=Paper=
{{Paper
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|pdfUrl=https://ceur-ws.org/Vol-1057/preface_LD4IE2013.pdf
|volume=Vol-1057
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<pdf width="1500px">https://ceur-ws.org/Vol-1057/preface_LD4IE2013.pdf</pdf>
<pre>
      LD4IE - Linked Data for Information Extraction



Overview

The World Wide Web provides access to tens of billions of pages, mostly containing
information that is largely unstructured and only intended for human readability. On
the other hand, the LOD provide billions of pieces of information linked together and
made available for automated processing. However, there is the lack of interconnection
between the information in the Web pages and that in LOD. A number of initiatives,
like RDFa (supported by W3C) or Microformats (used by schema.org and supported by
major search engines) are trying to enable machines to make sense of the information
contained in human readable pages by providing the ability to annotate webpage content
with links into LOD.
    This has created unprecedented opportunities for Web-scale Information Extraction.
First, it is the first time in the history of IE that a very large-scale information resource
(LOD) is available, covering a growing number of domains, to serve as learning materials.
Linked Data offers a uniform approach to link resources uniquely identifiable by URIs.
This creates a large knowledge base of entities and concepts, connected by semantic
relations. Such resources can be valuable seed data for IE tasks. Furthermore, the
annotated web pages can be considered as training data in the traditional machine
learning paradigm. The use of an uncontrolled and constantly evolving, community
provided set of independent Web resource for IE is totally untapped in the current state
of the art. Second, IE methods can also enhance web page annotation, which creates
further linked data.
    However, powering Web-scale IE using LOD faces major challenges. The first
requirement for Web-scale IE is discovering relevant learning materials on LOD. It
is non-trivial due to the highly heterogeneous vocabularies used by data publishers.
Users are often required to be familiar with the datasets, vocabularies, as well as query
languages that data publishers use to expose their data. Unfortunately, considering the
sheer size and the diversity of LOD, imposing such requirements on users is infeasible.
On the other hand, it is known that the coverage of domains can be very imbalanced and
for certain domains the data can be very sparse. For example, according to http://lod-
cloud.net, the ”Media”, ”Life Science”, ”Geographic” and ”Publications” domains
represent over 44% of the LOD. ”Cross-domain” datasets (e.g., encyclopedia-oriented)
represent only 13%. However, the relation between the quantity of training data and
learning accuracy follows a non-linear curve with diminishing returns. The majority
of LOD are created automatically by converting legacy databases with limited or no
human validation, thus errors are present. Similarly, community-provided resources and
annotations can contain errors, imprecision, spam, or even deviations from standards.
Also, very large and regular resources can be redundant, i.e. contain a large number
of instances that contribute little to the learning task, causing model overfitting, while
introducing considerable overhead.
    Addressing these challenges requires multi-field collaborative research effort cover-
ing various topics such as modeling IE tasks with respect to LD; efficient, large scale,
and robust learning algorithms able to scale and cope with noise; measures for assessing
learning material quality, and methods for selecting and optimizing training seeds.


Organising Committee

Anna Lisa Gentile, University of Sheffield, UK
Ziqi Zhang, University of Sheffield, UK
Claudia d’Amato, University of Bari, Italy
Heiko Paulheim, University of Mannheim, Germany



Program Committee
Rabeeh Ayaz Abbasi, Quaid-i-Azam University, Islamabad
Isabelle Augenstein, University of Sheffield, UK
Payam M. Barnaghi, University of Surrey, UK
Pierpaolo Basile, University of Bari, Italy
Chris Biemann, Technical University of Darmstadt, Germany
Chris Bizer, University of Mannheim, Germany
Eva Blomqvist, Linköping University, Sweden
Amparo Elizabeth Cano Basave, The Open University, UK
Fabio Ciravegna, University of Sheffield, UK
Ernesto William De Luca, University of Applied Sciences Potsdam, Germany
Nico Fanizzi, University of Bari, Italy
Christina Feilmayr, University of Linz, Austria
Georgi Georgiev, Ontotext AD, Bulgaria
Katja Hose, University of Aalborg, Denmark
Craig A. Knoblock, University of Southern California
Petr Knoth, Open University, UK
Vanessa Lopez, IBM Dublin, Ireland
Maria Teresa Pazienza, University of Roma ”Tor Vergata”, Italy
Thomas Roth-Berghofer, University of West London, UK
Andrea Varga, University of Sheffield, UK

</pre>