Australian National University

Wenyi Pi

Neo4j APOC Library Use Case

 Author

Wenyi Pi (

 ORCID:

0009–0002–2884–277)

 Introduction

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Introduction to APOC: Enhancing Neo4j Capabilities

Xuzeng He

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called

 nodes

(or vertices) and their relations are called

 edges

(or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Recent Advances in using Machine Learning with Graphs

Vaibhav Khobragade

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Enhancing Language Models: The Role of Knowledge Graph Augmentation in Overcoming LLM Challenges

Enhancing Data Interactivity with LLMs and Neo4j Knowledge Graphs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Since OpenAI launched ChatGPT, a

 large language model (LLM)

based chatbot, in 2023, it has set off a technological wave.

The integration of large language models (LLMs) with Neo4j-based knowledge graphs

An Introduction to RA-CM3, MuRAG and RACE Author  Xuzeng He (ORCID: 0009-0005-7317-7426)  Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Virtual assistants have popped up on numerous websites over the years.

Harnessing The Power of Knowledge Graphs in Question Answering

Qingqin Fang

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph

Author Qingqin Fang (ORCID: 0009–0003–5348–4264)

 Introduction

Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Hallucination in Large Language Models and Two Effective Alleviation Pathways

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

How to use GPT-4V For Stoplight Report

Dhruv Gupta

Improving the performance of Large Language Models Author  Dhruv Gupta (ORCID: 0009-0004-7109-5403)  ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

RAG: The next big thing after LLMs?

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Automated Knowledge Graph Construction with Large Language Models

Swinburne University of Technology

Nakul Nambiar

Zhuochen Wu

Authors:  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Zhuochen Wu (ORCID: 0009-0000-5642-5348)  Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data.

How to use GPT API to export a research graph from PDF publications

Dhruv Gupta

Prompt Engineering — Part 2


 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Prompt Engineering — Part 2

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs


 How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

When searching for information on the website, it is common to come across a flood of

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and…

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Recent Advances in Using Machine Learning with Graphs — Part 2

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.

Ethics and AI: Confronting the Challenges Ahead

How to efficiently retrieve information for different applications

Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Amir Aryani

Authors  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774)  Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.

Mini RAG using Neo4j

Aishwarya Nambissan

A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors  Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609)  The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.

Typesense and Neo4j in a hybrid information retrieval solution

Amir Aryani

A brief overview of different types of clustering techniques and their algorithms. Authors Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Amir Aryani (ORCID: 0000-0002-4259-9774)

 Background

Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels.

19 Clustering Techniques

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Brief Introduction to the History of Large Language Models (LLMs)

Understanding Sequential Data Modelling with Keras for Time Series Prediction

 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

Beginner’s Guide to Recurrent Neural Networks (RNNs) with Keras

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Combining Knowledge Graphs With Language Models for Interpretability

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Unveiling Research Trends through OpenAlex Visualization

Large Language Models for Fake News Generation and Detection Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers.

Are Large Language Models Our Allies or Enemies in the Fight Against Fake News?

Unlocking the power of language models: A deep dive into BERT


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Language Models: Deep Dive into BERT

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Transformers Models in NLP

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

The human brain is more extraordinary than any machine we could build. From an early age, many of us gain the ability to comprehend what our eyes tell us and articulate it. Furthermore, we combine evidence from all our senses to reason.

How Much Can Vision Language Models Really “See”?

A novel compression technique ensuring comparable performance with 70% less parameters

Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction The sizes of large language models (LLMs) have been steadily increasing over the last few years.

CompactifAI: Large Language Models Don’t Actually Have To Be Large

From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large Language Models (LLMs) have achieved remarkable success.

Three Paradigms of RAG

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) PubMed is a free Web literature search service developed and maintained by the National Center for Biotechnology Information (

 NCBI

), and it is also a part of NCBI’s Entrez retrieval system that provides access to a diverse set of 38 databases.

Enhancing PubMed: from a Medical Database to beyond

Aland Astudillo

How to use GROBID to extract text from PDF  Author  Aland Astudillo (ORCID: 0009-0008-8672-3168)  GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

How to use GROBID

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse.

