=Paper=
{{Paper
|id=None
|storemode=property
|title=A Search Interface for Researchers to Explore Affinities in a Linked Data Knowledge Base
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_6.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/VochtMWSE13
}}
==A Search Interface for Researchers to Explore Affinities in a Linked Data Knowledge Base==
https://ceur-ws.org/Vol-1035/iswc2013_demo_6.pdf
A Search Interface for Researchers to Explore
Affinities in a Linked Data Knowledge Base
Laurens De Vocht1 , Erik Mannens1 , Rik Van de Walle1 ,
Selver Softic2 , and Martin Ebner3
1
Ghent University - iMinds, Multimedialab
Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
{laurens.devocht,erik.mannens,rik.vandewalle}@ugent.be
2
Virtual Vehicle Research Center - Area Information and Process Management
Inffeldgasse 21a, 8010 Graz, Austria
selver.softic@tugraz.at
3
Graz University of Technology, IICM - Institute for Information Systems and
Computer Media
Inffeldgasse 16c, 8010 Graz, Austria
martin.ebner@tugraz.at
Abstract. Research information is widely available on the Web. Both
as peer-reviewed research publications or as resources shared via (mi-
cro)blogging platforms or other Social Media. Usually the platforms sup-
porting this information exchange have an API that allows access to the
structured content. This opens a new way to search and explore research
information. In this paper, we present an approach that visualizes in-
teractively an aligned knowledge base of these resources. We show that
visualizing resources, such as conferences, publications and proceedings,
expose affinities between researchers and those resources. We character-
ize each affinity, between researchers and resources, by the amount of
shared interests and other commonalities.
1 Introduction
Research 2.0 as adaptation of the Web 2.0 for researchers defines researchers as
main consumers of information. Typically researchers define queries with a set
of keywords when searching for information related to their work, for example
using Google or digital archives such as PubMed. Linked Data technologies offer
an entity based infrastructure to resolve the meanings of the keywords and the
relations between them. Combining keyword resolution and resource expansion
with Linked Data entities and filtering the results with personal preferences
enhances the search precision. Currently many researchers have a Social Media
account, such as on Twitter or Mendeley. We use these accounts to personalize
the search. Our interface supports searching for scientific events, authors or
groups of authors, as well as finding publications, and proceedings. The interface
uses a search engine which relies on Linked Data knowledge base containing
research related and personal information.
2 Real-time Keyword Disambiguation
We chose a real-time keyword disambiguation to guide the researchers in ex-
pressing their research needs. We do this by allowing users to select the correct
meaning from a drop down menu that appears below the search box. Present-
ing candidate query expansion terms in real-time, as users typed their queries,
can be useful during the early stages of the search [1]. In this is case it is very
important that the users understand meaning of the suggested terms. Therefore
we use an as straightforward as possible representation of the keyword mappings
as shown in Figure 1.
Fig. 1. Mapping of keywords to Linked Data entities.
3 Exploring Resources
Researchers can improve the definition of their “intended” search goal over sev-
eral iterations. Each time a combination of various resources is visualized. The
visualization suggests new queries: they are generally most useful for refining
the system’s representation of the researcher’s need. In case they have no idea
which entity to focus on or what topic to investigate next they get an overview
of possible entities of interest, like points of interest on a street map. By profil-
ing their activities and contributions on Social Media and other platforms such
as their own research publications, the affinity with the proposed resources is
enhanced. An iteration can consist of either one of two actions:
1. Query Expansion: The user expands the query space by clicking the re-
sults retrieved by initial keyword based search.The resolution of results hap-
pens based upon the properties of Linked Data like rdf:label, owl:sameAs,
rdf:seeAlso, dc:title or dc:description.
2. Additional Query Formulation: Additional query expansion happens ei-
ther through adding further keywords as well as through keyword combina-
tions already entered where the back-end tries to deliver additional results
based upon connection paths between the resources.
4 Visualizing Relations between Resources
We find relations between resources after matching the input given by the re-
searcher in the knowledge base. With the delivery of first results, our engine
expands the query and enhances the context. For this purpose we used a model
and an implementation that builds upon on our earlier work on the “Everything
is Connected” engine (EiCE) [2] and semantic profiling [3].
In the visualization we emphasize the affinities by showing, on a radial map [4],
how the current focused entity is related to the other found entities. It is based
on the concept of affinity that can be appropriately expressed in visual terms
as a spatial relationship: proximity [5]. We additionally express the amount of
unexpectedness as novelty of a resource in each particular search context. To
further enhance the guidance of users during search we have used two other
visual aids:
1. Color: Every entity has a type and associated unique color. For a certain
result set the user gets an immediate impression of the nature of the found
resources.
2. Size: We rank each entity according to novelty and relation to the context
and enlarge those that should attract attention from the researcher first. A
goal of the search is to explore information not seen before which makes it
difficult to define an accurate search goal. Besides allowing to search specific
entities, our visualization facilitates exploratory browsing. This is particu-
larly useful when information seeking with unclear defined search targets
[6].
Figure 2 shows how researchers can track the history of their search: the explored
relations are marked red and clearly highlight the context of a search. Researchers
can click on a list of resources they have searched to focus the visualization.
A screencast of the search interface is available online4 . In this screencast, we
show how researchers interact with the search interface and the above described
visualization.
5 Conclusions
We have developed an interface for personalized search in a social driven knowl-
edge base for researchers. Combining the latest Linked Data technologies with
an advanced indexing and path finding system, EiCE and Web 2.0 technologies
(such as JQuery and Django). The result is a semantic search application provid-
ing both a technical demonstration and a visualization that could be applied in
many other disciplines beyond Research 2.0. The main contribution of our work
is, besides retrieving resources from Linked Data repositories, allowing users to
interactively explore relations between the resources and find out the affinity
with each resource.
4
http://semweb.mmlab.be/search_interface_for_researchers
Fig. 2. A red line marks the explored relations in the visualized search context.
6 Acknowledgement
The research activities that have been described in this paper were funded by
Ghent University, iMinds (Interdisciplinary institute for Technology) a research
institute founded by the Flemish Government, Graz University of Technology,
the Institute for the Promotion of Innovation by Science and Technology in
Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders),
and the European Union. The authors would like to acknowledge the finan-
cial support of the “COMET K2 - Competence Centres for Excellent Technolo-
gies Programme” of the Austrian Federal Ministry for Transport, Innovation
and Technology (BMVIT), the Austrian Federal Ministry of Economy, Family
and Youth (BMWFJ), the Austrian Research Promotion Agency (FFG), the
Province of Styria and the Styrian Business Promotion Agency (SFG).
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