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2016 Volume 31
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RESEARCH ARTICLE   Open Access    

Data mining for building knowledge bases: techniques, architectures and applications

  • School of Computer Science and Engineering

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  • Abstract: Data mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we call knowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.
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  • Cite this article

    Alfred Krzywicki, Wayne Wobcke, Michael Bain, John Calvo Martinez, Paul Compton. 2016. Data mining for building knowledge bases: techniques, architectures and applications. The Knowledge Engineering Review 31(2)97−123, doi: 10.1017/S0269888916000047
    Alfred Krzywicki, Wayne Wobcke, Michael Bain, John Calvo Martinez, Paul Compton. 2016. Data mining for building knowledge bases: techniques, architectures and applications. The Knowledge Engineering Review 31(2)97−123, doi: 10.1017/S0269888916000047
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RESEARCH ARTICLE   Open Access    

Data mining for building knowledge bases: techniques, architectures and applications

  • School of Computer Science and Engineering

  • Published online: 31 March 2016
The Knowledge Engineering Review  31,  2016, 31(2): 97−123  |  Cite this article

Abstract: Abstract: Data mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have been proposed for what we call knowledge mining: integrating data mining for knowledge extraction from unstructured text (possibly making use of a knowledge base), and at the same time, consistently incorporating this new information into the knowledge base. After describing a number of existing knowledge mining systems, we review the state-of-the-art literature on both current text mining methods (emphasising stream mining) and techniques for the construction and maintenance of knowledge bases. In particular, we focus on mining entities and relations from unstructured text data sources, entity disambiguation, entity linking and question answering. We conclude by highlighting general trends in knowledge mining research and identifying problems that require further research to enable more extensive use of knowledge bases.

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    Acknowledgments

    • This work was supported by Data to Decisions Cooperative Research Centre.

    • http://googleblog.blogspot.com.au/2012/05/introducing-knowledge-graph-things-not.html

    • http://www.cis.upenn.edu/~ccb/ppdb/

    • Muppet has been released as open-source software under the name of Mupd8.

    • http://www.cmu.edu/homepage/computing/2010/fall/nell-computer-that-learns.shtml

    • Severe Acute Respiratory Syndrome, a viral disease with flu-like symptoms, an outbreak of which occurred in southern China between November 2002 and July 2003, resulting in numerous deaths.

    • http://google-opensource.blogspot.com.au/2014/06/cayley-graphs-in-go.html

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    • http://sourceforge.net/projects/zpar/

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    Alfred Krzywicki, Wayne Wobcke, Michael Bain, John Calvo Martinez, Paul Compton. 2016. Data mining for building knowledge bases: techniques, architectures and applications. The Knowledge Engineering Review 31(2)97−123, doi: 10.1017/S0269888916000047
    Alfred Krzywicki, Wayne Wobcke, Michael Bain, John Calvo Martinez, Paul Compton. 2016. Data mining for building knowledge bases: techniques, architectures and applications. The Knowledge Engineering Review 31(2)97−123, doi: 10.1017/S0269888916000047
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