Learn how can you combine Knowledge Graphs and Deep Learning to dramatically improve Search & Discovery systems, just like KZitem does. By using a combination of signals (audiovisual content, title & description and context), it is possible to find the main topics of a video. These topics can then be used to improve collaborative filtering, search, structured browsing (by exploiting the structure of the knowledge graph), ads, and much more.
Slides: www.slideshare.net/aurelienge...
Filmed at 2018.dotai.io on May 31st in Paris. Big thanks to the Dot AI team! More talks on www.dotconferences.com/talks
Google is very open about its research. Everything I talked about in this talk (and much more!) is available in many papers, blogs and talks. Here are a few pointers if you want to learn more:
* "Deep Neural Networks for KZitem Recommendations" paper by Paul Covington, Jay Adams and Emre Sargin (2016): research.google.com/pubs/arch...
* "Large-scale Video Classification with Convolutional Neural Networks", Karpathy et al.(2014): cs.stanford.edu/people/karpat...
* Google I/O 2013 on KZitem video annotations: • Google I/O 2013 - Sema...
* "Classifying KZitem Channels: a Practical System", Vincent Simonet (2013): research.google.com/pubs/arch...
* Wikidata: wikidata.org/
* Java framework for building Semantic Web and Linked Data applications: jena.apache.org/
* SPARQL tutorial: jena.apache.org/tutorials/spa...
* List of papers on Entity Recognition using Deep Learning: memkite.com/deeplearningkit/2...
* Extracting information from text using the NLTK library (python): www.nltk.org/book/ch07.html
* Project that lists Entity Linking tools and evaluates them on various datasets: aksw.org/Projects/GERBIL.html
Kudos to the KZitem Legos team in Paris, the VCA team in Mountainview, the KZitem Recommendation team in San Bruno, and to all the other teams I had such pleasure working with at KZitem and Google! :)
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