Querying the Web with Statistical Machine Learning

  • Volker Tresp
  • Yi Huang
  • Maximilian Nickel
Part of the Cognitive Technologies book series (COGTECH)


The traditional means of extracting information from the Web are keyword-based search and browsing. The Semantic Web adds structured information (i.e., semantic annotations and references) supporting both activities. One of the most interesting recent developments is Linked Open Data (LOD), where information is presented in the form of facts – often originating from published domain-specific databases – that can be accessed both by a human and a machine via specific query endpoints. In this article, we argue that machine learning provides a new way to query web data, in particular LOD, by analyzing and exploiting statistical regularities. We discuss challenges when applying machine learning to the Web and discuss the particular learning approaches we have been pursuing in THESEUS. We discuss a number of applications where the Web is queried via machine learning and describe several extensions to our approaches.


Machine Learning Disease Gene Link Prediction Deductive Reasoning Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Siemens AGMunichGermany
  2. 2.Ludwig Maximilian University MunichMunichGermany

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