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cloud-2025

Benchmarking the Mainstream Open Source Distributed Graph Databases at Meituan: NebulaGraph vs Dgraph vs JanusGraph

Graph Database vs Relational Database: What to Choose?

Difference Between Relational and Non Relational Database

Use cases of graph databases in real-time recommendation

Imagine asking ChatGPT, “How did the 2023 global chip shortage impact Tesla’s suppliers in Shenzhen through three degrees of separation?” Despite its eloquence, the LLM would likely falter—not because it lacks data, but because it lacks a way to map hidden, dynamic relationships. This is where graph databases like NebulaGraph become essential for the next generation of AI and LLMs.

As AI shifts from statistical pattern recognition to knowledge-driven reasoning, traditional databases (SQL, NoSQL) hit a wall. Their rigid tables and documents can’t model the interconnectedness of real-world data—supply chains, fraud networks, or molecular interactions. This blog explores how graph technology is rewriting the rules of intelligent systems.

# LLM Hallucinations: When “Confidently Wrong” Becomes Dangerous
LLMs hallucinate because they infer answers from patterns, not facts. Without grounding in verified knowledge, they risk generating plausible but dangerous outputs. Hallucinations aren’t just errors—they’re liabilities because they lack access to verified, structured knowledge. A medical chatbot might invent a drug interaction that doesn’t exist, risking patient safety.

# The Limitations of Traditional Databases
One of the biggest limitations of traditional databases is that they treat data points as isolated entities. Relational databases force data into tables, losing nuanced connections. For example, mapping a 10-layer corporate ownership structure requires 50+ JOINs in SQL, slowing queries to minutes. Vector databases retrieve semantically similar data but miss contextual relationships.

AI often struggles with the isolated data because so much of its power comes from understanding relationships. Graph databases, by contrast, are designed to store and manage data as interconnected entities, allowing AI to make sense of the links between seemingly disparate elements.

# Knowledge Graphs for Intelligent AI Systems
Graph databases are the backbone of such knowledge graphs, providing a powerful means of organizing and traversing complex webs of information. They allow AI systems to dynamically explore and connect various pieces of data, enhancing both the depth and the accuracy of the answers provided. Knowledge graphs are already being used in domains such as finance, healthcare, and education, where the complexity of the data requires more than a surface-level understanding.

Consider a fraud detection system for a major bank. Instead of treating each transaction as an isolated data point, a graph-powered AI can look at an entire network of transactions—cross-referencing user relationships, transaction frequency, locations, and any outliers that indicate something suspicious. It’s this networked understanding that enables real-time decision-making, stopping fraud as it happens rather than analyzing it after the damage is done.

# Graph RAG: Minimize Hallucinations of Traditional RAG
Graph RAG (Retrieval-Augmented Generation) is an advanced evolution of traditional RAG that integrates knowledge graphs into the retrieval process, significantly enhancing contextual understanding and multi-hop reasoning capabilities. Graph RAG addresses hallucinations of traditional RAG by leveraging the structured relationships in knowledge graphs, enabling more precise and contextually rich information retrieval. This structured approach minimizes hallucinations by grounding responses in factual, interconnected data, leading to more accurate and reliable answers.

# Two Major Methods to Implement Graph RAG

- **Build Your Graph RAG with NebulaGraph**: This method requires some coding work and solid AI knowledge. By leveraging NebulaGraph’s powerful graph database capabilities, you can construct a knowledge graph that integrates with your existing data sources. The approach of [Implementing Graph RAG with NebulaGraph](https://www.nebula-graph.io/posts/implement_graph_rag_with_nebulagraph) allows for customization and fine-tuning of the graph structure and retrieval algorithms, providing a robust solution for complex enterprise applications. It involves steps such as data ingestion, graph construction, and integration with large language models (LLMs) to enable sophisticated query handling and response generation.

- **Choose NebulaGraph RAG**: For those without coding experience, [NebulaGraph RAG](https://www.nebula-graph.io/posts/announcing_nebulagraph_RAG) offers an intelligent app-building platform that simplifies the process. This platform abstracts away the technical complexities, allowing users to build and deploy graph-powered AI applications with minimal effort. It provides a user-friendly interface for creating knowledge graphs, configuring retrieval parameters, and generating responses, making it accessible to a wider range of users within the organization.

