking systems, the definition will become a subject
+ present constraints in
Wikidata usage tracking across the client projects
+ (see <a href =
"https://www.mediawiki.org/wiki/Wikibase/Schema/wbc_entity_usage"; target =
"_blank">Wikibase/Schema/wbc entity usage</a>).
+ With more mature Wikidata
usage tracking systems, the definition will become a subject
of change. The term
<b>Wikidata usage volume</b> is reserved for total Wikidata usage (i.e. the sum
of usage statistics) in a particular
client project, group of
client projects, or semantic categories. By a <b>Wikidata semantic category</b>
we mean a selection of Wikidata items that is
that is operationally
defined by a respective SPARQL query returning a selection of items that
intuitivelly match a human, natural semantic category.
@@ -247,7 +249,10 @@
categories in WDCM is not
necessarily exhaustive (i.e. they do not necessarily cover all Wikidata items),
neither the categories are necessarily
mutually exclusive. The
Wikidata ontology is very complex and a product of work of many people, so
there is an optimization price to be paid in every attempt to
adapt or simplify its
present structure to the needs of a statistical analytical system such as WDCM.
The current set of WDCM semantic categories is thus not
- normative in any sense and
a subject of change in any moment, depending upon the analytical needs of the
community.</font></p>
+ normative in any sense and
can become a subject of change in any moment, depending upon the analytical
needs of the community.</font></p>
+ <p><font size = 2>The
currently used <b>WDCM Taxonomy</b> of Wikidata items encompasses the following
14 semantic categories: <i>Geographical Object</i>, <i>Organization</i>,
<i>Architectural Structure</i>,
+ <i>Human</i>,
<i>Wikimedia</i>, <i>Work of Art</i>, <i>Book</i>, <i>Gene</i>, <i>Scientific
Article</i>, <i>Chemical Entities</i>, <i>Astronomical Object</i>,
<i>Thoroughfare</i>, <i>Event</i>,
+ and <i>Taxon</i>.</font></p>
<hr>
<h4>Wikidata Usage
Overview</h4>
<br>
@@ -258,10 +263,10 @@
of the client project
pairwise Euclidean distances derived from the Projects x Categories contingency
table. Given that the original higher-dimensional space
from which the 2D map is
derived is rather constrained by the choice of a small number of semantic
categories, the similarity mapping is somewhat
imprecise and should be
taken as an attempt at an approximate big picture of the client projects
similarity structure only. More precise 2D maps of
- the similarity structures
in client projects are found on the WDCM Semantics Dashboard, where each
semantic category first receives an
+ the similarity structures
in client projects are found on the <a href =
"http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/"; target = "_blank">WDCM
Semantics Dashboard</a>, where each semantic category first receives an
<a href =
"https://en.wikipedia.org/wiki/Topic_model"; target = "_blank">LDA Topic
Model</a>,
and the similarity
structure between the client projects is then derived from project topical
distributions.<br>
- While the <i>Explore</i>
tab presents a dynamic <a href = "http://hafen.github.io/rbokeh/";
target="_blank">{Rbokeh}</a> visualization alongised
+ While the <i>Explore</i>
tab presents a dynamic <a href = "http://hafen.github.io/rbokeh/";
target="_blank">{Rbokeh}</a> visualization alongside
the tools to explore it in
detail, the <i>Highlights</i> tab shows a static <a href =
"http://ggplot2.org/"; target="_blank">{ggplot2}</a> plot with the most
important client projects
marked (<b>NOTE.</b> Only
top five projects (of each project type) in respect to Wikidata usage volume
are labeled).</font></p>
<hr>
@@ -271,7 +276,7 @@
category. The size of the
bubble reflects the volume of Wikidata usage from the respective category. If
two categories are found in proximity,
that means that the
projects that tend to use the one also tend to use the another, and vice versa.
Similarly to the Usage Overview, the 2D mapping is obtained by performing
a <a
href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding";
target="_blank">t-SNE dimensionality reduction</a>
- of the categories pairwise
Euclidean distances derived from the Projects x Categories contingency table.
</font></p>
+ of the pairwise category
Euclidean distances derived from the Projects x Categories contingency table.
</font></p>
<hr>
<h4>Wikidata Usage
Distribution</h4>
<br>
@@ -315,23 +320,25 @@
fluidRow(
column(width = 8,
HTML('<h2>WDCM Navigate</h2>
- <h4>Your orientation in
the WDCM Dashboards System<h4>
- <hr>
- <ul>
- <li><b>WDCM Overview</b>
(current dashboard).<br>
- <font size = "2">The big
picture. Fundamental insights in how Wikidata is used across the client
projects.</font></li><br>
- <li><b>WDCM
Semantics.</b><br>
- <font size =
"2">Detailed insights into the WDCM ontology (a selection of semantic
categories from Wikidata), its distributional
- semantics, and the way
it is used across the client projects. If you are looking for Topic Models, yes
that’s where
- they live in
WDCM.</font></li><br>
- <li><b>WDCM
Usage.</b><br>
- <font size =
"2">Fine-grained information on Wikidata usage across client projects and
project types. Cross-tabulations and similar..</font></li><br>
- <li><b>WDCM Items</b><br>
- <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects..</font></li><br>
- <li><b>WDCM System
Technical Documentation.</b><br>
- <font size = "2">A
document that will come to existence eventually. There are rumours of an
existing draft.</font></li>
- </ul>'
- )
+ <h4>Your orientation in the
WDCM Dashboards System<h4>
+ <hr>
+ <ul>
+ <li><b><a href =
"http://wdcm.wmflabs.org/";>WDCM Portal</a></b>.<br>
+ <font size = "2">The entry
point to WDCM Dashboards.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_OverviewDashboard/";>WDCM Overview</a> (current
dashboard)</b><br>
+ <font size = "2">The big
picture. Fundamental insights in how Wikidata is used across the client
projects.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/";>WDCM Semantics</a></b><br>
+ <font size = "2">Detailed
insights into the WDCM Taxonomy (a selection of semantic categories from
Wikidata), its distributional
+ semantics, and the way it
is used across the client projects. If you are looking for Topic Models -
that’s where
+ they live.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_UsageDashboard/";>WDCM Usage</a></b><br>
+ <font size =
"2">Fine-grained information on Wikidata usage across client projects and
