CFP: Semantic Web Journal - Special Issue on Linked Data for Information Extraction

*---------------------------
SEMANTIC WEB JOURNAL  - Call for papers: SPECIAL ISSUE ON Linked Data 
for Information Extraction
---------------------------
*Submission deadline: 07 April 2017, Hawaii-Time
--------------------*

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 
Microformats (used by schema.org 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.


Topics of Interest
------------------------------
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 
(schema.org)
- 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 Instructions
-----------------------------

Submissions shall be made through the Semantic Web journal website at 
http://www.semantic-web-journal.net. Prospective authors must take 
notice of the submission guidelines posted at 
http://www.semantic-web-journal.net/authors. 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 call is also available at the official journal website: 
http://www.semantic-web-journal.net/blog/call-papers-special-issue-linked-data-information-extraction

*

-- 
Anna Lisa Gentile
Postdoctoral Researcher
Data and Web Science Group
University of Mannheim
https://w3id.org/people/annalisa
email:annalisa@informatik.uni-mannheim.de
office: +49 621 181 2646
skype: anlige