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Technical Case Study [clear filter]
Tuesday, April 21

2:35pm EDT

Using Graph Technology to Map Fandom
For years fan analytics have been focused on metrics over time - Streams today, new followers this month, likes on a post… But not every fan who follows or likes a post is created equal. Using graph technology we are helping artists identify, communicate with, and ultimately monetize their superfans. Graph technology has been instrumental for us in measuring the relationship between artist and fan. Did the fan use the song in their YouTube video that has 100K views? Did the fan post from the show and get 5 friends to come? Did they tweet the artist’s Spotify link and get 100 retweets? These are all metrics that we are able to sift through using graph algorithms to determine who are those top 20% of fans that are driving the majority of an artist’s revenue.

avatar for Sajan Sanghvi

Sajan Sanghvi

CTO, Laylo
Saj currently serves as the CTO of Laylo, a platform aimed to be Salesforce for Artists. The platform allows artists to identify, communicate with, and ultimately monetize their superfans. Saj also serves as a band member of 'No Suits', totalling over 10M streams online, and represented... Read More →

Tuesday April 21, 2020 2:35pm - 2:50pm EDT
Room 6

3:30pm EDT

The Graph Database Workshop That Changed An Industry
In 2019, a group of eager engineers gathered in a room to learn about Neo4j, Graphql and Apollo. Little did they know, this workshop not only changed their lives, but shook an entire industry. This is the true story of how graph connected people around the world and catalyzed real change.

avatar for Polley Wong

Polley Wong

Polley Wong is a serial entrepreneur. She’s the CEO of design technology and research company VIUSPACE (pronounces as View-space) and co-founded the Interior and Architecture design firm We Create Group with Interior Designer Briana Earl. She’s a strong advocate for gender equality... Read More →

Tuesday April 21, 2020 3:30pm - 3:45pm EDT
Room 6

4:15pm EDT

Open Source Knowledge Graph of News
Journalism is in crisis. Newspaper revenues have been falling while fake news is on the rise. To alleviate these problems, we propose a knowledge graph of entities and relationships from publicly available news sources, using an open dataset of over 23,000 Vox articles as a proof-of-concept. We believe that this work will significantly enable both journalists and consumers of news to keep track of large volumes of information in a non-siloed manner. News organizations will have access to a comprehensive content catalog that allows for collaboration between and within other organizations, as well as producing investigative journalism pieces at a lower cost. The public will benefit from having a platform that is widely available, easily searchable, comprehensive over time and space, and curated by trustworthy sources. Because we also believe that our initiative is of great benefit to the public, we are both open sourcing our efforts and actively encouraging others to collaborate with us and each other to bring this project forward.

avatar for Hanhan J. Li

Hanhan J. Li

I’m Hanhan, the co-founder of PressDB, an exploratory writing engine that empowers people to research and write better and faster. I graduated with a dual masters’ degree in Journalism and Statistics from Columbia University in 2018.I have been fascinated about the graph world... Read More →

Chris Rusnak

Chris Rusnak has 8 years of work experience as a data scientist in information services and consulting industries. He has obtained a M.S. degree in Data Science from the Data Science Institute at Columbia University in 2017 and a B.A. degree in Math and Biological Sciences as sum... Read More →

Tuesday April 21, 2020 4:15pm - 4:30pm EDT
Room 6

4:40pm EDT

Resolving Locations in Text with NLP and Neo4j
Humans are very good at inferring the location of events described in text, even when no places are mentioned by name. Can computers do something similar? We demonstrate a solution, using a combination of natural language processing and graph techniques applied to a corpus of online news stories.

Effective news monitoring, for competitive intelligence and other purposes, often requires an understanding of the location at which the events mentioned in an article occur. This information is sometimes not given explicitly in the text, but can be inferred using a combination of natural language processing and graph techniques. This talk describes a Neo4j-based system for performing this task. We also discuss some lessons learned along the way, and a graph-based approach to enhancing the traditional process of named entity recognition.

avatar for Stephen Hall

Stephen Hall

Software Engineer, Predix Communications
After receiving a PhD in Electronic and Computer Engineering from the University of Wollongong, Australia, I held various academic and industry positions in telecommunications and computing. In 2000, I co-founded Predix Communications in Cape Town, South Africa, and the company developed... Read More →

