AKBC 2017 6th Workshop on Automated Knowledge Base Construction (AKBC) at NIPS 2017
December 8, 2017, Long Beach, California http://www.akbc.ws Knowledge Base Construction Extracting knowledge from text, images, and video and translating these extractions into a coherent, structured knowledge base (KB) is a task that spans the areas of machine learning, natural language processing, computer vision, databases, search, data mining and artificial intelligence. Recent years have seen significant advances here, both in academia and 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 <http://videolectures.net/akbc2010_grenoble/>, AKBC 2012 <https://akbcwekex2012.wordpress.com/>, AKBC 2013 <http://www.akbc.ws/2013/>, AKBC 2014 <http://www.akbc.ws/2014/> and AKBC 2016 <http://www.akbc.ws/2016/> each featured many invited talks from leaders in this area from academia, industry, and government agencies. We had senior invited speakers from Google, Microsoft, Facebook, several leading universities (MIT, Stanford, University of Washington, CMU, University of Massachusetts, and more), and DARPA. With this year’s workshop, we aim to resume this positive experience. By 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. Call For Papers We welcome papers documenting previously unpublished research; ongoing and exciting preliminary work is perfectly fine. We are particularly interested in visionary paper submissions. We aim for papers that express intriguing and promising ideas -- focusing less on where science is today and more on where it should go tomorrow. Topics of interest include, but are not limited to: - machine learning on text; unsupervised, lightly-supervised and distantly-supervised learning; learning from naturally-available data - deep learning for representing knowledge bases - 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 - question-answering using KBs, 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 In addition, for the first time, AKBC will address a longstanding issue in the AKBC, that of equitable comparison and evaluation across methods. We encourage people to use the KBP Online platform (https://kbpo.stanford.edu/), a new effort to standardize KB population/construction evaluation, for their system evaluation for paper submissions. The platform automates the annotation of kb output and is made available for free for AKBC participants. For further details or questions, visit kbpo.stanford.edu or email chaga...@stanford.edu. Invited Talks Xin Luna Dong (Amazon) Tom Mitchell (Carnegie Mellon University) Maximilian Nickel (Facebook AI Research) Sebastian Riedel (Bloomsbury AI / University College London) Sameer Singh (University of California, Irvine) Ivan Titov (University of Edinburgh) Luke Zettlemoyer (University of Washington / Allen Institute for Artificial Intelligence) Submission Please format your papers using the standard NIPS 2017 style files <https://nips.cc/Conferences/2017/PaperInformation/StyleFiles>, and restrict them to 5 pages (excluding references). Since the reviewing will not be double-blind, include author information and the \nipsfinalcopy flag. Note that you don't have to include a separate Abstract section in the submission. All accepted papers will be presented as posters, with exceptional submissions also presented as oral talks. Style files: https://nips.cc/Conferences/2017/PaperInformation/StyleFiles Submission site: https://easychair.org/conferences/?conf=akbc2017 AKBC Student Travel Award We are happy to announce that this year we are able to award free workshop registration to exceptional student applicants. Please send your CV and a short cover letter explaining why you need the travel award, your research interests and how they align with AKBC to i...@akbc.ws. Important Dates Submission Due: October 21, 2017 AKBC Student Travel Award Application Due: October 22, 2017 Notification: November 5, 2017 Camera-ready Due: November 12, 2017 Workshop: December 8, 2017 Deadlines are at 11:59pm PDT and subject to change. Organizers - Jay Pujara <https://cs.umd.edu/~jay/>, Information Sciences Institute, USA - Danqi Chen <http://cs.stanford.edu/~danqi/>, Stanford University, USA - Tim Rocktäschel <http://rockt.github.com/>, University of Oxford, UK - Bhavana Dalvi <http://allenai.org/team/bhavanad/>, Allen Institute for Artificial Intelligence, USA For any questions, please mail i...@akbc.ws.
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