The rapid growth of video content demands efficient and precise retrieval systems. While vision-language models (VLMs) excel in representation learning, they often struggle with adaptive, time-sensitive video retrieval. This paper introduces a novel framework that combines vector similarity search with graph-based data structures. By leveraging VLM embeddings for initial retrieval and modeling contextual relationships among video segments, our approach enables adaptive query refinement and improves retrieval accuracy. Experiments demonstrate its precision, scalability, and robustness, offering an effective solution for interactive video retrieval in dynamic environments.

Current cutting-edge facial recognition models offer human-level accuracy. Still, we can improve facial recognition model accuracies if we represent classifications in a graph. In this post, we are going to find false negative classifications of facial recognition models with Neo4j graph database. We have just focused on detecting false positives in facial recognition with Neo4j. False positives are mis-classifications verifying different persons as same person.

In my past 3 years as a Data Science professional, I have worked extensively with both RDBMS (Postgres) & Cassandra (NoSQL) but didn't get a chance to explore Graph databases. So, it's time to jump onto graph databases & how they can be integrated into different data science solutions. Consider this: Observe Google Maps for any city. A graph is basically a collection of Nodes (the landmarks) & edges(the roads). Nodes are connected (or may not be connected at all)to each other using the edges. Neo4j is the most popular database for analyzing graph data.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Whether you're genuinely interested in getting insights and solving problems using data, or just attracted by what has been called "the most promising career" by LinkedIn and the "best job in America" by Glassdoor, chances are you're familiar with data science. As we've elaborated previously, graphs are a universal data structure with manifestations that span a wide spectrum: from analytics to databases, and from knowledge management to data science, machine learning and even hardware. Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points -- that's the 30-second explanation, according to Alicia Frame. Frame is the senior director of product management for data science at Neo4j, a leading graph database vendor.

Graph databases like Neo4j are an excellent tool for creating recommendation engines. They allow us to examine a large context of a data point potentially comprising various data sources. Their powerful storage model is very well suited for applications where we want to analyze the direct surrounding of a node. If you would like understand what makes graphs so powerful in comparison with relational models, read here. In this article, I describe how we implemented a simple recommendation engine directly in Neo4j using only Cypher. The approach bases on basic NLP and simple conditional probabilities to find the most likely matching item.

Through this tutorial, you'll learn how to migrate the movies dataset from Neo4j to Memgraph. If you used Neo4j, you are probably familiar with their example graph that helps you learn the basics of the Cypher query language. Neo4j is an ACID-compliant transactional native graph database, while Memgraph is a platform designed for graph computations on streaming data. You can read more about their differences in the Neo4j vs Memgraph article. If you're having trouble running this on the new Apple M1 chip, try adding --platform linux/arm64/v8 after the run command.

Every day, millions of articles and papers are published. While there is a lot of knowledge hidden in those articles, it is virtually impossible to read all of them. Even if you only focus on a specific domain, it is still hard to find all relevant articles and read them to get valuable insights. However, there are tools that could help you avoid manual labor and extract those insights automatically. I am, of course, talking about various NLP tools and services. In this blog post, I will present a solution of how you can combine the power of NLP with knowledge graphs to extract valuable insights from relevant articles automatically.

We live in a time where data is supreme. Our private details, financial arrangements, careers, and amusement have been digitized and stored as data. Due to the greater volume of data generated, there is a more significant need to research and retain it. If you're conscious of the current market environment, you've probably noticed that the data science field is flourishing. Data Science signifies generated value from data, and it all comes down to comprehending the data and processing it to obtain actionable & insightful value from it.

The year is 2022, and graph machine learning is one of the rising trends in data analytics. While Neo4j has a Graph Data Science library that supports multiple graph algorithms and machine learning workflows, sometimes you want to export data from Neo4j and run it through your favorite machine learning frameworks like PyTorch or TensorFlow. In that scenario, you want to be able to export data from Neo4j in a fast and scalable way. But, unfortunately, using the Neo4j Python driver is not the most efficient way of retrieving data. However, no need to worry, Dave Voutila has got your back.

Must have a Master's Degree (or equivalent) in Computer Science, Engineering (any), Mathematics, or related field, plus one (1) year of IT experience. The one (1) year of IT experience must include experience with: Python, Machine Learning, Deep Learning, Natural Language Programing (NLP), AWS, Docker, Hive, Presto, Postgres, Neo4j, PowerBI, and Spotfire. In the alternative, we will accept a Bachelor's Degree (or equivalent) in Computer Science, Engineering (any), Mathematics, or related field, plus five (5) years of progressive post-baccalaureate IT experience. One (1) year of the five (5) years of progressive post-baccalaureate IT experience must include experience using Python, Machine Learning, Deep Learning, Natural Language Programing (NLP), AWS, Docker, Hive, Presto, Postgres, Neo4j, PowerBI, and Spotfire. All experience may be acquired concurrently.