# Build Tomorrow’s AI with NebulaGraph Today
The next AI breakthrough won’t come from bigger LLMs—it’ll come from smarter data infrastructure. NebulaGraph bridges the gap between LLMs’ linguistic brilliance and the structured intelligence of real-world knowledge. In the race to AGI, graph databases aren’t optional—they’re the bedrock. As OpenAI’s CTO once mused, “The future of AI isn’t just neural networks; it’s neural networks married to knowledge.”

Discover how graph databases like NebulaGraph are powering the future of AI and LLMs by enabling complex reasoning, reducing hallucinations, and unlocking scalable knowledge-driven applications.

Why Graph Databases Like NebulaGraph Are Essential for the Next Generation of AI and LLMs

**Author Introduction**

Hong Hailan specializes in leveraging large language models (LLMs) to automate the extraction of knowledge from unstructured medical records, construct knowledge graphs in traditional Chinese medicine, and uncover the experiences of renowned TCM practitioners.


# Background


Building knowledge graphs presents dual challenges: improving the efficiency of knowledge extraction and reducing human labor costs. To effectively address these issues, this paper builds on the schema layer and explores methods for automated knowledge extraction. The following sections detail the process of constructing a knowledge graph for the treatment of rhinitis by Master Gan Zuwang, a renowned traditional Chinese medicine practitioner.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/1.png)
The data for this study comes from clinical records of rhinitis patients, with a total of 1,081 detailed records selected for analysis. These valuable records contain various basic patient information, such as name, workplace, gender, age, diagnosis time, disease type, symptom details, diagnostic results, observations of tongue and pulse, treatment records, prescription content, and medication guidance.






# Knowledge Graph Construction Process Diagram





## Step 1: Prepare Sample Data

Utilize the 1,081 preprocessed medical records as sample data.


## Step 2: Define Extraction Targets

Based on the ontology-defined schema layer, establish extraction targets like diseases, related diseases, symptoms, examinations, tongue appearance, pulse appearance, and compatibility. Define extraction rules for each target type.




## Step 3: Automated Knowledge Extraction



Create a prompt template based on the defined extraction targets, using a "demonstration case + relationship list" format to guide large language models in automatically extracting medical record data and generating triples.



**The following is a specific Prompt example:**

Imagine you are a large language model designed to extract relationships in Traditional Chinese Medicine. Your task is to extract the relationships between symptoms and related diseases for rhinitis patients. Here's an example medical case:

"Rhinitis has been severe for three days, the condition naturally flares up, with excessive nasal discharge, difficulty blowing the nose. Headache. Nasal congestion. Nasal mucosal edema, with secretions. Thin white tongue coating, floating and tight pulse. Prescription: mulberry leaf 6g, peppermint 6g, platycodon root 6g, calamus 6g, spatholobus stem 10g, schizonepeta 6g, angelica dahurica 6g, xanthium fruit 10g, fagopyrum 10g, vitex 10g."

The output format is:[(“rhinitis”, “symptom”, “excessive nasal discharge”), (‘rhinitis’, ‘symptom’, ‘difficulty blowing the nose’), (‘rhinitis’, ‘symptom’, ‘headache’), (‘rhinitis’, ‘symptom’, ‘nasal congestion’), (‘rhinitis’, ‘examination’, ‘nasal mucosal edema’), (‘rhinitis’, ‘examination’, ‘secretions’), (‘rhinitis’, ‘tongue appearance’, ‘thin white tongue coating’), (‘rhinitis’, ‘pulse appearance’, ‘floating and tight pulse’), (‘rhinitis’, ‘combination’, ‘calamus 6g’), (‘rhinitis’, ‘combination’, ‘spatholobus stem 10g’), (‘rhinitis’, ‘combination’, ‘schizonepeta 6g’), (‘rhinitis’, ‘combination’, ‘angelica dahurica 6g’), (‘rhinitis’, ‘combination’, ‘xanthium fruit 10g’), (‘rhinitis’, ‘combination’, ‘fagopyrum 10g’), (‘rhinitis’, ‘combination’, ‘vitex 10g’)]. Follow this format to extract the following medical case in triplet format. The given sentence is: ‘***’. Given the relationship list: [‘symptom’, ‘related disease’, ‘combination’, ‘tongue appearance’, ‘examination’, ‘pulse appearance’], provide the relationships in the list. If they do not exist, output: none.


