Call for Papers: Special issue on Linked Data for Information Extraction

Call for papers: Special Issue on

Linked Data for Information Extraction

Information Extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. It is a crucial technology to enable the Semantic Web vision.

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, traditionally are manually created but 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 created a gigantic knowledge source of Linked Open Data (LOD), which now constitutes billions of triples (facts). This has created unprecedented opportunities for Information Extraction. Linked Data offers a uniform approach to link resources uniquely identifiable by URIs. This creates a large knowledge base of entities and concepts, connected by semantic relations. Such resources can be valuable resources to seed distant learning. Moreover, initiatives such as RDFa (supported by W3C) or microdata format (used by and supported by major search engines) constantly produce a vast amount of annotated web pages which can be used as training data in the traditional machine learning paradigm.

However, powering IE using LOD faces major challenges. First, discovering relevant learning materials on LOD for specific IE tasks is non-trivial due to (i) the highly heterogeneous vocabularies used by data publishers and (ii) the lack of contextual information for annotated content on web pages (e.g., annotations often predominantly found in page headers) and the skewed distribution towards popular entities. Users are often required to be familiar with the datasets, vocabularies, as well as query languages that data publishers use to expose their data. Unfortunately, considering the sheer size and the diversity of LOD, imposing such requirements on users is infeasible. Second, it is known that the coverage of domains can be very imbalanced and for certain domains the data can be very sparse. Furthermore, the majority of LOD are created automatically by converting legacy databases with limited or no human validation, thus data inconsistency and redundancy are widespread.

Another crucial aspect in IE research is the shift of attention from purely unstructured text to semi-structured content. Two main source of interest are Web tables and Open Data (often available as csv files). These data are particularly rich of content and relations but often lack contextual data, often used in classical IE methods.

The aim of this special issue is to foster research on methodologies that exploit Linked Data for Information Extraction, to answer questions such as: to what extent can we identify domain-specific learning resources for IE; how to identify and deal with noise in the learning resources; how can these learning resources be used to train IE models, both for classical unstructured text and for semi-structured content; and how should the information extracted by such models integrate into the existing LOD.


We solicit original papers addressing the challenges and research questions mentioned above. Topics of interest are listed (but not limited to) the ones below. Note that work must make use of Linked Data of any form and must be related to Information Extraction in some way. Please contact the editors if in doubt.

  • Methods for generating seed data for IE (e.g., distant supervision) from Linked Data
  • Methods for identifying labelled data for IE from the annotated webpage content under the initiative such as RDFa and Microdata format (
  • IE tasks exploiting Linked Data in any form, such as (not limited to)
    • wrapper induction
    • table annotation
    • named entity recognition
    • relation extraction
    • ontology population, ontology expansion (A-box)
    • ontology learning (T-box)
  • Methods for identifying and reducing noise in the context of IE tasks
  • Disambiguation using Linked Data
  • IE for knowledge graph construction


  • Submission deadline: 05 May 2017. Papers submitted before the deadline will be reviewed upon receipt.

Submission Instructions

Submissions shall be made through the Semantic Web journal website at Prospective authors must take notice of the submission guidelines posted at Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Linked Data for Information Extraction" special issue.

All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process.

Guest editors

Anna Lisa Gentile, University of Mannheim, Germany
Ziqi Zhang, Nottingham Trent University, UK

The guest editors can be contacted at

Guest editorial board

  • Nitish Aggarwal, IBM
  • José Luis Ambite, University of Southern California
  • Claudia D'Amato, University of Bari
  • Brian Davis, Insight Centre for Data Analytics, Galway
  • Anca Dumitrache, VU University Amsterdam
  • Ashutosh Jadhav, IBM
  • Craig Knoblock, University of Southern California
  • Tobias Kuhn, VU University Amsterdam
  • Varish Mulwad, GE Global Research
  • Andrea Giovanni Nuzzolese, STLab, ISTC-CNR
  • Simone Paolo Ponzetto, University of Mannheim
  • Jay Pujara, University of Maryland
  • Achim Rettinger, AIFB
  • Giuseppe Rizzo, ISMB
  • Monika Solanki, University of Oxford

The guest editorial board will be recruited based on the topics of the submitted papers.