Semantics for Big Data [W3C late to semantic heterogeneity party]

Semantics for Big Data

Dates:

Submission due: May 24, 2013

Acceptance Notification: June 21, 2013

Camera-ready Copies: June 28, 2013

Symposium: November 15-17, 2013

From the webpage:

AAAI 2013 Fall Symposium; Westin Arlington Gateway in Arlington, Virginia, November 15-17, 2013.

Workshop Description and Scope

One of the key challenges in making use of Big Data lies in finding ways of dealing with heterogeneity, diversity, and complexity of the data, while its volume and velocity forbid solutions available for smaller datasets as based, e.g., on manual curation or manual integration of data. Semantic Web Technologies are meant to deal with these issues, and indeed since the advent of Linked Data a few years ago, they have become central to mainstream Semantic Web research and development. We can easily understand Linked Data as being a part of the greater Big Data landscape, as many of the challenges are the same. The linking component of Linked Data, however, puts an additional focus on the integration and conflation of data across multiple sources.

Workshop Topics

In this symposium, we will explore the many opportunities and challenges arising from transferring and adapting Semantic Web Technologies to the Big Data quest. Topics of interest focus explicitly on the interplay of Semantics and Big Data, and include:

  • the use of semantic metadata and ontologies for Big Data,
  • the use of formal and informal semantics,
  • the integration and interplay of deductive (semantic) and statistical methods,
  • methods to establish semantic interoperability between data sources
  • ways of dealing with semantic heterogeneity,
  • scalability of Semantic Web methods and tools, and
  • semantic approaches to the explication of requirements from eScience applications.

The W3C is late to the party as evidenced by semantic heterogeneity becoming “…central to mainstream Semantic Web research and development” after the advent of Linked Data.

I suppose better late than never.

At least if they remember that:

Users experience semantic heterogeneity in data and in the means used to describe and store data.

Whatever solution is crafted, its starting premise must be to capture semantics as seen by some defined user.

Otherwise, it is capturing the semantics of designers, authors, etc., which may or may not be valuable to some particular user.

RDF is a good example of capturing someone else’s semantics.

As its uptake is evidence of the interest in someone else’s semantics. (Simple Web Semantics – The Semantic Web Is Failing — But Why?)

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