Workshop
4th Workshop on Automated Knowledge Base Construction (AKBC)
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin P Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason E Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark

Sat Dec 13th 08:30 AM -- 06:30 PM @ Level 5; room 515
Event URL: http://akbc.ws »

Goal:

Extracting knowledge from Web pages, and integrating it into a coherent knowledge base (KB) is a task that spans the areas of natural language processing, information extraction, information integration, databases, search, and machine learning. Recent years have seen significant advances here, both in academia and in the industry. Most prominently, all major search engine providers (Yahoo!, Microsoft Bing, and Google) nowadays experiment with semantic KBs. Our workshop serves as a forum for researchers on knowledge base construction in both academia and industry.

Unlike many other workshops, our workshop puts less emphasis on conventional paper submissions and presentations, but more on visionary papers and discussions. In addition, one of its unique characteristics is that it is centered on keynotes by high-profile speakers. AKBC 2010, AKBC 2012, and AKBC 2013 each had a dozen invited talks from leaders in this area from academia, industry, and government agencies. We had senior invited speakers from Google, Microsoft, Yahoo, several leading universities (MIT, University of Washington, CMU, University of Massachusetts, and more), and DARPA. With this year’s proposal, we would like to resume this positive experience. By inviting established researchers for keynotes, and by focusing particularly on vision paper submissions, we aim to provide a vivid forum of discussion about the field of automated knowledge base construction.

Topics of interest:

* machine learning on text; unsupervised, lightly-supervised and distantly-supervised learning; learning from naturally-available data.
* human-computer collaboration in knowledge base construction; automated population of wikis.
* inference for graphical models and structured prediction; scalable approximate inference.
* information extraction; open information extraction, named entity extraction; ontology construction;
* entity resolution, relation extraction, information integration; schema alignment; ontology alignment; monolingual alignment, alignment between knowledge bases and text;
* pattern analysis, semantic analysis of natural language, reading the web, learning by reading
databases; distributed information systems; probabilistic databases;
* scalable computation; distributed computation.
* queries on mixtures of structured and unstructured data; querying under uncertainty;
* dynamic data, online/on-the-fly adaptation of knowledge.
* languages, toolkits and systems for automated knowledge base construction.
* demonstrations of existing automatically-built knowledge bases.

Audience:

AKBC 2012 and AKBC 2013 attracted 70-100 participants. These were researchers from academia, industry, and government, as well as students. The workshop brought together people from the areas of natural language processing, machine learning, and information extraction. We would expect a similar composition of the audience also for AKBC 2014. Since our keynote talks are given by very senior researchers (usually the coordinators of entire scientific projects), the talks are usually high-level and easily understandable. Therefore, we are confident that the workshop will be of interest also to novices in the area or first year students, who wish to get an overview of the automated KB construction. At the same time, the high calibre of our speakers is almost certain to attract established researchers who wish to get a survey of the latest developments in the field. The vision papers, too, play their role in attracting the audience, as these papers are deliberately designed to provoke thought and discussion from domain experts and novices to the field alike.

Author Information

Sameer Singh (University of California, Irvine)
Fabian M Suchanek (Paris-Saclay University)
Sebastian Riedel (University College London)
Partha Pratim Talukdar (Indian Institute of Science (IISc))
Kevin P Murphy (Google)
Chris Ré (Stanford)
William Cohen (Google AI)
Tom Mitchell (Carnegie Mellon University)
Andrew McCallum (UMass Amherst)
Jason E Weston (Facebook AI Research)

Jason Weston received a PhD. (2000) from Royal Holloway, University of London under the supervision of Vladimir Vapnik. From 2000 to 2002, he was a researcher at Biowulf technologies, New York, applying machine learning to bioinformatics. From 2002 to 2003 he was a research scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2004 to June 2009 he was a research staff member at NEC Labs America, Princeton. From July 2009 onwards he has been a research scientist at Google, New York. Jason Weston's current research focuses on various aspects of statistical machine learning and its applications, particularly in text and images.

Ramanathan Guha (Google)
Boyan Onyshkevych (DARPA)
Hoifung Poon (Microsoft Research)
Oren Etzioni (University of Washington)
Ari Kobren (UMass Amherst)
Arvind Neelakantan (Google)
Peter Clark (Allen Institute for AI)

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