How to use Large Language Models to tag your data: A complete tutorial

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Deeper understanding of In-Context Retrieval-Augmented Language Models

Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Large Language Models and Knowledge Graphs: Ways to combine them

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Prompt Engineering

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A

 knowledge graph

(graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Exploring Methods of Cypher Query Optimisations

The AI Helper Turning Mountains of Data into Bite-Sized Instructions

Author Aland Astudillo (ORCID: 0009-0008-8672-3168) LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.

What is LLMLingua?

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models   Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author  Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction   Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

Tools and Platform for Integration of Knowledge Graph with RAG pipelines.

Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.

Unveiling the Synergy: Retrieval Augmented Generation (RAG) Meets Knowledge Graphs

Author  Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition   A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Research Collaboration Network

Improving the performance and application of Large Language Models

Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

Multimodal Large Language Models for Misinformation Detection and Reasoning

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.

RNNs vs GRUs vs LSTMs

A Unified and Collaborative Framework for LLM Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

RAG 2.0 is Coming?

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

NLU vs. NLG: Unveiling the Two Sides of Natural Language Processing

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

An Introduction to Recurrent Neural Networks (RNNs)

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Generative AI’s Leap into Video in 2024 and its Ethical Horizon

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Question Answering enhanced by Knowledge Graph

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Knowledge Graphs: Deep Dive into OpenAlex

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

Automated Knowledge Graph Construction with Large Language Models — Part 2

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

The longer the context, the better? Unlimited Context Length in Megalodon

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

The Journey of Large Language Models: Evolution, Application, and Limitations

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Understanding the Balance between Internal Knowledge and External Sources

Author Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

When RAG and LLM Conflict: Who Will AI Listen to?

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Navigating the Long Context Conundrum: Challenges in Language Models’ Information Processing

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution.

Fine-tuning Large Language Models: A Brief Introduction

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Prompt engineering: A Way to Smartly Use AI

Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.

Large Language Models for Code Writing: Security Assessment

Research Graph

Google’s Groundbreaking AI Model Explained
 


 Introduction

In the rapidly evolving landscape of artificial intelligence, Google continues to stand at the forefront, consistently pushing the boundaries of what these technologies can achieve. One of their latest advancements, GEMMA2, represents a significant leap in AI capabilities.

What is Gemma2?

Stories by Research Graph on Medium

> Research Graph is a heterogeneous network that connects research
> outputs, research activities and researchers.

<figure>
<img
src="https://cdn-images-1.medium.com/max/917/1*6SmzMUfWzbDuXSCWs0kgXA@2x.jpeg" />
<figcaption>Image from
https://blog.apo.org.au/2018/02/27/researchgraph-pilot-project</figcaption>
</figure>

## Author 

* Amir Aryani (ORCID: [0000-0002-4259-9774](https://orcid.org/0000-0002-4259-9774))

# Introduction

In this article we look at Research Graph as an information model , and
an approach to connect and capture the connections between research
outputs, researchers and research activities. We explore the metadata
model, and we discuss how to capture this graph in a Neo4j Graph
Database.

While scientific papers and research publications are key components of
the research ecosystem, understanding the research and innovation
ecosystem involves more than just studying research papers. Researchers
need to be aware of currently active projects in their field to avoid
duplication and to identify collaboration opportunities. Also, they need
to learn from completed projects and their outcomes, engage with experts
in their fields, and continuously seek potential funding and
collaborative opportunities.

Scientific papers and research publications are key components of the
research ecosystem. For a young PhD student, the journey begins by
reading academic papers to learn about their field, while established
researchers use the latest publications to keep up with developments,
discoveries, and innovations in their area of expertise. However, a
thorough understanding of the research and innovation ecosystem involves
more than just studying research papers. Researchers need to be aware of
currently active projects in their field to avoid duplication and to
identify collaboration opportunities. They should also learn from
completed projects and their outcomes, engage with experts in their
fields, and continuously seek potential funding and collaborative
opportunities. Arguably, understanding this interconnected ecosystem is
crucial for a successful academic career. The challenge for early career
researchers lies in navigating this ecosystem in a way that provides
insights into the best opportunities. Similarly, for governments,
policymakers, and administrations, comprehending the dynamic components
of innovation, staying abreast of the state of the art, and identifying
research capabilities and expertise represent significant challenges.

