Conversational Artificial Intelligence With Neo4j and Unreal Engine – Part 2
Jun 13 8 mins read
Learn how to connect knowledge in applications that have a certain degree of intentionality, as opposed to approaches based on ML only. Read more →
Knowledge Graphs & LLMs: Fine-Tuning vs. Retrieval-Augmented Generation
Jun 06 11 mins read
Discover the limitations of Large Language Models (LLMs), and how to overcome them through fine-tuning vs. retrieval-augmented generation. Read more →
chatbotschatgptfine tuninggraphknowledge-graphllmmachine-learningNeo4jretrieval augmented generation
LangChain Cypher Search: Tips & Tricks
Jun 02 9 mins read
Discover how to optimize prompts for Cypher statement generation to retrieve relevant information from Neo4j in your LLM applications. Read more →
Harnessing Large Language Models With Neo4j
May 26 5 mins read
Episode 1 – Exploring Real-World Use CasesLarge language models (LLMs) like ChatGPT have taken the world by storm in 2023 due to their ability to understand and generate human-like text. Their capacity to adapt to different conversational contexts, answer questions… Read more →
Context-Aware Knowledge Graph Chatbot With GPT-4 and Neo4j
Apr 11 16 mins read
Learn how to implement a context-aware chatbot in GPT-4 that bases its answers on the information retrieved from a graph database. Read more →
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Creating a Knowledge Graph From Video Transcripts With ChatGPT 4
Apr 03 12 mins read
Learn how to use ChatGPT 4 as a domain expert to help you extract knowledge and turn it into a graph from a video transcript. Read more →
Enhancing Word Embedding with Graph Neural Networks
Mar 21 16 mins read
Natural Language Processing (NLP) has seen rapid advancements in recent years. One important aspect of this progress has been the use of embeddings, which are numerical representations of words or phrases that capture their meaning and relationships to other words… Read more →
Demystifying Graph Neural Networks
Mar 15 2 mins read
Graph Neural Networks (GNNs) are gaining tons of recognition in the machine learning community due to their potential for solving complex tasks in social networks, drug discovery, recommendation systems, and more. Read more →
Knowledge Graph-Based Chatbot With GPT-3 and Neo4j
Mar 08 11 mins read
ChatGPT has changed how I, and probably most of you, look at AI and chatbots. We can use chatbots to help us find information, construct creative works, and more.However, one problem with ChatGPT and similar chatbots is that they can hallucinate… Read more →
Announcing NODES 2022! Submit Your Talk
Aug 09, 2022 2 mins read
NODES 2022 is a free, multi-track, two-day virtual conference of highly technical presentations by developers and data scientists solving problems with graphs. Read more →
User Segmentation Based on Node Roles in the Peer-to-Peer Payment Network
Jul 21, 2022 12 mins read
Knowing your users is vital to any business. When your users can interact with each other on a social media platform, content sharing platform, or even work-related platforms, you can construct a network between your users based on their interactions and extract graph-based features to segment your users. Of course, these same approaches can be applied to other platforms that are not user-centric. Read more →
Using Neo4j Graph Data Science in Python to Improve Machine Learning Models
Jul 06, 2022 14 mins read
A wave of graph-based approaches to data science and machine learning is rising. We live in an era where the exponential growth of graph technology is predicted [1]. The ability to analyze data points through the context of their relationships… Read more →
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Exploring Fraud Detection With Neo4j & Graph Data Science – Part 4
Mar 01, 2022 9 mins read
In part 4 of our fraud detection series, we will cover how to apply graph machine learning to predict the high fraud risk user accounts we labeled in parts 1, 2, and 3. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 3
Mar 01, 2022 5 mins read
In part 3 of our fraud detection series, we may want to expand beyond our business logic to automatically identify other users that are suspiciously similar to the fraud risks already identified. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 2
Mar 01, 2022 6 mins read
In part 2 of this fraud detection series, we will provide more formal definitions for resolving entities that will allow us to partition well-defined communities in a scalable manner. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Part 1
Mar 01, 2022 6 mins read
In the first part of this fraud detection series, we will introduce the sample graph dataset we are using and begin exploring the graph for potential fraud patterns. Read more →
Exploring Fraud Detection With Neo4j & Graph Data Science – Summary
Mar 01, 2022 3 mins read
Fraud Detection is one of today's most challenging data science problems. Thankfully, Neo4j Graph Data Science (GDS) offers practical solutions that empower data scientists to make rapid progress in fraud detection analytics and machine learning. Read more →