- From: Anna Lisa Gentile <a.l.gentile@dcs.shef.ac.uk>
- Date: Wed, 10 Jul 2013 23:14:59 +0100
- To: public-lod@w3c.org
Apologies for multiple posting. Deadline extension: submission accepted until the 19th of July, please submit your abstract as soon as possible! *********************************************** LD4IE 2013 The 1st international Workshop on Linked Data for Information Extraction Sydney, Australia, October 21 -22, 2013 Workshop website: http://oak.dcs.shef.ac.uk/ld4ie2013/index.html Twitter: @LD4IE2013 #LD4IE #LD4IE2013 Facebook page: https://www.facebook.com/Ld4ie2013 in conjunction with ISWC 2013 The 12th International Semantic Web Conference Sydney, Australia, October 21 -25, 2013 http://iswc2013.semanticweb.org/ *************** Important Dates - NEW!!! *************** Abstract submission deadline: Submit your abstract ASAP! Paper submission deadline: July 19, 2013 Acceptance Notification: August 9, 2013 Camera-ready versions: to be announced Workshop date: to be announced (21-22 October 2013) *************** 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 not guaranteed 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 * modelling extraction tasks (e.g. defining IE templates using LD ontologies) * extracting and building knowledge patterns based on LD * user friendly approaches for querying LD *** 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 *************** Submission ******************** 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 submissions must be written in English and must be formatted according to the information for LNCS Authors (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.). Please submit your contributions electronically in PDF format to EasyChair at https://www.easychair.org/conferences/?conf=ld4ie Accepted papers will be published online via CEUR-WS. *************** Workshop Chairs *************** 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 -- Anna Lisa Gentile Research Associate Department of Computer Science University of Sheffield http://staffwww.dcs.shef.ac.uk/people/A.L.Gentile office: +44 (0)114 222 1876
Received on Wednesday, 10 July 2013 22:15:31 UTC