project types. Cross-tabulations and similar..</font></li><br>
+ <li><b>WDCM Items</b><br>
+ <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects.<b> (Under development)</b></font></li><br>
+ <li><b>WDCM System
Technical Documentation</b><br>
+ <font size = "2">A document
that will come to existence eventually. There are rumours of an existing
draft.</font></li>
+ </ul>'
+ )
)
)
) # - tabPanel Structure END
@@ -346,7 +353,7 @@
column(width = 12,
hr(),
HTML('<b>Wikidata Concepts Monitor :: WMDE
2017</b><br>Diffusion: <a
href="https://phabricator.wikimedia.org/diffusion/AWCM/"; target =
"_blank">WDCM</a><br>'),
- HTML('Contact: Goran S. Milovanovic, Data Analyst,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
+ HTML('Contact: Goran S. Milovanovic, Data Scientist,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
<br>IRC: goransm'),
br(),
br()
diff --git a/WDCM_SemanticsDashboard/server.R b/WDCM_SemanticsDashboard/server.R
index f07ae5b..203de39 100644
--- a/WDCM_SemanticsDashboard/server.R
+++ b/WDCM_SemanticsDashboard/server.R
@@ -448,6 +448,55 @@
}, ignoreNULL = FALSE)
+ ### ------------------------------------------
+ ### --- TAB: tabPanel Similarity
+ ### ------------------------------------------
+
+ ### --- SELECT: update select 'selectCategory2'
+ updateSelectizeInput(session,
+ 'selectCategory2',
+ "Select Semantic Category:",
+ choices = categories,
+ selected = categories[round(runif(1, 1,
length(categories)))],
+ server = TRUE)
+
+ ### --- OBSERVE: input$selectCategory2
+ observeEvent(input$selectCategory2, {
+
+ if (!is.null(input$selectCategory2)) {
+
+ wdcmP <- wdcmProject %>%
+ select(Project, Usage)
+
+ projCatFrame <- wdcm2_projects_2dmaps %>%
+ filter(Category %in% input$selectCategory2) %>%
+ left_join(wdcmP, by = 'Project')
+
+ ### --- output$overviewPlotDynamic
+ output$overviewPlotDynamic <- renderRbokeh({
+ outFig <- figure(width = 1400, height = 900, logo = NULL) %>%
+ ly_points(D1, D2,
+ data = projCatFrame,
+ size = log(Usage),
+ color = 'Project Type',
+ hover = list(Project, Usage)) %>%
+ x_axis(visible = F) %>%
+ y_axis(visible = F) %>%
+ theme_grid(which = c("x", "y"),
+ grid_line_color = "white") %>%
+ theme_plot(outline_line_alpha = 0) %>%
+ set_palette(discrete_color = pal_color(unname(projectTypeColor)))
+ outFig
+ }) %>% withProgress(message = 'Generating plot',
+ min = 0,
+ max = 1,
+ value = 1, {incProgress(amount = 1)})
+
+ } else {return(NULL)}
+
+ }, ignoreNULL = FALSE)
+
+
}) ### --- END shinyServer
diff --git a/WDCM_SemanticsDashboard/ui.R b/WDCM_SemanticsDashboard/ui.R
index e9df674..7eb4aaf 100644
--- a/WDCM_SemanticsDashboard/ui.R
+++ b/WDCM_SemanticsDashboard/ui.R
@@ -147,7 +147,7 @@
)
), # - tabPanel
Semantic Models END
- tabPanel(title =
"Projects",
+ tabPanel(title = "Project
Semantics",
id = "projects",
fluidRow(
column(width =
12,
@@ -206,7 +206,50 @@
height = "1000px"))
)
)
- ) # - tabPanel
Projects END
+ ), # - tabPanel
Projects END
+ tabPanel(title =
"Similarity Maps",
+ id =
"similarity",
+ fluidRow(
+ column(width =
12,
+
fluidRow(
+
column(width = 6,
+
br(),
+
HTML('<font size = 2>Select a semantic category of Wikidata items to take a
look at. A 2D map will be generated where each
+
project is represented by a bubble, and where the distance between the
projects corresponds with the similarity in their
+
usage of Wikidata items <i>from the selected category</i>. Think about
semantic categories as perspectives from which you
+
can take a look at the structure of similarity that holds among the
Wikimedia projects in respect to their usage of Wikidata items.</font>'),
+
br(), br()
+
)
+
)
+ )
+ ),
+ # - fluidRow:
Category Selection
+ fluidRow(
+ br(),
+ column(width =
3,
+
selectizeInput("selectCategory2",
+
"Select Category:",
+
multiple = F,
+
choices = NULL,
+
selected = NULL)
+ )
+ ),
+ fluidRow(
+ hr(),
+ column(width =
12,
+
h4('Similarity Map'),
+
HTML('<font size = 2>Each bubble represents a client project.
+
The size of the bubble reflects the volume of Wikidata usage in the respective
project; a logarithmic scale is used in this plot.<br>
+
Projects similar in respect to their usage of Wikidata items <i>from the
selected category</i> are grouped together.
+
Use the tools next to the plot legend to explore the plot and hover over
bubbles for details.</font>'),
+ hr(),
+
withSpinner(rbokeh::rbokehOutput('overviewPlotDynamic',
+
width = "1400px",
+
height = "900px")
+ )
+ )
+ )
+ )
) # - tabBox: Dashboard
END
)
@@ -224,18 +267,21 @@
<h4>Introduction<h4>
<br>
<p><font size = 2>This
Dashboard is a part of the <b>Wikidata Concepts Monitor (WDMC)</b>. The WDCM
system provides analytics on Wikidata usage
- across the client projects.
The WDCM Overview Dashboard presents the big picture of Wikidata usage; other
WDCM dashboards go
- into more detail. The
Overview Dashboard provides insights into <b>(1)</b> the similarities between
the client projects in respect to their use of
- of Wikidata, as well as
<b>(2)</b> the volume of Wikidata usage in every client project, <b>(3)</b>
Wikidata usage tendencies, described by the volume of
- Wikidata usage in each of
the semantic categories of items that are encompassed by the current WDCM
edition, <b>(4)</b> the similarities between the
- Wikidata semantic
categories of items in respect to their usage across the client projects,
<b>(5)</b> ranking of client projects in respect to their
- Wikidata usage volume,
<b>(6)</b> the Wikidata usage breakdown across the types of client projects and
Wikidata semantic categories.</font></p>
+ across the
Wikimedia sister projects. The WDCM Semantics Dashboard is probably the central
and the analytically most complicated of all WDCM Dashboards.
+ Here we provide
only the necessary basics of distributional semantics needed in order to
understand the results of semantic topic modeling presented on this
+ WDCM dashboard. A
user who needs to dive deep into the similarity structures between the
Wikimedia sister projects in respect to their Wikidata usage patterns
+ will most
probably have to do some additional reading first. However, the Dashboard
simplifies the presentation of the results as much as possible to make them
+ accessible to any
Wikidata user or Wikipedia editor who is not necessarily involved in Data or
Cognitive Science. Reading through the <b>WDCM Semantic Topic Models</b>
+ section in this
page is <i>highly advised</i> to anyone who has never met semantic topic models
or distributional semantics before. Before that, our next stop: Definitions.
