ISWC 2013 Workshop, Oct 21 2013, Sydney, Australia

Call for Papers

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.

We welcome paper submissions with topics related to Information Extraction
using Linked Data, such as :

Modeling Extraction Tasks

  • modelling extraction tasks (e.g. defining IE templates using LD ontologies)
  • extracting and building knowledge patterns based on LD
  • user friendly approaches for querying linked data

Information Extraction

  • selecting relevant portions of LD as training data
  • selecting relevant knowledge resources from LD
  • IE methods robust to noise in LD as training data
  • Information Extractions tasks/applications exploiting LD (Wrapper induction, Table interpretation, IE from unstructured data, Named Entity Recognition, Relation Extraction...)
  • linking extracted information to existing LD datasets

Linked Data for Learning

  • assessing the quality of LD data for training
  • select optimal subset of LD to seed learning
  • managing heterogeneity, incompleteness, noise, and uncertainty of LD
  • scalable learning methods using LD
  • pattern extraction from LD

Format

We accept the following formats of submissions:

  • Full paper with a maximum of 12 pages including references
  • Short paper with a maximum of 6 pages including references
  • Poster with a maximum of 4 pages including references

All research submissions must be in English. Submissions must be in PDF formatted in the style of the Springer Publications format for Lecture Notes in Computer Science (LNCS). Submissions are not anonymous.

Accepted papers will be published online via CEUR-WS.