## Step 4: Clean the Extracted Results

Use regular expressions to clean the automatically generated triplet data by removing errors, redundancies, irrelevant, and incomplete triplets. This process ensures the accuracy and reliability of the data.


## Step 5: Store the Triplets

Store the final extracted triplet data in the NebulaGraph database.

This paper directly extracts diseases and Chinese herbs into corresponding triplets to ensure the reliability of the knowledge extraction process using large language models. During the writing process, these triplets are further decomposed to ensure the rationality of the knowledge graph. Additionally, the entities and attributes of Chinese herbs are jointly extracted during the knowledge extraction. For example, the dosage of Fagopyrum 10g in a rhinitis patient’s prescription is extracted as ('rhinitis', 'combination', 'fagopyrum 10g').

Before storing the triplets, this paper uses regular expressions to extract the entities and their corresponding attributes. For instance, ('rhinitis', 'combination', 'fagopyrum 10g') is decomposed into ('rhinitis', 'combination', 'Fagopyrum') and ('fagopyrum', 'dosage', '10g'), as demonstrated in the constructed knowledge graph.




# Using NebulaGraph to build a knowledge graph




**Why NebulaGraph**？NebulaGraph is a high-performance and user-friendly graph database that offers researchers a new way to explore and analyze data thanks to its outstanding features and broad applications. It comes with a graph query language called **nGQL**, which provides users with a rich set of graph operation functions. Whether for basic CRUD operations on nodes and edges or complex graph algorithms and path queries, nGQL handles them effortlessly. This **flexibility and robust functionality make NebulaGraph an excellent choice for handling complex network structures and maximizing the value of medical case data**.

**Specific steps:**

* Algorithm pseudocode

* Construct prompt templates, related cases, and relationship lists

* Write functions to connect to **NebulaGraph**

* for i := 0 to N do // traverse medical case data

* Generate questions by combining data and templates

* Pass the questions to the LLM to get answers

* Clean the answers and remove unnecessary triples

* Build the IDs for patients, examinations, symptoms, and prescriptions in the current case

* for j := 0 to M do // iterate through the triple list


```
if relation'=="combination”:       
      Determine whether the Chinese herb in the triplet contains a dosage unit attribute. If it does, extract the attribute and store the extracted result in the knowledge graph. 
Else: Directly write the extracted results into the knowledge graph.
end for
```





# Experiment



This study utilizes a dataset of 353 pre-prepared medical cases as the foundation for knowledge extraction experiments using large language models (LLMs). In the experiment, other LLMs are employed for data annotation, followed by manual verification and correction to ensure the accuracy and reliability of the annotated data. This process serves to evaluate the knowledge extraction performance of each model. The Bert-BiLSTM-CRF model is selected as the baseline for joint entity and relationship extraction, and the results of automatic extraction using the Bert-BiLSTM-CRF model are compared with those obtained from large language models, primarily GLM3, GLM4, and ChatGPT-4. The evaluation metrics include F1 score, precision, and recall, with the experimental results presented in the table.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/2.png)



# Knowledge Graph Display


This study leverages the constructed knowledge graph to analyze and mine medication patterns used by a master physician in treating rhinitis, such as identifying the top 10 drugs most frequently prescribed for severe sneezing symptoms. The following Cypher query is executed to achieve this:

```
MATCH (v:Patient)-[:HAS_SYMPTOM]->()-[:SPECIFIC_SYMPTOM]->(v1) 
WHERE id(v1) = 'severe sneezing' 
MATCH (v:Patient)-[:PRESCRIBED]->(v3)-[e2:COMPONENT_OF]->(v4) 
RETURN v4, count(v4) AS cnt 
ORDER BY cnt DESC 
LIMIT 10;
```