+ </font></p>
<hr>
<h4>Definitions</h4>
<br>
<p><font size =
2><b>N.B.</b> The current <b>Wikidata item usage statistic</b> definition is
<i>the count of the number of pages in a particular client project
where the respective
Wikidata item is used</i>. Thus, the current definition ignores the usage
aspects completely. This definition is motivated by the currently
- present constraints in
Wikidata usage tracking across the client projects. With more mature Wikidata
usage tracking systems, the definition will become a subject
+ present constraints in
Wikidata usage tracking across the client projects
+ (see <a href =
"https://www.mediawiki.org/wiki/Wikibase/Schema/wbc_entity_usage"; target =
"_blank">Wikibase/Schema/wbc entity usage</a>).
+ With more mature Wikidata
usage tracking systems, the definition will become a subject
of change. The term
<b>Wikidata usage volume</b> is reserved for total Wikidata usage (i.e. the sum
of usage statistics) in a particular
client project, group of
client projects, or semantic categories. By a <b>Wikidata semantic category</b>
we mean a selection of Wikidata items that is
that is operationally
defined by a respective SPARQL query returning a selection of items that
intuitivelly match a human, natural semantic category.
@@ -245,63 +291,59 @@
mutually exclusive. The
Wikidata ontology is very complex and a product of work of many people, so
there is an optimization price to be paid in every attempt to
adapt or simplify its
present structure to the needs of a statistical analytical system such as WDCM.
The current set of WDCM semantic categories is thus not
normative in any sense and
a subject of change in any moment, depending upon the analytical needs of the
community.</font></p>
+ <p>The currently used
<b>WDCM Taxonomy</b> of Wikidata items encompasses the following 14 semantic
categories: <i>Geographical Object</i>, <i>Organization</i>, <i>Architectural
Structure</i>,
+ <i>Human</i>,
<i>Wikimedia</i>, <i>Work of Art</i>, <i>Book</i>, <i>Gene</i>, <i>Scientific
Article</i>, <i>Chemical Entities</i>, <i>Astronomical Object</i>,
<i>Thoroughfare</i>, <i>Event</i>,
+ and <i>Taxon</i>.</p>
<hr>
- <h4>Wikidata Usage
Overview</h4>
+ <h4>WDCM Semantic Topic
Models</h4>
<br>
- <p><font size = 2>The
similarity structure in Wikidata usage <i>across the client projects</i> is
presented. Each bubble represents a client project.
- The size of the bubble
reflects the volume of Wikidata usage in the respective project. Projects
similar in respect to the semantics of Wikidata
- usage are grouped
together.<br>
- The bubble chart is
produced by performing a <a
href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding";
target="_blank">t-SNE dimensionality reduction</a>
- of the client project
pairwise Euclidean distances derived from the Projects x Categories contingency
table. Given that the original higher-dimensional space
- from which the 2D map is
derived is rather constrained by the choice of a small number of semantic
categories, the similarity mapping is somewhat
- imprecise and should be
taken as an attempt at an approximate big picture of the client projects
similarity structure only. More precise 2D maps of
- the similarity structures
in client projects are found on the WDCM Semantics Dashboard, where each
semantic category first receives an
- <a href =
"https://en.wikipedia.org/wiki/Topic_model"; target = "_blank">LDA Topic
Model</a>,
- and the similarity
structure between the client projects is then derived from project topical
distributions.<br>
- While the <i>Explore</i>
tab presents a dynamic <a href = "http://hafen.github.io/rbokeh/";
target="_blank">{Rbokeh}</a> visualization alongised
- the tools to explore it in
detail, the <i>Highlights</i> tab shows a static <a href =
"http://ggplot2.org/"; target="_blank">{ggplot2}</a> plot with the most
important client projects
- marked (<b>NOTE.</b> Only
top five projects (of each project type) in respect to Wikidata usage volume
are labeled).</font></p>
+ <h5>Suggested Readings</h5>
+ <ul>
+ <li><b>Distributional
Semantics.</b> In <i>Wikipedia</i>. Retrieved October 24, 2017, from <a href =
"https://en.wikipedia.org/wiki/Distributional_semantics"; target =
"_blank">https://en.wikipedia.org/wiki/Distributional_semantics</a></li>
+ <li><b>Topic model.</b> In
<i>Wikipedia</i>. Retrieved October 24, 2017, from <a href =
"https://en.wikipedia.org/wiki/Topic_model"; target =
"_blank">https://en.wikipedia.org/wiki/Topic_model</a></li>
+ <li><b>Latent Dirichlet
allocation.</b> In <i>Wikipedia</i>. Retrieved October 24, 2017, from <a href =
"https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"; target =
"_blank">https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation</a></li>
+ <li><b>Dimensionality
reduction.</b> In <i>Wikipedia</i>. Retrieved October 24, 2017, from <a href =
"https://en.wikipedia.org/wiki/Dimensionality_reduction"; target =
"_blank">https://en.wikipedia.org/wiki/Dimensionality_reduction</a></li>
+ </ul>
+ <p><font size = 2>While <a
href = "https://www.wikidata.org/wiki/Wikidata:Main_Page"; target =
"_blank">Wikidata</a> itself is a <a href =
"https://en.wikipedia.org/wiki/Ontology_(information_science)"
+ target = "_blank">semantic
ontology</a> with pre-defined and evolving normative rules of description and
inference, <b>Wikidata usage</b> is essentialy a social, behavioral phenomenon,
+ suitable for study by means
of <a href = "https://en.wikipedia.org/wiki/Machine_learning"; target =
"_blank">machine learning</a> in the field of <a href =
"https://en.wikipedia.org/wiki/Distributional_semantics";
+ target =
"_blank">distributional semantics</a>: the analysis and modeling of statistical
patterns of occurrence and co-occurence of Wikidata item and property
+ usage across the client
projects (e.g. <i>enwiki</i>, <i>frwiki</i>, <i>ruwiki</i>, etc). WDCM thus
employes various statistical approaches in an attempt to describe and provide
insights from the observable Wikidata
+ usage statistics (e.g. <a
href = "https://en.wikipedia.org/wiki/Topic_model"; target = "_blank">topic
modeling</a>, <a href = "https://en.wikipedia.org/wiki/Cluster_analysis"; target
= "_blank">clustering</a>,
+ <a href =
"https://en.wikipedia.org/wiki/Dimensionality_reduction"; target =
"_blank">dimensionality reduction</a>, all beyond providing elementary
descriptive statistics of Wikidata usage, of course).
