=Paper=
{{Paper
|id=Vol-2721/paper485
|storemode=property
|title=ScholarLensViz: A Visualization Framework for Transparency in Semantic User Profiles
|pdfUrl=https://ceur-ws.org/Vol-2721/paper485.pdf
|volume=Vol-2721
|authors=Felicitas Löffler,Valentin Wesp,Samira Babalou,Philipp Kahn,René Lachmann,Bahar Sateli,René Witte,Birgitta König-Ries
|dblpUrl=https://dblp.org/rec/conf/semweb/LofflerWBKLSWK20
}}
==ScholarLensViz: A Visualization Framework for Transparency in Semantic User Profiles==
https://ceur-ws.org/Vol-2721/paper485.pdf
ScholarLensViz: A Visualization Framework for
Transparency in Semantic User Profiles
Felicitas Löffler1 ID , Valentin Wesp1 ID , Samira Babalou1 ID , Philipp Kahn1 ,
René Lachmann1 ID , Bahar Sateli4 ID , René Witte4 ID ,
Birgitta König-Ries1,2,3 ID ?
1
Heinz Nixdorf Chair for Distributed Information Systems
Institute for Computer Science, Friedrich Schiller University Jena, Germany
{firstname.lastname}@uni-jena.de
2
Michael Stifel Center for Data-Driven and Simulation Science, Jena, Germany
3
German Center for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig,
Germany
4
Semantic Software Lab, Concordia University, Montréal, Canada
{lastname@semanticsoftware.info}
Abstract. Personalized applications are a two-edged sword. They are
convenient and assist users by keeping the focus on relevant topics, but
they are often black boxes and users typically do not know why cer-
tain entries appear in their profile. As transparency and provenance are
essential for researchers, in this paper, we introduce ScholarLensViz, a
visualization component for scholarly user profiles displaying a scholar’s
research competences including the provenance. It also provides visual-
izations to inspect the diversity of a profile and to analyze the semantic
similarity of the profile entries.
Keywords: Visualization, Provenance, Semantic User Profiles, LOD
1 Introduction
Scholarly user profiles are gaining increasing attention and are becoming more
important for various application scenarios, such as expertise retrieval [10],
search and recommendation of research articles [1] or research network analy-
sis [6]. These intelligent systems support scholars in time-consuming daily tasks,
such as data discovery and data reuse, by providing content tailored to a scholar’s
preferences. In most applications, scholars are permitted to inspect these col-
lected user preferences but do not know where the information comes from.
However, the justification of entries is an important issue in scholarly profiles.
Users need explanations why a certain preference is presented.
?
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
In this work, we address these issues and introduce ScholarLensViz 5 , a visu-
alization framework to display scholarly profiles based on a semantic user model.
In earlier work, Bakalov et al. [2] proved the feasibility of a transparent visu-
alization of semantic user profiles. We further developed that idea and present
a new graphical user interface with linkage to the LOD cloud. ScholarLensViz
presents a scholar’s competences and their provenance. We obtain the compe-
tences from publications and store them in an RDF graph, using a workflow
being introduced in our previous work ScholarLens [8]. The system also enables
the inspection of a profile’s diversity and provides visualizations to explore the
semantic similarity of the profile entries.
2 Related Work
Various scholarly applications have emerged that create user profiles out of dif-
ferent resources, for different purposes and with various visualizations. Semantic
Scholar [1], a smart search over research articles, generates user profiles from
publications based on paper heuristics. In contrast, AMiner [10] focuses on ex-
pertise retrieval, a research field that aims at finding experts on a certain topic.
Following a topic modeling approach, data is collected from institutional web-
sites, documents and conferences. The visualization provided includes radar dia-
grams for author’s statistics and stacked area charts for a scholar’s automatically
extracted research interests. Full semantic approaches are VIVO [5] and Scho-
lia [6]. While VIVO provides a framework, an ontology and a graphical user
interface for representing and visualizing scholars and their research context
within an organization, Scholia aims at visualizing scientific bibliographic infor-
mation through Wikidata. Both applications provide visualizations for co-author
networks and research topics a scholar is competent in. In addition, Scholia of-
fers various entry points to explore not only authors and publications but also
organizations, locations or projects and supports numerous display formats such
as timelines, scatter charts, line chart or trees. Provenance information, e.g.,
explanations from which source a topic or research interest has been extracted,
is less considered in current approaches for scholarly user profiles. Our system
attempts to close that gap and to leverage transparency and provenance in the
visualization of semantic user profiles.
