In this demo paper we introduce a Linked Data-driven, Semantically-enabled Journal Portal (SEJP) that o ers a variety of interactive scientometrics modules. SEJP allows editors, reviewers, authors, and readers to explore and analyze (meta)data published by a journal. Besides Linked Data created from the journal's internal data, SEJP also links out to other sources and includes them to develop more powerful modules. These modules range from simple descriptive statistics, over the spatial analysis of visitors and authors, to topic trending modules. While SEJP will be available for multiple journals, this paper shows its deployment to the Semantic Web journal by IOS Press. Due to its open & transparent review process, SWJ o ers a wide variety of additional information, e.g., about reviewers, editors, paper decisions, and so forth.
3 The current SEJP version can be used at http://sejp.geog.ucsb.edu/SWJPortal
SWJ employs a highly customized version of the popular Drupal content
management system (CMS)[
Once the back-end data was organized, structured, and published to the Web, a modular user interface was developed to allow visual analysis of the SWJ data. A modular approach was taken with a plug and play mentality, allowing the analysis modules to be separated and con gured based on the particular requirements of the applications. Built through HTML5, CSS, JavaScript, D3, and ExtJS, the front-end interface for the application is light-weight and compatible with any modern W3-compatible browser. Given the separation between the back-end data and the front-end analysis modules, SEJP is able to integrate data from other SPARQL endpoints and APIs; in our case the Semantic Web Dog Food portal and Microsoft Academic Search. 3
A variety of scientometric modules were developed for analyzing the SWJ data. Visual-analytic tools range from pie charts showing paper submission types to Cartograms of website visitors to edge-node graphs showing links between collaborating authors. Two of the more unique trend modules are discussed here. 3.1
This module shows how the research topics contained in the SWJ trend over
time. In order to construct this module, a topic modeling approach was taken
to extract latent topics in papers submitted to the SWJ. First, the text for all
original submissions between March 2010 and April 2013 were accessed, cleaned
of standard English stop-words and non-alpha numeric characters and stemmed4.
The submissions were then grouped by time periods (3 month are considered as
4 Using the Snowball stemmer - http://snowball.tartarus.org
one period), combining text from all articles within this period in to one single
document. This produced a total of 13 documents. Latent Dirichlet allocation
(LDA) was then applied to the documents with the purpose of extracting a
set number of latent topics. LDA is an unsupervised, generative probabilistic
model used to infer latent topics in a textual corpus [
(a) Word cloud showing (b) Research topic trending module showing how an example topic. topics change over time
The topics are then displayed through the user interface via an interactive line graph constructed with the JavaScript D3 charting library (Figure 1b). LDA de nes each document (publications grouped by time period) as a distribution over all topics with the total probability across all topics summing to 1. Visually this is represented with time period shown on the X-axis and probabilities for each topic (multiplied by 100) shown on the Y-axis. Initially the 20 topics are color coded and shown on the chart with the option to show or hide each topic through a click-able legend on the right. Hovering one's mouse over a line produces a pop-up bubble that informs the user of the topic strength as well as the top ten words most probable to that topic.
The author-paper-keyword hive chart module (Figure 2) is a unique interactive visualization showing the relationship between authors, papers, and keywords. This module has the capability to help users to discover the possibly hidden relations among the three distinct types of data.
Hovering over a node on the authors axis (orange) produces a one-to-many relationship on the keyword axis (green) showing all the keywords mentioned by a speci c author. Additionally, there is a one-to-many relationship between the selected author and the papers (blue axis) that he or she has contributed to the SWJ. The same relationships are true of selecting any node in either the paper or the keywords axis, allowing for exploration of the data from any node. This module allows users to nd authors who are concerned with similar research topics, and can also help visually discover all the coauthors of a researcher. Editors can use the module to nd suitable reviewers. 4
This demo paper presents a Linked Data-driven, semantically-enabled journal portal for scientometrics and deploys it to the Semantic Web journal. SEJP uses a journal's internal data and also connects to other (Linked Data) sources to includes them in the analysis. Two scientometric analysis modules were discussed in the prior sections focusing on the changing of topics over time as well as the relations between authors, papers and keywords. These modules, however, are only a small subset of a suite of interactive modules developed for the portal. As development continues to progress, new modules and tools will be added, further advancing the portal's capability for scientometrics. In the near future, SEJP will be deployed to other journals as well.