+ <br><br>
+ <b><i>Wikidata Usage
Patterns.</b></i> The <i>“golden line”</i> that connects the reasoning behind
all WDCM functions can be non-technically described in the following way.
Imagine observing the number of times a set of
+ size <b>N</b> of particular
Wikidata items was used across some project (<i>enwiki</i>, for example).
Example having the same data or other projects as well: for example, if 200
projects are under analysis, then we
+ have <b>200</b> counts for
<b>N</b> items in a set, and the data can be desribed by a <b>N x 200</b>
matrix (<i>items</i> x <i>projects</i>). Each column of counts, representing
the frequency of occurence of all Wikidata
+ entities under
consideration across one of the 200 projects under discussion - a vector,
obviously - represents a particular <i>Wikidata usage pattern</i>. By
inspecting and modeling statistically the usage pattern matrix -
+ a matrix that encompasses
all such usage patterns across the projects, or the derived
covariance/correlation matrix - many insigths into the similarities between
Wikimedia projects items projects (or, more precisely,
+ the similarities between
their usage patterns) can be found.
+ <br>In essence, the
technology and mathematics behind WDCM relies on the same set of practical
tools and ideas that support the development of <a href =
"https://en.wikipedia.org/wiki/Semantic_search"; target = "_blank">semantic
search engines</a>
+ and <a href =
"https://en.wikipedia.org/wiki/Recommender_system"; target =
"_blank">recommendation systems</a>, only applied to a specific dataset that
encompasses the usage patterns for tens of millions of Wikidata entities across
its client projects.</font></p>
<hr>
- <h4>Wikidata Usage
Tendency</h4>
+ <h4>Dashboard: Semantic
Models</h4>
<br>
- <p><font size = 2>The
similarity structure in Wikidata usage <i>across the semantic categories</i> is
presented. Each bubble represents a Wikidata semantic
- category. The size of the
bubble reflects the volume of Wikidata usage from the respective category. If
two categories are found in proximity,
- that means that the
projects that tend to use the one also tend to use the another, and vice versa.
Similarly to the Usage Overview, the 2D mapping is obtained by performing
- a <a
href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding";
target="_blank">t-SNE dimensionality reduction</a>
- of the categories pairwise
Euclidean distances derived from the Projects x Categories contingency table.
</font></p>
+ <p><font size = 2>Each of
the 14 currently used semantic categories in the WDCM Taxonomy of Wikidata
items receives a separate topic model. Each topic model encompasses two or more
+ topics, or semantic themes.
Here you can select a semantic category (e.g. "Geographical Object", "Human")
and a particular topic from its model. The page will produce three outputs:
+ (1) the <i>Top 50 items in
this topic</i> chart, which presents the 50 most important items in the select
topic of the selected category\'s topic model, (2) the <i>Topic similarity
network</i>,
+ which presents the
similarity structure among the 50 most important items in the selected topic,
and (c) the <i>Top 50 projects in this topic</i> chart, where 50 Wikimedia
projects in which the
+ selected topic plays a
prominent role in the selected semantic category.
+ </font></p>
<hr>
- <h4>Wikidata Usage
Distribution</h4>
+ <h4>Dashboard: Project
Semantics</h4>
<br>
- <p><font size = 2>The plots
are helpful to build an understanding of the relative range of Wikidata usage
across the client projects.
- In the <i>Project Usage
Rank-Frequency</i> plot, each point represents a client project; Wikidata usage
is represented on the vertical and
- the project usage rank on
the horizontal axis, while only top project (per project type) are labeled. The
highly-skewed, asymmetrical
- distribution reveals that a
small fraction of client projects only accounts for a huge proportion of
Wikidata usage.<br> In the
- <i>Project Usage
log(Rank)-log(Frequency)</i> plot, the logarithms of both variables are
represented.
- A <a href =
"https://en.wikipedia.org/wiki/Power_law"; target="_blank">power-law</a>
relationship holds true if this
- plot is linear. The plot
includes the best linear fit, however, no attempts to estimate the underlying
probability distribution were made. </font></p>
+ <p><font size = 2>Make a
selection of Wikimedia projects here and hit <i>Apply Selection</i>. The
Dashboard will produce a series of charts, one per each Wikidata semantic
category that is
+ present in your selection
of projects, and compute the relative importance (%) of each topic in the given
selection and for each semantic category. Do not forget that category specific
+ semantic models do not
necessarily encompass the same number of topics (in fact, they rarely do);
also, <i>Topic n</i> in one category is obviously not the same thing as
<i>Topic n</i> in
+ some other category.
+ </font></p>
<hr>
- <h4>Client Project
Types</h4>
+ <h4>Dashboard: Similarity
Maps</h4>
<br>
- <p><font size = 2>Project
types are provided in the rows of this chart, while the semantic categories are
given on the horizontal axis.
- The height of the
respective bar indicates Wikidata usage volume from the respective semantic
category in a particular client project.</font></p>
- <hr>
- <h4>Client Projects Usage
Volume</h4>
- <br>
- <p><font size = 2>Use the
slider to select the percentile rank range of the Wikidata usage volume
distribution across the client project to show. The
- chart will automatically
adjust to present the selected projects in increasing order of Wikidata usage,
and presenting at most 30 top projects
- from the selection.
<b>NOTE.</b> The <a href="https://en.wikipedia.org/wiki/Percentile_rank";
target="_blank">percentile rank</a>
- of a score is the
percentage of scores in its frequency distribution that are equal to or lower
than it.
- For example, a client
project that has a Wikidata usage volume greater than or equal to 75% of all
client projects under
- consideration is said to be
at the 75th percentile, where 75 is the percentile rank.<br> In effect, you can
browse the whole
- distribution of Wikidata
usage across the client projects by selecting the lower and uppers limit in
terms of usage percentile rank.</font></p>
- <hr>
- <h4>Wikidata Usage
Browser</h4>
- <br>
- <p><font size = 2>A
breakdown of Wikidata usage statistics across client projects and semantic
categories. To the left,
- a table that presents a
Client Project vs. Semantic Category cross-tabulation. The Usage column in this
table is the Wikidata
- usage statistic for a
particular Semantic Category x Client Project combination (e.g. The Wikidata
usage in the category "Human" in
- the dewiki project). To the
right, the total Wikidata usage per client project is presented (i.e. the sum
of Wikidata usage across
- all semantic categories for
a particular client project; e.g. the total Wikidata usage volume of
enwiki).</font></p>
+ <p><font size = 2>Upon a
selection of semantic category, the Dashboard will present a 2D map which
represents the similarities between the Wikimedia projects computed from the
selected category\'s
+ semantic model only. Here
you can learn how similar or dissimilar are the sister projects in respect to
their usage Wikidata items from a single semantic category.
