Journal of Web Semantics. Special Issue on Dealing with the Messiness of the Web of Data

[Stefan]

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                         CALL FOR PAPERS
                      Journal of Web Semantics
     Special Issue on Dealing with the Messiness of the Web of Data

                     submission: February 2011
                      to appear: January 2012
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           (Guest editors: Stefan Schlobach, Craig A. Knoblock)


Description

Research on the Semantic Web, which is now in its second decade, has had
a tremendous success in encouraging people to publish data on the Web in
structured, linked, and standardized ways. The success of what has now
become the Web of Data can be read from the sheer number of triples 
available
within the Linked-Open Data, Linked Life Data and Open-Government 
initiatives.
However, this growth in data makes many of the established assumptions
inappropriate and offers a number of new research challenges.

In stark contrast to early Semantic Web applications that dealt with small,
hand-crafted ontologies and data-sets, the new Web of Data comes with a 
plethora
of contradicting world-views and contains incomplete, inconsistent, 
incorrect,
fast-changing and opinionated information. This information not only comes
from academic sources and trustworthy institutions, but is often community
built, scraped or translated.

In short: the Web of Data is messy, and methods to deal with this messiness
are paramount for its future.

For this special issue we seek articles describing foundational and 
theoretical
work as well as technological solutions for dealing with the messiness 
of the
Web of Data. More specifically, we expect submissions on (but not 
restricted to)
the following topics in the context of the Web of Data:

* Knowledge Representation in the presence of messy
     * Context and multi-dimensionality
     * Ontology and data versioning
     * Enforcing and encouraging conventions
     * Representation of uncertain, incomplete and inconsistent data
     * Emergent semantics and self-organizing behaviour

* Querying and reasoning over the messy Web of Data
     * Schemaless querying and integration
     * Dataspaces for the Web of Data
     * Federated querying
     * Reasoning over uncertain, incomplete and inconsistent data
     * Quantitative and statistical methods

* Data integration
     * Identify resolution and record linkage
     * Ontology Alignment
     * Bridging structured and unstructured data
     * Knowledge extraction from noisy data

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Important Dates

We will aim at an efficient publication cycle in order to guarantee prompt
availability of the published results. We will review papers on a rolling
basis as they are submitted and explicitly encourage submissions well before
the submission deadline. Submit papers online at the journal's Elsevier Web
site.

    Submission deadline: 1 February 2011
    Author notification: 15 June 2011
    Revisions submitted: 1 August 2011
    Final decisions: 15 September 2011
    Publication: 1 January 2012

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Instructions for submission:
*  The submission website for this journal is located at: 
http://ees.elsevier.com/jws
*  To ensure that all manuscripts are correctly identified for inclusion
    into the special issue you are editing, it is important that authors 
select
     "S.I.: Messiness of the Web of Data"
when they reach the "Article Type" step in the submission process.
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Guest Editors/Contacts
Stefan Schlobach (contact) -- Vrije Universiteit Amsterdam -- 
schlobac at few.vu.nl
Craig A. Knoblock -- University of Southern California -- knoblock at isi.edu





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Some additional information that we could make available on the web-site.

Some example problems that would be interesting for this special issue:
1) Similarity Search: Often users are interested in finding similar 
resources on the
  WoD. For example, find cities like Amsterdam or compare universities 
across Europe.
  Here, users may not be able to specifically identify the desired 
overlap. Instead,
  it is up to the query answering system to identify the overlap and 
supply reasonable
  answers.
2) Schemaless Query: One of the positive things about the WoD is the 
ability for data
  providers and consumers to use their preferred schema. However, this 
makes it difficult
  to query new data sources. Users must discover, which schema is used. 
Furthermore, it
  makes queries across data sources even more difficult because mappings 
between vocabularies
  must be available. We believe that approximation can help alleviate 
this problem by finding
  answers "close enough" to the posed query's schema.
3) Robust Query: Misspellings, misuse of vocabulary, violations of 
schema constraints,
  all these are part of daily life on the WoD. Today, technologies 
either skip over such
  data or must contain workarounds to deal with it. A systematic 
approach to dealing with
  these issues using approximation techniques, would provide a more 
usable WoD.
4) Aggregated Search Results: answers to more sophisticated queries do 
not reside all
  within one triple store. Only by aggregating facts from multiple 
stores can answers be
  provided. While federation can virtually provide a single triple 
store, it has limitations
  in terms of the consistency required across the underlying triple 
stores. We believe that
  approximation can provide a mechanism to enable more robust aggregated 
search results and
  federation.
5) Robust extraction: most data that is useful for the Web of Data is 
not build using
  Semantic technology but stems from traditional databases. Often this 
data is translated,
  or even scraped from Web Services or even html pages. Linking this 
information in well-
  understood and Semantically correct are crucial for the WoD.