posted by organizer: andriry || 4076 views || tracked by 8 users: [display]

LD4IE 2017 : Linked Data for Information Extraction


When Oct 21, 2017 - Oct 25, 2017
Where Vienna, Austria
Abstract Registration Due Jul 14, 2017
Submission Deadline Jul 21, 2017
Notification Due Aug 24, 2017
Final Version Due Sep 1, 2017
Categories    linked data   semantic web   information extraction   knowledge extraction

Call For Papers

LD4IE 2017
The 5th international Workshop on Linked Data for Information Extraction
in conjunction with the 16th International Semantic Web Conference (ISWC 2017)
Vienna, Austria, October 21-25, 2017

Workshop website:
Twitter: @LD4IE #LD4IE #LD4IE2017
Facebook page: Ld4ie2017 (at

*************** Important Dates ***************

Abstract submission deadline: July 14, 2017
Paper submission deadline: July 21, 2017
Acceptance Notification: August 24, 2017
Camera-ready versions: September 1, 2017
Workshop date: to be announced (October 21-22, 2017)

*************** 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.); (ii) robust learning algorithms for handling LD; (iv) publishing IE results to the LOD cloud.

*************** Research Topics ***************

Topics of interest include, but are not limited to:

* Modelling Extraction Tasks
** extracting knowledge patterns for task modelling
** 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, …)
** Domain specific IE consuming and producing LOD (social data, scholarly data, health data, ...)
** 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

*************** Submission ********************

All submissions must be written in English.
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.

Two formats are possible for the submission: PDF and HTML.

PDF submissions must be formatted according to the information for LNCS Authors (

We would like to encourage you to submit your paper as HTML, in which case you need to submit a zip archive containing an HTML file and all used resources.
If you are new to HTML submission these are good places to start:
* dokieli ( is a clientside editor for decentralised article publishing, annotations and social interactions. It is compliant with the Linked Research ( initiative. Example papers using LNCS and ACM: and on website
* Research Articles in Simplified HTML (RASH) format: documentation and stylesheets at

In order to check if your HTML submission is compliant with the page limit constraint, please use one of the LNCS layouts and printing/storing it as PDF.

Please submit your contributions electronically in PDF or HTML format to EasyChair at
When submitting your paper, select the appropriate topic between:
* Research - long paper
* Research - short paper

Accepted papers will be published online via CEUR-WS.

*************** Workshop Chairs ***************

Anna Lisa Gentile, IBM Research Almaden, US
Andrea Giovanni Nuzzolese, STLab, ISTC-CNR, Italy
Ziqi Zhang, Nottingham Trent University, UK

Related Resources

SITAIBA 2021   SITAIBA 2021 : 2021 International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications
KEOD 2021   13th International Conference on Knowledge Engineering and Ontology Development
DMCIT 2022   2022 6th International Conference on Data Mining, Communications and Information Technology (DMCIT 2022)
KGSWC 2021   3rd Iberoamerican Knowledge Graph and Semantic Web Conference
AIBC--Ei Compendex and Scopus 2022   2022 3rd International Artificial Intelligence and Blockchain Conference (AIBC 2022)--Ei Compendex, Scopus
IWSW 2021   4th International Workshop on Semantic Web
Chengdu--DMCIT 2022   2022 6th International Conference on Data Mining, Communications and Information Technology (DMCIT 2022)
ARIMA 2021   Special Issue Data Intelligibility, Business Intelligence and Semantic Web
APIT--EI Compendex, Scopus 2022   2022 4th Asia Pacific Information Technology Conference (APIT 2022)--Ei Compendex, Scopus
KaRS 2021   3rd Knowledge-aware and Conversational Recommender Systems Workshop 2021 @ ACM RecSys