+ </font></p>
+
')
)
)
@@ -315,17 +357,19 @@
<h4>Your orientation in the
WDCM Dashboards System<h4>
<hr>
<ul>
- <li><b>WDCM Overview</b>
(current dashboard).<br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/";>WDCM Portal</a></b>.<br>
+ <font size = "2">The entry
point to WDCM Dashboards.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_OverviewDashboard/";>WDCM Overview</a></b><br>
<font size = "2">The big
picture. Fundamental insights in how Wikidata is used across the client
projects.</font></li><br>
- <li><b>WDCM
Semantics.</b><br>
- <font size = "2">Detailed
insights into the WDCM ontology (a selection of semantic categories from
Wikidata), its distributional
- semantics, and the way it
is used across the client projects. If you are looking for Topic Models, yes
that’s where
- they live in
WDCM.</font></li><br>
- <li><b>WDCM Usage.</b><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/";>WDCM Semantics</a> (current
dashboard)</b><br>
+ <font size = "2">Detailed
insights into the WDCM Taxonomy (a selection of semantic categories from
Wikidata), its distributional
+ semantics, and the way it
is used across the client projects. If you are looking for Topic Models -
that’s where
+ they live.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_UsageDashboard/";>WDCM Usage</a></b><br>
<font size =
"2">Fine-grained information on Wikidata usage across client projects and
project types. Cross-tabulations and similar..</font></li><br>
<li><b>WDCM Items</b><br>
- <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects..</font></li><br>
- <li><b>WDCM System
Technical Documentation.</b><br>
+ <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects.<b> (Under development)</b></font></li><br>
+ <li><b>WDCM System
Technical Documentation</b><br>
<font size = "2">A document
that will come to existence eventually. There are rumours of an existing
draft.</font></li>
</ul>'
)
@@ -344,7 +388,7 @@
column(width = 12,
hr(),
HTML('<b>Wikidata Concepts Monitor :: WMDE
2017</b><br>Diffusion: <a
href="https://phabricator.wikimedia.org/diffusion/AWCM/"; target =
"_blank">WDCM</a><br>'),
- HTML('Contact: Goran S. Milovanovic, Data Analyst,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
+ HTML('Contact: Goran S. Milovanovic, Data Scientist,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
<br>IRC: goransm'),
br(),
br()
diff --git a/WDCM_ShinyServerFrontPage/wdcm_ShinyFront.html
b/WDCM_ShinyServerFrontPage/wdcm_ShinyFront.html
index fa93e8b..2e1387b 100644
--- a/WDCM_ShinyServerFrontPage/wdcm_ShinyFront.html
+++ b/WDCM_ShinyServerFrontPage/wdcm_ShinyFront.html
@@ -147,7 +147,7 @@
you probably need to get to learn about some important WDCM definitions
(and the constraints that dictated them) first. You can do that by
reading through the Definitions section of the WDCM Wikitech Technical
-Documentation <span style="font-weight: bold;">[[LINK HERE!!!]]</span>.
+Documentation <span style="font-weight: bold;"></span>.
Do not panic, please: it is written in a language that a non-technical
person who does not necessarily care about <a
href="https://en.wikipedia.org/wiki/Data_science"; target="_blank">Data
@@ -177,22 +177,25 @@
style="color: rgb(34, 34, 34); font-family: "Helvetica
Neue",Helvetica,"Lucida Grande",Tahoma,Verdana,sans-serif;
font-size: 25.2px; font-style: normal; font-weight: normal; letter-spacing:
normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none;
white-space: normal; widows: 2; word-spacing: 0px; background-color: rgb(255,
255, 255); display: inline ! important; float: none;"></span>Adam
Shorland, Software Developer, Wikimedia Foundation Deutschland</a>. <a
href="https://www.wikidata.org/wiki/Q18016466"; target="_blank">Lydia
-Pintcher, Product Manager of Wikidata, Wikimedia Foundation Deutschland</a>,
+Pintcher, Product Manager of Wikidata, Wikimedia Deutschland</a>,
supervised the development of the system and contributed the currently
-used WDCM Semantic Taxonomy <span style="font-weight: bold;">[[LINK
-HERE!!!]]</span> that the system relies on. The software development of
+used WDCM Semantic Taxonomy <span style="font-weight: bold;"></span> that the
system relies on. The software development of
the WDCM system is supervised by <a
href="https://www.wikidata.org/wiki/User:Tobias_Gritschacher_%28WMDE%29";
target="_blank">Tobias Gritschacher, Engineering Manager, Wikimedia
Foundation Deutschland</a>, while <a
href="https://www.mediawiki.org/wiki/User:Jan_Dittrich_%28WMDE%29";
target="_blank">Jan Dittrich, UX Design / Research, Wikimedia
-Foundation Deutschland</a> supervises the UI/UX aspects.</small><br>
+Foundation Deutschland</a> supervises the UI/UX aspects.The write-ups of the
+ previous experiences in managing Shiny Dashboards on behalf of
+ <a href = "https://wikimediafoundation.org/wiki/User:MPopov_(WMF)" target =
"_blank">Mikhail Popov</a> and the team that built our
+ <a href = "https://discovery.wmflabs.org/"; target = "_blank">Discovery
Dashboards</a>
+ were very helpful in the development of the WDCM Dashboards.</small><br>
<br>
-<h2>3. How it works?<br>
+<h2>3. How does it work?<br>
</h2>
<small>The WDCM Wikitech Technical
-Documentation <span style="font-weight: bold;">[[LINK HERE!!!]]</span>
+Documentation <span style="font-weight: bold;"></span>
should be providing enough information in respect to how WDCM works. To
put it in a nutshell, the current version of the WDCM system is fully
developed in <a href="https://www.r-project.org/"; target="_blank">R</a>,
@@ -235,23 +238,23 @@
<div id="shiny">
<h2>WDCM Dashboards</h2>
- <hr>
+
<a href = "http://wdcm.wmflabs.org/WDCM_OverviewDashboard/";><h4>WDCM
Overview</h4></a>
<a href = "http://wdcm.wmflabs.org/WDCM_OverviewDashboard/";><img
src="OverviewDashboard.png" alt="WDCM Overview" style="width:300px;"></a>
<br>
<div class="caption">
The Overview Dashboard provided an introductory overview - the "big
picture" of Wikidata usage.