Research Graph is composed of five primary node types:

- **Researcher**: An individual who conducts scientific or scholarly
 study, often within a specific field of knowledge. Their role involves
 creating new knowledge through research, which often includes running
 experiments, contributing to studies, and writing articles, books,
 or reports.
- **Publications**: Academic publications include journal articles,
 books, patents, conference abstracts, and theses, serving as the
 primary medium for disseminating research findings. Academic
 publications are often subject to a peer review process to ensure
 quality and validity. In the domain of humanities and social sciences
 (HASS), publications can form media pieces, performance arts, and
 other non traditional research outputs. Publications are essential for
 sharing new knowledge and advancing scholarly discourse.
- **Research Data**: Data that is used or generated in the course of
 academic studies. They form the empirical basis for research in many
 disciplines, providing the raw information needed for analysis,
 conclusions, and validation of research hypotheses.
- **Grants**: Award and grants are financial support provided by funding
 bodies (like government agencies, foundations, or institutions) to
 researchers or research projects. They are pivotal for enabling
 research by providing necessary resources, and they often influence
 the direction and scope of research activities. It is common for an
 awarded grant to support multiple projects or sub projects to an
 hierarchical. structure of research activities.
- **Organisations**: This encompasses institutions like universities,
 research institutes, and academic societies. Organizations play a
 crucial role in the research ecosystem by providing infrastructure,
 resources, and a collaborative environment for conducting research.
 They are also involved in disseminating research findings and may
 participate in the management of research projects and funding.

<figure>
<img
src="https://cdn-images-1.medium.com/max/960/1*1i25PuJG9X8r4n6tQyrK_g@2x.jpeg" />
<figcaption>Image from
https://blog.apo.org.au/2018/02/27/researchgraph-pilot-project, RDA
Plenary 11, when Research Graph announced a partnership with Australia
Policy Observatory</figcaption>
</figure>

### History

Research Graph formed as a project during the [Research Data Alliance
Working Group on Data Registry
Interoperability](https://www.rd-alliance.org/groups/data-description-registry-interoperability.html).
The initial version of this project was providing the graph schema to a
software platform called [Research Data Switchboard
(RD-Switchboard)](https://github.com/rd-switchboard). This was an
initial attempt by Australian National Data Service (Australia), Dryad
(United States), GESIS (Germany) and other partners to create a platform
for creating links between research data collections across global data
repositories. The graph schema later evolved to Research Graph as a meta
model, and later formed the bases for a series of open source connected
projects. Research Graph Foundation, a not for profit foundation, formed
in Australia in 2018 to coordinate these projects. You can learn more
about the foundation at
[www.researchgraph.org](http://www.researchgraph.org).

### Metadata

Research Graph nodes connected by relationships such as authorship links
between researchers and publications, employment links between
researchers and organisations, and funder links between organisations
and grants.

> In the context of this article, we refer to the node properties and
> structure of the relationships as **metadata**.

<figure>
<img
src="https://cdn-images-1.medium.com/max/658/1*w57fFcZs41aAn-GmL5kfRQ@2x.jpeg" />
<figcaption>Research Graph Schema,
https://researchgraph.org/schema</figcaption>
</figure>

The detail list of properties for nodes and relationships are presented
at [www.researchgraph.org/schema](http://www.researchgraph.org/schema).
However, it is important to highlight five main properties that applies
to all nodes:

- **source**: A string value that shows the domain name of the source
 repository such as crossref.org, or orcid.org.
- **local_id**: A string value that contains the local primary
 identifier of this node at the \*source\*.
- **key**: A string value with the function as a uniquely identifying
 the node across the global network of scholarly works. To achieve
 this, we combine the source [second-level
 domain](https://en.m.wikipedia.org/wiki/Second-level_domain)(e.g.
 crossref) and the local_id.
- **type**: A string that represent the type of the node including
 "researcher","organisation", "publication","dataset","grant".
- **last_updated**: Date and time of the last update for the metadata of
 this node in this research graph.

