Learning Knowledge Bases for Text and Multimedia
Lexing Xie, The Australian National University
Haixun Wang, Google Research
ACM Multimedia 2014 Tutorial, Movember 3, 2014
Part I: Leanring knowledge bases from text (download pdf, 16MB),
Part II: Knowledge graphs for images and multimedia (download pdf, 40 MB).
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Knowledge acquisition, representation, and reasoning has been one of the long-standing challenges in artificial intelli- gence and related application areas. Only in the past few years, massive amounts of structured and semi-structured data that directly or indirectly encode human knowledge be- came widely available, turning the knowledge representation problems into a computational grand challenge with feasible solutions in sight. The research and development on knowl- edge bases is becoming a lively fusion area among web in- formation extraction, machine learning, databases and infor- mation retrieval, with knowledge over images and multime- dia emerging as another new frontier of representation and acquisition. This tutorial aims to present a gentle overview of knowledge bases on text and multimedia, including rep- resentation, acquisition, and inference. The content of this tutorial are intended for surveying the field, as well as for educating practitioners and aspiring researchers.
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This tutorial is structured in two main parts. The first part introduces the background and foundations of knowledge bases, and focuses on knowledge acquisition from text and hyper-text data, the second part discusses ac- quiring and applying knowledge for image and multimedia collections. Tutorial materials and an an- notated bibliography will be released online.
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November 2014
Contact: Lexing Xie
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