</div>
- <hr>
+ <br>
<a href = "http://wdcm.wmflabs.org/WDCM_UsageDashboard/";><h4>WDCM
Usage</h4></a>
<a href = "http://wdcm.wmflabs.org/WDCM_UsageDashboard/";><img
src="UsageDashboard.png" alt="WDCM Usage" style="width:300px;"></a>
<br>
<div class="caption">
The Usage Dashboard provides a thorough insight into Wikidata usage
across the sister projects and semantic categories.
</div>
- <hr>
+ <br>
<a href = "http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/";><h4>WDCM
Semantics</h4></a>
<a href = "http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/";><img
src="SemanticsDashboard.png" alt="WDCM Semantics" style="width:300px;"></a>
<br>
@@ -259,8 +262,8 @@
The Semantics Dashboard provides an insight into the distributional
semantics of Wikidata usage.
</div>
- <hr>
+ <br>
<a href = "https://www.wikidata.org/wiki/Wikidata:Main_Page"; target =
"blank"><img src="Wikidata-logo-en.png" style="width:300px;"></a>
</div>
diff --git a/WDCM_TechDocumentation/WikidataConcepts_TechDocumentation.odt
b/WDCM_TechDocumentation/WikidataConcepts_TechDocumentation.odt
index f3c6168..eb011f0 100644
--- a/WDCM_TechDocumentation/WikidataConcepts_TechDocumentation.odt
+++ b/WDCM_TechDocumentation/WikidataConcepts_TechDocumentation.odt
Binary files differ
diff --git a/WDCM_UsageDashboard/server.R b/WDCM_UsageDashboard/server.R
index e70a06b..5bdfdd6 100644
--- a/WDCM_UsageDashboard/server.R
+++ b/WDCM_UsageDashboard/server.R
@@ -547,7 +547,7 @@
### --- SELECT: update select 'selectProject'
updateSelectizeInput(session,
'selectProject',
- choices = c(projects, paste("_", projectTypes, sep="")),
+ choices = c(projects, paste("_", projectTypes, sep =
"")),
selected = c("_Wikipedia", "_Wikinews", "_Wiktionary"),
server = TRUE)
diff --git a/WDCM_UsageDashboard/ui.R b/WDCM_UsageDashboard/ui.R
index 56b7727..cc2c552 100644
--- a/WDCM_UsageDashboard/ui.R
+++ b/WDCM_UsageDashboard/ui.R
@@ -425,90 +425,80 @@
tabPanel("Description",
fluidRow(
column(width = 8,
- HTML('<h2>WDCM
Overview Dashboard</h2>
+ HTML('<h2>WDCM Usage
Dashboard</h2>
<h4>Description<h4>
<hr>
<h4>Introduction<h4>
<br>
<p><font size =
2>This Dashboard is a part of the <b>Wikidata Concepts Monitor (WDMC)</b>. The
WDCM system provides analytics on Wikidata usage
- across the client
projects. The WDCM Overview Dashboard presents the big picture of Wikidata
usage; other WDCM dashboards go
- into more detail.
The Overview Dashboard provides insights into <b>(1)</b> the similarities
between the client projects in respect to their use of
- of Wikidata, as
well as <b>(2)</b> the volume of Wikidata usage in every client project,
<b>(3)</b> Wikidata usage tendencies, described by the volume of
- Wikidata usage in
each of the semantic categories of items that are encompassed by the current
WDCM edition, <b>(4)</b> the similarities between the
- Wikidata semantic
categories of items in respect to their usage across the client projects,
<b>(5)</b> ranking of client projects in respect to their
- Wikidata usage
volume, <b>(6)</b> the Wikidata usage breakdown across the types of client
projects and Wikidata semantic categories.</font></p>
- <hr>
+ across the
Wikimedia sister projects. The WDCM Usage Dashboard focuses on providing the
detailed statistics on Wikidata usage in particular sister projects or
+ the selected
subsets of them. Three pages that present analytical results in this Dashboard
receive a description here: (1) <b><i>Usage</i></b>, (2)
<b><i>Tabs/Crosstabs</i></b>,
+ and (3)
<b><i>Tables</i></b>. But first, definitions.</font></p>
+ <hr>
<h4>Definitions</h4>
<br>
<p><font size =
2><b>N.B.</b> The current <b>Wikidata item usage statistic</b> definition is
<i>the count of the number of pages in a particular client project
where the
respective Wikidata item is used</i>. Thus, the current definition ignores the
usage aspects completely. This definition is motivated by the currently
- present
constraints in Wikidata usage tracking across the client projects. With more
mature Wikidata usage tracking systems, the definition will become a subject
+ present
constraints in Wikidata usage tracking across the client projects
+ (see <a href =
"https://www.mediawiki.org/wiki/Wikibase/Schema/wbc_entity_usage"; target =
"_blank">Wikibase/Schema/wbc entity usage</a>).
+ With more mature
Wikidata usage tracking systems, the definition will become a subject
of change. The
term <b>Wikidata usage volume</b> is reserved for total Wikidata usage (i.e.
the sum of usage statistics) in a particular
client project,
group of client projects, or semantic categories. By a <b>Wikidata semantic
category</b> we mean a selection of Wikidata items that is
- that is
operationally defined by a respective SPARQL query returning a selection of
items that intuitivelly match a human, natural semantic category.
+ that is
operationally defined by a respective SPARQL query, returning a selection of
items that intuitivelly match a human, natural semantic category.
The structure of
Wikidata does not necessarily match any intuitive human semantics. In WDCM, an
effort is made to select the semantic categories so to match
the intuitive,
everyday semantics as much as possible, in order to assist anyone involved in
analytical work with this system. However, the choice of semantic
categories in
WDCM is not necessarily exhaustive (i.e. they do not necessarily cover all
Wikidata items), neither the categories are necessarily
mutually
exclusive. The Wikidata ontology is very complex and a product of work of many
people, so there is an optimization price to be paid in every attempt to
adapt or simplify
its present structure to the needs of a statistical analytical system such as
WDCM. The current set of WDCM semantic categories is thus not
normative in any
sense and a subject of change in any moment, depending upon the analytical
needs of the community.</font></p>
+ <p><font size =
2>The currently used <b>WDCM Taxonomy</b> of Wikidata items encompasses the
following 14 semantic categories: <i>Geographical Object</i>,
<i>Organization</i>, <i>Architectural Structure</i>,
+ <i>Human</i>,
<i>Wikimedia</i>, <i>Work of Art</i>, <i>Book</i>, <i>Gene</i>, <i>Scientific
Article</i>, <i>Chemical Entities</i>, <i>Astronomical Object</i>,
<i>Thoroughfare</i>, <i>Event</i>,
+ and
<i>Taxon</i>.</font></p>
<hr>
- <h4>Wikidata
Usage Overview</h4>
+ <h4>Usage</h4>
<br>
- <p><font size =
2>The similarity structure in Wikidata usage <i>across the client projects</i>
is presented. Each bubble represents a client project.