Beyond this properties, all five node types have properties that further
expand the information about each node. Some properties are optional and
some mandatory. For example, **researcher** nodes has a mandatory
property called "full_name" that would contain the complete name of the
researcher. However, it has several optional nodes such as "first_name",
"last_name", "orcid", "scopus_author_id".

### Persistent Identifiers (PIDs)

A persistent identifier is a long-lasting reference to a digital
resource. ORCID is an example of a persistent identifier for a person.
Learn more about [persistent
identifiers](http://www.ands.org.au/guides/persistent-identifiers-awareness).
DOIs (digital object identifiers) are persistent identifiers for things
or entities such as journal articles and
datasets. [Crossref](https://www.crossref.org) and [DataCite](https://www.datacite.org)
are the main organisations assigning DOIs in the academic ecosystem. We
recommend learning about [the work of DataCite and
Crossref](https://project-thor.readme.io/docs/who-are-datacite-and-crossref)\],
[the organization identifier
project](https://orcid.org/blog/2016/10/31/organization-identifier-project-way-forward), [Project
THOR (Technical and Human infrastructure for Open
Research](https://doi.org/10.5438/6423)),
and [Metadata2020](http://www.metadata2020.org/) (the scholarly
community's group effort to advocate for richer shared metadata).

PIDs are essential components for connecting scholarly networks and
forming a research graph. Most research graph nodes have PIDs as their
main identifier. For metadata from ORCID, such as such as the node that
represents https://orcid.org/0000-0002-4259-9774, the node properties
include

- **source**: "orcid.org"
- **local_id**: "0000--0002--4259--9774"
- **Key**:"orcid/0000--0002--4259--9774"
- **orcid**:"0000--0002--4259--9774"
- **first_name**:"Amir"
- **last_name**: "Aryani"
- **full_name**: "Amir Aryani"
- **scopus_author_id**: "35068996400"

Notice how "key" is formed as the results of concatenating the second
level domain name from source ("orcid") to the "local_id" property. In
this case, we use the orcid value as the "local_id" of the node. In
addition, there is a seperate property called "orcid". While this seems
redundant, it provides consistency for nodes from other sources that
might include ORCID identifier for researchers such as open access
repositories.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*z7bfYB1kjaCJtz6lPtFxOw.png" />
<figcaption>Neo4j Browser, Screenshot from Australian National PID
Graph, <a
href="https://researchgraph.org/national-graph/">https://researchgraph.org/national-graph/</a></figcaption>
</figure>

### Why Neo4j?

For large research graph data, we need a database to capture and analyse
the graph content. At the time of writing this article, there are
several graph databases such as [ArangoDB](https://arangodb.com/),
[Amazon Neptune](https://aws.amazon.com/neptune), and [Azure Cosmos
DB](https://azure.microsoft.com/en-au/products/cosmos-db). However,
Neo4j seems to be one of the most versatile options as it satisfy the
following requirements:

- Neo4j open source version enables free use and customisation for
 research.
- Neo4j has a large and active community with a lot of education
 materials and active user groups.
- Easy to use Neo4j integration with Python, Java and other programming
 languages makes it very easy to start working with this graph
 database.