3 Architecture
Fig. 1 presents the overall concept. A scholar’s provided or selected publications
are converted from PDF to XML and are added to ScholarLens [8] (orange-
colored) that processes the documents and extracts competences. The user model
comprises three core concepts: scholars, their competence topics and a set of
scholarly documents. All scholars are instances of the User Model Ontology
(UM) [3] User class. Users have CompetenceRecords containing the individual
5
ScholarLensViz source code, https://github.com/fusion-jena/ScholarLensViz
Competence as a literal. These classes are used from the Competence Manage-
ment Ontology (CM) [4]. In addition, we model a user’s publications with the
PUBO Ontology [9] that provides properties to describe relations between docu-
ments and semantic annotations generated by the pipeline. The final output is an
RDF graph containing the competences and being visualized by ScholarLensViz
(blue-colored). We separated the visualization (client-side) and the calls to the
Fig. 1: Overall concept to construct scholarly profiles with ScholarLens and to
visualize and integrate it in personalized applications with ScholarLensViz.
knowledge base (server-side). Thus, a server handles the SPARQL requests and
provides HTTP methods to the client. This separation minimizes security risks
such as sending direct SPARQL queries from the client to the knowledge base.
Embedding ScholarLensViz into personalized applications, for instance as wid-
get or standalone application, requires additional authentication mechanism to
be handled by the integrating system (green-colored).
4 ScholarLensViz
ScholarLensViz provides three dialogs. (A) The start dialog aims to display a
user’s Top-25 ranked competences and their provenance. The elements of the
pie chart (Fig. 2) represent a user’s competences. The arc of the pie slices de-
notes the competence rank, and the color represents the category obtained from
the Computer Science Ontology (CSO) [7] by matching the competence entries
(which are presented as DBpedia entities) with CSO labels. The comment tab,
becoming visible by clicking on a pie element, provides the competence descrip-
tion obtained from DBpedia. In order to justify the origin of the competences,
we provide a provenance tab displaying the sentences from a user’s publications
and highlighting the relevant phrases that contain the competence and its rank,
the paper title and, if available, the DOI.
In order to inspect the diversity of a user’s profile and the semantic similar-
ity between the profile entries, ScholarLensViz offers two further charts (Fig. 3).
Fig. 2: The start dialog presents a scholarly user profile in a pie chart and displays
the publications from which the competences have been extracted.
(B) A force-directed graph and (C) a chord chart visualizing the semantic sim-
ilarity and relatedness between the competences computed with Sematch [11].
While semantic similarity is hierarchy-based, the semantic relatedness consid-
ers all relations between concepts [11]. We use the average of both values as
both values influence the diversity of a profile. The more diverse a profile is,
the less strong connections appear in the force-directed graph per competence.
The chord chart allows a better analysis on the accumulated similarity values
over the selected competences. The length of the outer arc reflects the number
of similar connections a competence provides.
Fig. 3: Semantic similarity and relatedness of the profile entries are displayed
in a force-directed graph (left) and a chord chart (right). Dotted lines in the
force-directed graph denote a lesser (<0.4) semantic similarity, a dashed line a
medium (0.4 – 0.7) similarity and solid (>0.7) lines a strong semantic similarity
between the profile entries. The colors in the chord chart correspond to the
colored competence entries.
5 Demonstration
In this demo6 , visitors can inspect the competences from known computer scien-
tists working in the fields of Semantic Web. In addition, we added experts from
other computer science domains as a basis for comparison. Users can select the
competences and can change the thresholds for the semantic similarity.
Acknowledgements This work was supported by the German Academic Ex-
change Service (DAAD) within the scope of the PPP program and the Deutsche
Forschungsgemeinschaft (DFG) within the scope of the GFBio project (229241684).
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