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Connecting the Knowledge Ecosystem Founded in 2019 at Columbia University, The Knowledge Graphs Conference is emerging as the premiere source of learning around knowledge graph technologies. We believe knowledge graphs are an underutilized yet essential force for solving complex societal challenges like climate change, democratizing access to knowledge and opportunity, and capturing business value made possible by the AI revolution.
KGC bridges the gap between industry, which is increasingly recognizing the necessity of integrated data, and academia, where semantic technologies have been developing for over twenty years. Our events, education, content, and community efforts facilitate meaningful exchange between diverse groups, and increase awareness, development and adoption of this powerful technology.
Conference – bridging the gap between research and industry
We organize workshops and tutorials to progress a number of Tech4Good themes, targeting objectives such as the United Nations Sustainable Development Goals and the development of a COVID-19 vaccine. At our most recent conference, 530 attendees participated, representing over thirty industries across forty-two countries. Speakers ranged from Bell Labs pioneer John Sowa to Morgan Stanley, AstraZeneca, and leading academics from Europe and USA. A variety of workshops and tutorials were also given, including several on tech4good themes–from the UN SDGs to personal health graphs and fake news.
KGC Vision and Values
Our goal is to build the community and become a leading source of learning around knowledge graphs.
We will achieve this by engaging and convening industry leaders and innovators, across sectors.
We will focus on the diversity of perspectives:
Professional Diversity: Industry practitioners, Business Users, Faculty, Scientists, Students
Gender & Age diversity
We will gather, share and publish content to increase learning.
We will build the community online and in-person through our content, meetups and conferences.
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Machine Learning in Yahoo Knowledge Graph
The Yahoo Knowledge(YK) graph crawls, reconciles and blends information (around 10B fact triples) from 200 M entities across 30 semi-structured source (crawlable sites like Wikipedia, IMDB, LonelyPlanet etc and as well licensed feeds) graphs to a merged graph of 75 M entities, 5B facts distributed across 140 entity types and 300 attributes. From classifying entity type of source entities, to reconcile entities across sources (e.g. Brad Pitt from Wikipedia vs. Brad Pitt from IMDB), and blending conflicting and complementing facts for each entity from different sources, the YK graph encapsulates production scale machine learning solutions for multi-label classification(e.g. predicted entity types for Arnold Schwarzenegger could be Actor, Politician, BusinessPerson etc ), large scale high precision binary classifiers along with an array of distributed hashing techniques help scale a potential billion edge comparisons (de-duplication of entities across sources require high precision classifiers for which we develop active learning and precision clamped training strategies) and lastly hubs and authorities based fact blending from competing sources. To support product initiatives like surfacing knowledge augmented results on web and sponsored searches we build a variety of "knowledge discovery" services like 1. knowledge triples based question answering and reading comprehension type question answering utilizing our blended/merged knowledge graph, 2. related entities for a given entity to other connected entities beyond direct ontological relations to generate browsing interest to other sites/properties in Yahoo. In contrast to broad cross domain knowledge, we delve into deep domain specific information extraction from news text and videos to power unique experiences for brands like Yahoo! Sports. Specifically for US Sports (NBA/NFL/NHL/MLB/Soccer) our text information extraction sits in the cross roads of fact finding in articles, fine grained entity typing and topical extractive summarization of temporal topics like trades/contracts/injuries and performances connecting player and potential teams to provide 360 degree browsing of daily fantasy news/sports rumors. Through our Video deep linking capabilities we link moments in highlight videos to points in time of a game such that we can power within-video search/browse experiences for e.g. queries like "Lebron Jame's dunks from yesterday" would seek to exact moments in a highlight video where LeBron dunked or "Laker's top scorer's tonight" would find the stats of the top Laker's scorers, followed by seeking to exact moments of their plays in highlight videos.