The most prominent advantage of Neo4j is the programming environment and
user interface. It is highly accessible to novice programmers due to
excellent integration with Java, Python and other programming languages.
In addition, the graph explorer is easily usable by business analysts.
The query language to interact with a Neo4j database is called Cypher.
You can fin a comprehensive list of Cypher commands and useful tutorials
at

- Getting Started with Cypher:
 <https://neo4j.com/docs/getting-started/appendix/tutorials/guide-cypher-basics/>
- Cypher Cheat Sheet:
 <https://neo4j.com/docs/cypher-cheat-sheet/5/auradb-enterprise>
- Cypher Basics:
 <https://vladbatushkov.medium.com/learn-neo4j-cypher-basics-in-30-minutes-94d68a52544>

### Neo4j Labels

A Neo4j graph is constituent of nodes and edges. Both nodes and edges
have properties and labels. Properties are essential for information
retrieval as it enables us to find query database for particular nodes
and relationships. Labels are key components of the graph that provides
a level of classification. It enables grouping nodes and relationships
and connect them the same way we connect rows in a SQL database in a
form a table, or collect JSON objects in a MongoDB database in a
collection. The main advantage of these labels is indexing. We can index
properties agains labels. Indexes in Neo4j are pairs of \<property,
index\>, and that is essential for large graphs. Grouping nodes by
labels and creating index at this level enables smaller, and
faster indexes.

We have two major labels in a Research Graph Neo4j database:

- Source: these are labels that reference to the source Property such as
 "orcid", "corssref", and "datacite".
- Type: these labels refer to five main node types including
 "publication", "dataset", "researcher", "organisation", and "grant".

Note: Labels and properties are always lowercase, this enables us to
have consistent cypher queries.

### Summary

Research Graph is a heterogeneous network that connects research
outputs, research activities and researchers. This network provides
insights into the digital footprint of research, and we can leverage
Research Graph to analyse science and innovation networks such as
citation networks, collaboration networks, and research communities. In
addition, the Research Graph networks are the pathways to trace and
analyse the impact of research funding and research projects. In this
article, we explored the foundation of the Research Graph meta model,
recommended the minimum metadata and we introduced Neo4j as the best
approach to capture and link Research Graph nodes.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=0e1ab5df4497){width="1"
height="1"}

**An Introduction to Retrieval Augmented Generation (RAG) and Knowledge
Graph**

<figure>
<img src="https://cdn-images-1.medium.com/max/904/0*UtyEkTLv-rFAJV5a" />
<figcaption>Image from Pexel</figcaption>
</figure>

### Author

* Qingqin Fang (ORCID: [0009--0003--5348--4264](https://orcid.org/0009-0003-5348-4264))

### **Introduction**

Large Language Models (LLMs) have transformed the landscape of natural
language processing, demonstrating exceptional proficiency in generating
text that closely resembles human language. However, a significant
challenge faced by these advanced models is the phenomenon known as
"hallucination." Hallucination in LLMs refers to the generation of
content that appears factual but lacks a basis in reality. This issue
poses a critical obstacle to the safe deployment of LLMs in real-world
applications, where accuracy and reliability are paramount.

In this post, we'll embark on a journey to understand hallucination, its
causes and two effective mitigation techniques: **Retrieval Augmented
Generation** and **Knowledge Graph** integration, including the
classification and evolution. By combining these strategies, we aim to
counter hallucination challenges and contribute to the responsible
development of LLMs.

### **What is Hallucination?**

Hallucination in LLMs occurs when the model generates information that
is factually incorrect or entirely fabricated. This phenomenon stems
from the training phase of the model, where the LLM has been exposed to
vast amounts of online text data. While this extensive training data
enables LLMs to exhibit impressive language fluency, it also opens the
door to potential pitfalls.

The generation of hallucinations in LLMs can be attributed to
several factors:

1\. **Biases in Training Data**: LLMs learn from the data they are
trained on, which may contain biases or inaccuracies. These biases can
influence the model's output, leading to the generation of hallucinated
content.

2\. **Ambiguity in Prompts**: LLMs rely on prompts to generate text.
Ambiguous or unclear prompts can result in the model extrapolating
information incorrectly, leading to hallucinations in the generated
text.

3\. **Lack of Comprehension:** Despite their language generation
capabilities, LLMs may lack true comprehension of the content they
generate. This can result in the model modifying information to
superficially align with the input, even if it is factually incorrect.

### **Hallucination Mitigation**

**Prompt engineering** is the crafting of instructions for Artificial
Intelligence (AI) text generation models, adjusting the parameters of a
sophisticated tool to achieve optimal results, ensuring more refined and
tailored content, resulting in more accurate and reliable outputs.

