- From: Heiko Paulheim <heiko@informatik.uni-mannheim.de>
- Date: Thu, 25 Apr 2013 13:00:13 +0200
- To: semantic-web@w3.org, public-lod@w3.org, dbpedia-discussion@lists.sourceforge.net
- Message-ID: <51790CBD.5010802@informatik.uni-mannheim.de>
Apologise for multiple posting. ******************************************************************************* LD4IE 2013 The 1st international Workshop on Linked Data for Information Extraction Sidney, Australia, October 21 -22, 2013 Workshop website:http://oak.dcs.shef.ac.uk/ld4ie2013/index.html Twitter: <at> LD4IE2013 #LD4IE #LD4IE2013 Facebook page:https://www.facebook.com/Ld4ie2013 in conjunction with ISWC 2013 The 12th International Semantic Web Conference Sidney, Australia, October 21 -25, 2013 http://iswc2013.semanticweb.org/ ************************* 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. ************************* 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 ************************* Important Dates************************* Abstract submission deadline: July 5, 2013 Paper submission deadline: July 12, 2013 Acceptance Notification: August 9, 2013 Camera-ready versions: to be announced Workshop date: to be announced (21-22 October 2013) ************************* 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 athttps://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
Received on Thursday, 25 April 2013 11:01:15 UTC