- The size of the
bubble reflects the volume of Wikidata usage in the respective project.
Projects similar in respect to the semantics of Wikidata
- usage are grouped
together.<br>
- The bubble chart
is produced by performing a <a
href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding";
target="_blank">t-SNE dimensionality reduction</a>
- of the client
project pairwise Euclidean distances derived from the Projects x Categories
contingency table. Given that the original higher-dimensional space
- from which the 2D
map is derived is rather constrained by the choice of a small number of
semantic categories, the similarity mapping is somewhat
- imprecise and
should be taken as an attempt at an approximate big picture of the client
projects similarity structure only. More precise 2D maps of
- the similarity
structures in client projects are found on the WDCM Semantics Dashboard, where
each semantic category first receives an
- <a href =
"https://en.wikipedia.org/wiki/Topic_model"; target = "_blank">LDA Topic
Model</a>,
- and the
similarity structure between the client projects is then derived from project
topical distributions.<br>
- While the
<i>Explore</i> tab presents a dynamic <a href =
"http://hafen.github.io/rbokeh/"; target="_blank">{Rbokeh}</a> visualization
alongised
- the tools to
explore it in detail, the <i>Highlights</i> tab shows a static <a href =
"http://ggplot2.org/"; target="_blank">{ggplot2}</a> plot with the most
important client projects
- marked
(<b>NOTE.</b> Only top five projects (of each project type) in respect to
Wikidata usage volume are labeled).</font></p>
+ <p><font size =
2>The Usage tab provides elementary statistics on Wikidata usage across the
semantic categories (left column) and sister projects
+ (right
column).<br>
+ <b><i>To the
left</b></i>, we first encounter a general overview of <i>Basic Facts</i>: the
number of Wikidata items that are encompassed by the current WDCM taxonomy (in
effect,
+ this is the
number of items that are encompassed by all WDCM analyses), the number of
sister projects that have client-side Wikidata usage tracking enabled
(currently,
+ that means that
the <a href = "https://www.mediawiki.org/wiki/Wikibase/Schema/wbc_entity_usage";
target = "_blank">Wikibase/Schema/wbc entity usage</a>) is present there),
+ the number of
semantic categories in the current version of the WDCM Taxonomy, and the number
of different sister project types (e.g. <i>Wikipedia</i>, <i>Wikinews</i>, etc).
+ <br>
+ The <b>Category
Report</b> subsection allows you to select a specific semantic category and
generate two charts beneath the selection: (a) the category top 30 projects
chart, and
+ (b) the category
top 30 Wikidata items chart. The first chart will display 30 sister projects
that use Wikidata items from this semantic category the most, with the usage
data
+ represented on
the horizontal axis, and the project labels on the vertical axis. The
percentages next to the data points in this chart refer to the proportion of
total category usage
+ that takes place
in the respective project. The next chart will display the 30 most popular
items from the selected semantic category: item usage is again placed on the
horizontal axis,
+ item labels are
on the vertical axis, and item IDs are placed next to the data points
themselves.
+ <br>
+ The <b>Categories
General Overview</b> subsection is static and allows no selection; it
introduces two concise overviews of Wikidata usage across the semantic
categories of
+ Wikidata items.
The <i>Wikidata Usage per Semantic Cateory</i> chart provides semantic
categories on the vertical and item usage statistics on the horizontal axis;
the percentages
+ tells us about
the proportion of total Wikidata usage that the respective semantic category
carries. Beneath, the <i>Wikidata item usage per semantic category in each
project type</i>
+ provides a
cross-tabulation of semantic categories vs. sister project types. The
categories are color-coded and represented on the horizontal axes, while each
chart represents one project
+ type. The usage
scale, represented on the vertical axes, is logarithmic to ease the comparison
and enable practical data visualization.
+ <br>
+ <b><i>To the
right</b></i>, an opportunity to inspect Wikidata usage in a single Wikimedia
project is provided. The <b>Project Report</b> section allows you to select a
single Wikimedia
+ project and
obtain results on it. The first section that will be generated upon making a
selection provides a concise narrative summary of Wikidata usage in the
selected project alongside
+ a chart
presenting an overview of Wikidata usage per semantic category. The next chart,
<i>Wikidata usage rank</i>, show the rank position of the selected project
among other sister projects
+ in respect to the
Wikidata usage volume. Beneath, a more complex structure, <i>Semantic
Neighbourhood</i>, is given. In this network, or a directed graph if you
prefere, each project points
+ towards the one
most similar to it. The selected projects has a different color. The results
are relevant only in the context of the current selection: the selected project
and its 20 nearest
+ semantic
neighboors only are presented. Once again: each project points to the one which
utilizes Wikidata in a way most similar to it. The <i>top 30 Wikidata items</i>
chart presents the top 30
+ Wikidata items in
the selected project: item labels are given on the vertical axis, Wikidata
usage on the horizontal axis, and the item IDs are labeled close to the data
points themselves.
+ </font></p>
<hr>
- <h4>Wikidata
Usage Tendency</h4>
+
<h4>Tabs/Crosstabs</h4>
<br>
- <p><font size =
2>The similarity structure in Wikidata usage <i>across the semantic
categories</i> is presented. Each bubble represents a Wikidata semantic
- category. The
size of the bubble reflects the volume of Wikidata usage from the respective
category. If two categories are found in proximity,
- that means that
the projects that tend to use the one also tend to use the another, and vice
versa. Similarly to the Usage Overview, the 2D mapping is obtained by
performing
- a <a
href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding";
target="_blank">t-SNE dimensionality reduction</a>
- of the categories
pairwise Euclidean distances derived from the Projects x Categories contingency
table. </font></p>
+ <p><font size = 2>
+ Here we have the
most direct opportunity to study the Wikidata usage statistics across the
sister projects. A selection of projects and semantic categories will be
intersected and only results in
+ the scope of the
intersection will be returned. The charts should be self-explanatory: the usage
statistic is always represented by the vertical axis, while the horizontal axis
and sub-panels play
+ various roles in
the context of whether a category vs project or a category vs project type
crosstabulation is provided. Data points are labeled in million (M) or thousand
(K) pages (see Wikidata usage)
+ definition
above). While charts can display a limited number of data points only, relative
to the size of the selection, each of them is accompanied by a <b>Data
(csv)</b> button that will initiate a
+ download of the
full respective data set as a comma separated file.
+ </font></p>
<hr>
- <h4>Wikidata
Usage Distribution</h4>
+ <h4>Tables</h4>
<br>
- <p><font size =
2>The plots are helpful to build an understanding of the relative range of
Wikidata usage across the client projects.