Some studies have focused on developing innovative models to address
hallucinations, emphasising a continuous and progressive process that
combines algorithmic advancements and improvements in data quality.
Instead of fine-tuning models, these methods implement a comprehensive
model architecture to combat hallucinations.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*9fVvSrInAjXrXVW3" />
<figcaption>Hallucination Mitigation Techniques in LLMs</figcaption>
</figure>

In this post, we delve into two effective mitigation
strategies --- Retrieval Augmented Generation (RAG) and Knowledge Graph
(KG) integration --- to address hallucination challenges in LLMs.

### **Retrieval Augmented Generation (RAG) in LLM**

RAG is a technique used to enhance the responses of LLMs by
incorporating external, authoritative knowledge bases during the text
generation process. By tapping into external sources of information, RAG
provides additional context and verifiable data to guide the generation
of text, ultimately improving the accuracy and reliability of the
generated content.

**RAG Categories Based on its Implementation:**

1\. Before Generation: Techniques involving information retrieval before
text generation, like LLM-Augmenter.

2\. During Generation: Methods utilising external knowledge retrieval
mechanisms during text generation, such as Knowledge Retrieval and
Decompose-and-Query framework.

3\. After Generation: Approaches focusing on post-generation
verification and refinement, like RARR and High Entropy Word Spotting
and Replacement.

4\. End-to-End: Holistic integration of retrieval mechanisms throughout
the entire text generation process, exemplified by RAG.

By categorising RAG into these subcategories, researchers and developers
can better understand the diverse applications and methodologies of
retrieval augmented generation in mitigating hallucination in large
language models.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*Vqf5oczQPp_leeS-" />
<figcaption>Comparison between the three paradigms of RAG. Source:
Shirui Pan et al., 2023. Link: <a
href="https://arxiv.org/abs/2306.08302">https://arxiv.org/abs/2306.08302</a></figcaption>
</figure>

### The Evolution of RAG Paradigms

1\. Naive RAG: Initial approach following a "Retrieve-Read" methodology,
cost-effective but with limitations.

2\. Advanced RAG: Introduced sophisticated elements like query rewriting
and chunk reranking to enhance LLM performance.

3\. Modular RAG: Further refinement with new modules like Search and
Memory Modules to improve retrieval accuracy.

One interesting aspect of the evolution from Naive RAG to Advanced RAG
and Modular RAG, signifying significant progress in the field. Naive
RAG, initially adopted after ChatGPT, followed a traditional
"Retrieve-Read" approach but had limitations despite being
cost-effective. Advanced RAG introduced advanced elements like query
rewriting and chunk reranking, enhancing LLM performance. Modular RAG
further improved this by adding modules like Search and Memory Modules
for better retrieval accuracy.

This progression addressed the limitations of Naive RAG by integrating
more advanced technologies. Advanced RAG and Modular RAG aimed to
optimise retrieval-augmented generation systems through complex
architectures and new modules. The integration of RAG with fine-tuning,
a notable technique, combines retrieval-based methods and fine-tuning to
enhance RAG model performance effectively. By exploring optimal
integration strategies like sequential or joint training, this hybrid
approach shows promise in enhancing large language models through a
blend of retrieval and fine-tuning methods.

While RAG is a cutting-edge technology with growth potential, it still
faces challenges. Improving user-friendliness, query understanding, data
compatibility, and evaluation methods are key areas for development.
Ongoing research is crucial to unlock RAG's full potential and shape its
future in language models and generative tasks, leveraging its strengths
in integrating retrieval and fine-tuning methods.

### **Knowledge Graph in LLM**

A Knowledge Graph (KG) is an organised collection of data that
represents entities, their attributes, and the relationships between
them in a structured format. KGs enable machines to understand the
semantic meaning and connections within the data, facilitating
sophisticated reasoning, data analysis, and information retrieval.
Integrating KGs into LLMs involves incorporating external knowledge
sources into the pre-training and inference stages of the models. This
integration aims to enhance the understanding of acquired knowledge by
LLMs, enabling them to generate more informed and contextually accurate
responses.

