GraphConnect 2020

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.

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.

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.

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.

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.

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.

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!

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.