This query identifies patients exhibiting severe sneezing symptoms, retrieves their treatment prescriptions, and sorts the drugs by frequency of occurrence, returning the top 10 most frequently prescribed drugs. The top 10 drugs are: Cicada Skin, Windproof, Ink Plant, Licorice, Earthworm, Black Soybean Coat, Chebulic Myrobalan, White Atractylodes, Rubia Root, and Astragalus.
![](https://nebula-website-cn.oss-cn-hangzhou.aliyuncs.com/nebula-website-5.0/share/%E9%BC%BB%E7%82%8E%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1/3.png)


This paper introduces how to build a knowledge graph for the treatment of rhinitis with traditional Chinese medicine using NebulaGraph.

Rhinitis Knowledge Graph in Traditional Medicine Using NebulaGraph

We are thrilled to announce the General Availability of the NebulaGraph Cloud Standard Tier with 14-day free trial! [NebulaGraph Cloud](https://www.nebula-graph.io/cloud-2025) is a fully-managed graph database service designed to support and enhance Graph RAG projects. It offers native support for ISO-GQL, the industry's first standardized graph query language, ensuring seamless integration with various AI models and tools. This support for ISO-GQL enables efficient querying and retrieval of data from the graph database, providing the necessary context for AI models to generate high-quality content.

# The Ideal Choice for Various Users

Graph databases excel at handling complex, interconnected data by modeling it as nodes and edges, which represent entities and their relationships, respectively. This structure enables efficient querying and analysis of data connections, making it ideal for applications where understanding relationships is crucial. The Standard Tier of NebulaGraph Cloud is tailored to meet the needs of different user groups:

- **Individual Developers**: For those looking to expand their skill set or build innovative projects, such as Graph RAG, the Standard Tier offers an affordable and accessible tool for learning and experimentation. It allows them to focus on developing their Graph RAG models without worrying about the underlying infrastructure complexities.

- **Data Scientists and Researchers**: The platform provides a robust environment for academic research and experimentation with Graph RAG. Researchers can leverage NebulaGraph Cloud to analyze complex datasets, develop novel Graph RAG algorithms, and gain valuable insights into the intersection of graph databases and generative AI.

- **Startup Founders and Small Teams**: For startups and small teams, the Standard Tier offers quick setup and minimal maintenance, allowing them to rapidly prototype and scale their AI projects. It helps them bring their innovative ideas to life without the need for extensive resources or expertise in managing graph databases.

# Key Features of NebulaGraph Cloud:

- **ISO-GQL Support**: NebulaGraph Cloud is the first to support the ISO-GQL standard graph query language on the cloud, allowing users to experience this powerful querying capability firsthand.

- **Beginner-Friendly**: Features no-code graph creation and interactive visual analysis, along with built-in demo datasets, making it accessible for beginners.

- **Flexible Data Modeling**: Supports multiple data types and complex relationship modeling, making it versatile for a variety of applications.

- **Observability**: Includes a monitoring dashboard with visual charts and continuous performance and resource utilization reporting.

# Special Launch Pricing:
**For the first 14 days after creating an instance, NebulaGraph Cloud Standard Tier will be available for free.** After the trial period, if the instance is not deleted, it will automatically start charging at a special discounted rate, with the benefit of this discounted pricing extending for the first six months.

Don’t miss this opportunity to innovate with NebulaGraph Cloud. **[Sign up for the 14-day free trial](https://cloud.nebula-graph.io/login) and experience the future of graph databases with native ISO-GQL support now!**

We are thrilled to announce the General Availability of the NebulaGraph Cloud Standard Tier with 14-day free trial!

cloud-2025

Innovate with NebulaGraph Cloud: Your Trusted Partner in Graph RAG for AI

Why NebulaGraph Cloud?

Graph Your Way to Success with NebulaGraph Cloud

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Innovate with NebulaGraph Cloud:
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