LD4IE 2013 - The 1st international Workshop on Linked Data for Information Extraction

This workshop focuses on the exploitation of Linked Data for Web Scale Information Extraction (IE), which
concerns extracting structured knowledge from unstructured/semi-structured documents on the Web.
One of the major bottlenecks for the current state of the art in IE is the availability of learning
materials (e.g., seed data, training corpora), which, typically are manually created and are expensive
to build and maintain.
Linked Data (LD) defines best practices for exposing, sharing, and connecting data, information, and
knowledge on the Semantic Web using uniform means such as URIs and RDF. It has so far been created a gigantic
knowledge source of Linked Open Data (LOD), which constitutes a mine of learning materials for IE.
However, the massive quantity requires efficient learning algorithms and the unguaranteed quality of
data requires robust methods to handle redundancy and noise.
LD4IE intends to gather researchers and practitioners to address multiple challenges arising from the
usage of LD as learning material for IE tasks, focusing on (i) modelling user defined extraction tasks
using LD; (ii) gathering learning materials from LD assuring quality (training data selection,
cleaning, feature selection etc.); (iii) robust learning algorithms for handling LD; (iv) publishing
IE results to the LOD cloud.
************************* Topics*******
Topics of interest include, but are not limited to:
***** Modelling Extraction Tasks
* modeling extraction tasks
* extracting knowledge patterns for task modeling
* user friendly approaches for querying linked data
***** Information Extraction
* selecting relevant portions of LOD as training data
* selecting relevant knowledge resources from linked data
* IE methods robust to noise in training data
* Information Extractions tasks/applications exploiting LOD (Wrapper induction, Table
interpretation, IE from unstructured data, Named Entity Recognition...)
* publishing information extraction results as Linked Data
* linking extracted information to existing LOD datasets
***** Linked Data for Learning
* assessing the quality of LOD data for training
* select optimal subset of LOD to seed learning
* managing incompleteness, noise, and uncertainty of LOD
* scalable learning methods
* pattern extraction from LOD