- In the <i>Project
Usage Rank-Frequency</i> plot, each point represents a client project; Wikidata
usage is represented on the vertical and
- the project usage
rank on the horizontal axis, while only top project (per project type) are
labeled. The highly-skewed, asymmetrical
- distribution
reveals that a small fraction of client projects only accounts for a huge
proportion of Wikidata usage.<br> In the
- <i>Project Usage
log(Rank)-log(Frequency)</i> plot, the logarithms of both variables are
represented.
- A <a href =
"https://en.wikipedia.org/wiki/Power_law"; target="_blank">power-law</a>
relationship holds true if this
- plot is linear.
The plot includes the best linear fit, however, no attempts to estimate the
underlying probability distribution were made. </font></p>
- <hr>
- <h4>Client
Project Types</h4>
- <br>
- <p><font size =
2>Project types are provided in the rows of this chart, while the semantic
categories are given on the horizontal axis.
- The height of the
respective bar indicates Wikidata usage volume from the respective semantic
category in a particular client project.</font></p>
- <hr>
- <h4>Client
Projects Usage Volume</h4>
- <br>
- <p><font size =
2>Use the slider to select the percentile rank range of the Wikidata usage
volume distribution across the client project to show. The
- chart will
automatically adjust to present the selected projects in increasing order of
Wikidata usage, and presenting at most 30 top projects
- from the
selection. <b>NOTE.</b> The <a
href="https://en.wikipedia.org/wiki/Percentile_rank"; target="_blank">percentile
rank</a>
- of a score is the
percentage of scores in its frequency distribution that are equal to or lower
than it.
- For example, a
client project that has a Wikidata usage volume greater than or equal to 75% of
all client projects under
- consideration is
said to be at the 75th percentile, where 75 is the percentile rank.<br> In
effect, you can browse the whole
- distribution of
Wikidata usage across the client projects by selecting the lower and uppers
limit in terms of usage percentile rank.</font></p>
- <hr>
- <h4>Wikidata
Usage Browser</h4>
- <br>
- <p><font size =
2>A breakdown of Wikidata usage statistics across client projects and semantic
categories. To the left,
- a table that
presents a Client Project vs. Semantic Category cross-tabulation. The Usage
column in this table is the Wikidata
- usage statistic
for a particular Semantic Category x Client Project combination (e.g. The
Wikidata usage in the category "Human" in
- the dewiki
project). To the right, the total Wikidata usage per client project is
presented (i.e. the sum of Wikidata usage across
- all semantic
categories for a particular client project; e.g. the total Wikidata usage
volume of enwiki).</font></p>
+ <p><font size =
2>The section presents searchable and sortable tables and crosstabulations with
self-explanatory semantics. Access full WDCM usage datasets from
here.</font></p>
+
')
)
)
@@ -519,23 +509,25 @@
fluidRow(
column(width = 8,
HTML('<h2>WDCM
Navigate</h2>
- <h4>Your
orientation in the WDCM Dashboards System<h4>
- <hr>
- <ul>
- <li><b>WDCM
Overview</b> (current dashboard).<br>
- <font size =
"2">The big picture. Fundamental insights in how Wikidata is used across the
client projects.</font></li><br>
- <li><b>WDCM
Semantics.</b><br>
- <font size =
"2">Detailed insights into the WDCM ontology (a selection of semantic
categories from Wikidata), its distributional
- semantics, and
the way it is used across the client projects. If you are looking for Topic
Models, yes that’s where
- they live in
WDCM.</font></li><br>
- <li><b>WDCM
Usage.</b><br>
- <font size =
"2">Fine-grained information on Wikidata usage across client projects and
project types. Cross-tabulations and similar..</font></li><br>
- <li><b>WDCM
Items</b><br>
- <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects..</font></li><br>
- <li><b>WDCM
System Technical Documentation.</b><br>
- <font size =
"2">A document that will come to existence eventually. There are rumours of an
existing draft.</font></li>
- </ul>'
- )
+ <h4>Your orientation in the
WDCM Dashboards System<h4>
+ <hr>
+ <ul>
+ <li><b><a href =
"http://wdcm.wmflabs.org/";>WDCM Portal</a></b>.<br>
+ <font size = "2">The entry
point to WDCM Dashboards.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_OverviewDashboard/";>WDCM Overview</a></b><br>
+ <font size = "2">The big
picture. Fundamental insights in how Wikidata is used across the client
projects.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_SemanticsDashboard/";>WDCM Semantics</a></b><br>
+ <font size = "2">Detailed
insights into the WDCM Taxonomy (a selection of semantic categories from
Wikidata), its distributional
+ semantics, and the way it
is used across the client projects. If you are looking for Topic Models -
that’s where
+ they live.</font></li><br>
+ <li><b><a href =
"http://wdcm.wmflabs.org/WDCM_UsageDashboard/";>WDCM Usage</a> (current
dashboard)</b><br>
+ <font size =
"2">Fine-grained information on Wikidata usage across client projects and
project types. Cross-tabulations and similar..</font></li><br>
+ <li><b>WDCM Items</b><br>
+ <font size =
"2">Fine-grained information on particular Wikidata item usage across the
client projects.<b> (Under development)</b></font></li><br>
+ <li><b>WDCM System
Technical Documentation</b><br>
+ <font size = "2">A document
that will come to existence eventually. There are rumours of an existing
draft.</font></li>
+ </ul>'
+ )
)
)
) # - tabPanel Structure END
@@ -551,7 +543,7 @@
column(width = 12,
hr(),
HTML('<b>Wikidata Concepts Monitor :: WMDE
2017</b><br>Diffusion: <a
href="https://phabricator.wikimedia.org/diffusion/AWCM/"; target =
"_blank">WDCM</a><br>'),
- HTML('Contact: Goran S. Milovanovic, Data Analyst,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
+ HTML('Contact: Goran S. Milovanovic, Data Scientist,
WMDE<br>e-mail: goran.milovanovic_...@wikimedia.de
<br>IRC: goransm'),
br(),
br()
--
To view, visit https://gerrit.wikimedia.org/r/386323
To unsubscribe, visit https://gerrit.wikimedia.org/r/settings
Gerrit-MessageType: merged
Gerrit-Change-Id: I9d3c5ec541ceb4fd7041dfed75907b5de8398843
Gerrit-PatchSet: 1
Gerrit-Project: analytics/wmde/WDCM
Gerrit-Branch: master
Gerrit-Owner: GoranSMilovanovic <goran.milovanovic_...@wikimedia.de>
Gerrit-Reviewer: GoranSMilovanovic <goran.milovanovic_...@wikimedia.de>
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