CAF-SIAL: Concept Aggregation Framework for Structuring
Informational Aspects of Linked Open Data


Atif Latif1,2, Muhammad Tanvir Afzal3, Anwar Us Saeed1,4, Patrick Hoefler5,6, and
Klaus Tochtermann1,5,7

{1Institute for Knowledge Management , 5Know-Center}
Graz University of Technology, Inffeldgasse 21a, 8010 Graz, Austria,
{2atif.latif, 4anwar.ussaeed }@student.TUGraz.at, 6phoefler@know-center.at,
7klaus.tochtermann@TUGraz.at
3Institute for Information Systems and Computer Media (IICM)
Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria
mafzal@iicm.edu


Abstract

Linked Open Data (LOD) is becoming an
essential part of the Semantic Web. Although LOD
has amassed large quantities of structured data from
diverse, openly available data sources, there is still a
lack of user-friendly interfaces and mechanisms for
exploring this huge resource. In this paper we
highlight two critical issues related to the
exploration of the semantic LOD pool by end users.
We introduce a proof of concept application which
helps users to search information about a concept
without having to know the mechanics of the
Semantic Web or Linked Data. We assume that this
kind of application may lead to bridge the gap
between semantic search and end users. With this
application, we concentrated on two aspects:
1) A novel Concept Aggregation Framework to
present the most relevant information of LOD
resources in an easy to understand way.
2) A simplified keyword search mechanism
which hides the complex underlying semantic search
logic.
This research is intended to simplify the LOD end
user interfaces so that they may be used by novice
users who don't possess any prior knowledge of
semantic structures.

1. Introduction

The World Wide Web can be considered as a
huge repository of networked resources. Due to the
exponential growth of World Wide Web, it is a
challenging task for search engines to locate
meaningful pieces of information from heavily
redundant and unstructured resources. The semantic
paradigm of information processing suggests a
solution to the above problem. Semantic resources
are structured, and related semantic metadata can be
used to query and search the required piece of
information precisely. On the other hand, the bulk of
the data currently residing on the Web is
unstructured or semi-structured at best. Therefore,
the W3C SWEO launched the Linking Open Data1
movement, a community effort that motivates people
to publish their information in a structured way
(RDF)2. LOD not only “semantifies” different kind
of open data sets but also provides a framework for
the interlinking of these datasets. This framework is
based on the rules described by Tim Berners-Lee [1].
As of May 2009, the LOD cloud consists of over 4.7
billion RDF triples, which are interlinked by around
142 million RDF/OWL links. Although LOD has
gained huge volumes of data and has attracted
attention of many researchers, it still lacks broad
recognition, especially in commercial domains. This
is, amongst other reasons, because of complex
semantic search and end user applications [2].
In the absence of official standards, DBpedia3 and
Yago4 are considered de facto standards for
classification. DBpedia is also considered a hub of
linked data for interlinking and finding facts. Facts
about a specific resource, extracted from the
infoboxes of Wikipedia, are structured in the form of
properties defined in DBpedia's ontology. This

1http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/
LinkingOpenData
2 http://www.w3.org/RDF/
3 http://dbpedia.org
4 http://www.mpi-inf.mpg.de/yago-naga/yago/