Tuesday April 21, 2020 4:40pm - 4:55pm EDT
Room 6
Wednesday, April 22

11:20am EDT

Enhancing conversational AI with a contextual graph model
A contextual graph model feeds AI suggestions to refine a retail catalog display in a person to person conversation. The graph indexes a conversational sequence on genres, related sub-genres and attributes to yield suggestions that are meaningful and relevant through the conversation flow.

avatar for Kofi Dadzie

Kofi Dadzie

Co-Founder, Executive Vice-Chair, Rancard
Kofi is Co-Founder and Executive Vice Chair of Rancard. He has led the company in its evolution to conversational discovery with AI & social recommendations, following success in mobile content distribution technology with developers and brands including Google, BBC, VOA, MTV, ESPN... Read More →

Wednesday April 22, 2020 11:20am - 11:35am EDT
Room 6

1:30pm EDT

Knowledge Graphs for AI-Powered Shopping Assistants
The world around us is a network of connected concepts. Concepts can belong to different domains, but they are not isolated, rather maintain some connections with each other. Graph databases contain these connections besides the data, in contrast to traditional databases where relations are found by expensive operations during query time. In recent years, conversational systems have been trending as they provide an effortless shopping experience for customers. One of the challenges towards building a seamless dialogue with the user is understanding the products in the catalog. We address this by exploiting the perks of graph databases into the conversational systems for e-commerce through creating a knowledge graph representation for catalog data. This graph representation can be applied to many domains including grocery, movies, furniture, fashions, etc. Moreover, it can be beneficial in several facets within each domain, such as improving the training data for NLU models or answering product-related questions or even to narrow down the search results for search refinement queries.

To enable these, we first link retail concepts to catalog products, each with a specific brand, size, color, type, etc. In our application, this particular linkage between these two entities was missing initially but we utilize other hierarchical relations and design a simple but powerful semi-supervised-learning algorithm to create this linkage. To enhance our algorithm, we utilize the user logs carrying insightful information among these entities. We load the output of our algorithm into the neo4j database, a fast visual graph database supporting multiple hop query and node properties. In particular, to further benefit from Neo4j, we use its Cypher query language feature to address knowledge-based questions by transforming natural queries to Cypher queries.


Ghodrat Aalipour

Senior Data Scientist, Walmart Labs
I joined Walmart Labs in September 2018 and since then, I have been working on NLU systems for e-Commerce, please see here or here. Prior to that I was a lecturer in the School of Mathematical Sciences at RIT and a visiting faculty at the University of Colorado Denver. I have a P... Read More →

Wednesday April 22, 2020 1:30pm - 1:45pm EDT
Room 6

1:50pm EDT

The Connected Habitat of Impact
The Nature Conservancy

The Nature Conservancy has a global team of 400+ scientists that are helping drive our understanding of how nature’s resources like water, land and air interact with society, and vice versa. When we pick our actions and efforts to conserve a piece of land or a patch on the ocean, the ecological impacts are usually scientifically clear and well stated. However, more often than not, preserving nature’s resources can impact more than just the environment - take for instance the task of protecting land. Land provides food, food feeds people, industries and infrastructure and more. Even the interconnectedness of land and water make nature a complex entity of change! We need a better way to understand and drive decisions for conservation impact!

Helping nature do nature faster

While nature always works, it takes time. For instance, we know reforestation helps restore our water systems in a sustainable way! However, it can be years before we are able to see the proof of the impact. We need technology and tools to speed up our understanding and confidence in our strategies. We need to engage more regional and relevant stakeholders that can push the success as well as de-risk the execution of such strategies. ## Telling stories & making a case for impact With the power of graphs, we can better understand the connectedness of people and nature’s resources. By leaning on patterns for “detecting risk” & “recommending actions”, we can kickstart some innovative and highly relevant stories of impact!


Niraj Swami

Niraj Swami is an avid technologist & innovator with deep interests in Artificial Intelligence, applied knowledge graphs, cognitive technologies and behavioral economics. Niraj has led Applied AI and innovation initiatives for the learning, business productivity and healthcare spaces... Read More →

Wednesday April 22, 2020 1:50pm - 2:05pm EDT
Room 6

2:35pm EDT

Exploring NASA Open data
How’s NASA Open Data datasets related? Which datasets are related and which aren’t? How easy is it to search for specific data in NASA? Let’s go through the work of putting all this information in a Neo4j Database to get the most of one of the biggest open data sources ever.


Wednesday April 22, 2020 2:35pm - 2:50pm EDT
Room 6