Data Mining in Unusual Domains with Information-rich Knowledge Graph Construction, Inference and Search

author: Pedro Szekely, Information Sciences Institute (ISI), University of Southern California
author: Mayank Kejriwal, Information Sciences Institute (ISI), University of Southern California
published: Nov. 21, 2017,   recorded: August 2017,   views: 999
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Description

The growth of the Web is a success story that has spurred much research in knowledge discovery and data mining. Data mining over Web domains that are unusual is an even harder problem. There are several factors that make a domain unusual. In particular, such domains have significant long tails and exhibit concept drift, and are characterized by high levels of heterogeneity. Notable examples of unusual Web domains include both illicit domains, such as human trafficking advertising, illegal weapons sales, counterfeit goods transactions, patent trolling and cyberattacks, and also non-illicit domains such as humanitarian and disaster relief. Data mining in such domains has the potential for widespread social impact, and is also very challenging technically. In this tutorial, we provide an overview, using demos, examples and case studies, of the research landscape for data mining in unusual domains, including recent work that has achieved state-of-the-art results in constructing knowledge graphs in a variety of unusual domains, followed by inference and search using both command line and graphical interfaces.

Link to tutorial: http://usc-isi-i2.github.io/KDD17/

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