Q&A with Thorsten Liebig from Derivo, Gavin Mendel-Gleason from TeminusDB, and Chen Yong Cher from Graphen

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 distribute...

The Rich Context project at NYU Wagner is the knowledge graph complement to the ADRF platform for cross-agency social science research using sensitive data, currently used by 50+ agencies. Rich Context represents metadata about datasets and their use in research which in turn influences public po...

Graphs provide a new dimension to managing and analyzing data, and enterprises are keen to explore and adopt this technology. There have been some barriers to adoption, including a lack of familiarity with graph query languages and tools and challenges in integrating graph analytics into existing...

Predictive analytics in inventory management has not been the traditional domain of knowledge graphs and semantics; however, it is a surprisingly natural fit. This talk will review knowledge graphs in the supply chain and look into the details of implementation. In our central case, using a seman...

In this presentation, we'll cover why a data catalog is the first knowledge graph that an organization should build. Most organizations don't have a clear picture of their data assets and how they're being used - a data datalog helps provide that picture and unlock the value of data. We'll explor...

The development and availability of Web knowledge repositories, in particular Wikipedia, as the largest general-knowledge encyclopedic collection, have changed remarkably not only the way in which individual users fulfill their informational needs but also the way in which information providers o...

Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts (e.g., events, entities). These data constitute a valuable source of insights for studie...

In today’s world understanding the full context of an event is not an easy thing, especially in a world where there’s countless sources of low quality one dimensional content is fighting for our attention. Which leaves most readers of the news uniformed or spending time piecing together incomplet...

One of the challenges in protecting consumer privacy and managing data risk is the ability to validate that privacy-related data from across our data ecosystem has been identified and categorized accurately and consistently. This challenge has become especially salient in light of recent legislat...

The Information Management team (including Nicolas Seyot) at Morgan Stanley has built an RDF graph and a semantic knowledge base to help answer domain specific questions, formulate classification recommendations and deliver quality search to our internal users. In doing so over the past 4 years, ...

The Financial Industry Business Ontology (FIBO) has matured in significant areas over the last few years, but until recently there were few examples to help users understand how to interpret it and build it into their own knowledge graphs. Since early 2019, the FIBO development team has been focu...

Akash Magoon presents Nayya, the winner of the startup-investor pitch event. Nayya is a digital health platform that integrates with a company's benefit to help employees find, book, and access high-value care.

Q&A of the 4th session with Silviu Cucerzan of Microsoft Research, Konstantin Todorov of the University of Montpellier, and Dylan Roy from Dow Jones.

KGC organizer François Scharffe opening address

The proposed meta-graph solution for recommender systems is a living process for semi-automatically resolving recommendations using guided queries upon a knowledge graph. In addition, this solution is explainable; it can provide comprehensible recommendations which show the reason for each result...

Q&A of Session 5 with Bethany Sehon, & Brian Donohue from Capital One, Radu Marian from Bank of America and Nicolas Seyot from Morgan Stanley.

Knowledge graphs are increasingly built using complex multifaceted machine learning based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction...

Cherre’s knowledge graph is a model of the entire US real estate ecosystem. The graph incorporates hundreds of millions of entities such as properties, addresses, individual and commercial owners, lenders, brokers, estate managers, lawyers etc. as nodes – while the edges are various types of conn...

The enterprise knowledge graphs help modern organizations to preserve the semantic context of abundant accessible information. They become the backbone of enterprise knowledge management and AI technologies with the ability to differentiate things versus strings. Still, beyond the hype of repacka...

Q&A of the first session of day 1. Here our own Francois Scharffe, Neda Abolhassani from Accenture Labs, Paul Groth from the University of Amsterdam and Ron Bekkerman from Cherre answer some questions from the audience.

Francois Scharffe leads the Q&A between Vassil Momtchev from Ontotext and John Sowa from Kyndi.

Electronic health records (EHRs) have become a popular source of observational health data for learning insights that could inform the treatment of acute medical conditions. Their utility for learning insights for informing preventive care and management of chronic conditions however, has remaine...

Boost your Graph with Semantic NLP: Nicole Moldovan from Lymba talks about the Lymba platform and how Knowledge Graphs help produce better results in this demo called Boost your Graph with Semantic NLP. Nicole shows the viewers a problem some customers face with unstructured data. Afterwards, Nic...