**Knowledge Graph Categories Based on its Implementation:**

1\. KG-enhanced LLMs: This framework focuses on integrating KGs into
LLMs to enhance knowledge understanding during pre-training and
inference. By leveraging external knowledge, this approach aims to
improve LLM performance and interpretability.

2\. LLM-augmented KGs: In this framework, LLMs are utilised for various
KG tasks such as embedding, completion, construction, text generation,
and question answering. The emphasis is on using LLMs to enhance
KG-related tasks by incorporating textual information and improving
downstream task performance.

3\. Synergized LLMs + KGs: This framework emphasises a collaborative
partnership between LLMs and KGs, enabling bidirectional reasoning
through data and knowledge exchange. By leveraging the strengths of both
models, this approach aims to enhance the capabilities of LLMs and KGs
in a mutually beneficial manner.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*G7rOC_hNfsgwuhaF" />
<figcaption>The general roadmap of unifying KGs and LLMs. Source: Shirui
Pan et al., 2023. Link: <a
href="https://arxiv.org/abs/2306.08302">https://arxiv.org/abs/2306.08302</a></figcaption>
</figure>

The frameworks, including KG-enhanced LLMs, LLM-augmented KGs, and
Synergized LLMs + KGs, emphasise the importance of integrating external
knowledge sources into LLMs to enhance knowledge understanding,
interpretability, and task performance. This structured approach
facilitates bidirectional reasoning, data exchange, and collaborative
utilisation of data and knowledge, leading to more informed and
contextually accurate responses generated by LLMs.

Overall, the integration of KGs into LLMs not only enhances model
performance but also broadens the scope of applications in natural
language processing tasks. By leveraging external knowledge sources,
LLMs can access a wealth of information, improve response accuracy, and
provide more contextually relevant outputs, ultimately advancing the
field of computational linguistics and contributing to the development
of more sophisticated language models.

### **Conclusion**

Addressing hallucination mitigation in LLMs involves navigating a
complex challenge through a range of creative methodologies. Looking
ahead, a crucial direction lies in countering hallucinations through the
integration of multiple mitigation strategies, specifically by combining
RAG and Knowledge Graph (KG). Furthermore, diminishing reliance on
labelled data and exploring unsupervised or weakly supervised learning
methods could contribute to enhanced scalability and adaptability.

By adeptly leveraging these techniques, LLMs hold the potential to
produce text that is not only more reliable but also contextually
relevant and factually accurate. This advancement is pivotal for the
field of natural language processing, ensuring responsible AI
development and fostering a more profound understanding of
language nuances.

As we chart the course forward, ongoing research remains paramount.
Continued exploration into the intricacies of combining RAG and KG,
improving user-friendliness, refining query understanding, accommodating
various data types, and establishing robust evaluation methods will be
instrumental. The collaborative efforts of researchers and developers
are key to unlocking the full potential of these approaches and shaping
the future landscape of language models and generative tasks.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=a96c6a33bf02){width="1"
height="1"}

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*Gzmbpq5qJUz2W1zpHd1XGA.png" />
<figcaption>Created using DALL-E on 28 Jan 2024</figcaption>
</figure>

### Authors

* Nakul Nambiar (ORCID: [0009-0009-9720-9233](https://orcid.org/0009-0009-9720-9233))
* Amir Aryani (ORCID: [0000-0002-4259-9774](https://orcid.org/0000-0002-4259-9774))

Knowledge graphs, which offer a structured representation of data and
its relationships, are revolutionising how we organise and access
information. With large amounts of data, it sometimes becomes difficult
to draw insights from it. This blog article examines how to combine
Neo4j, a graph database, with OpenAI's Retrieval-Augmented Generation
(RAG) model to build a robust knowledge management system.

LLMs are pre-trained models and generate outputs based on the prompts we
pass them. Sometimes, the LLM results are precisely what you need, but
other times, they just return random facts from their training data.
Retrieval-augmented generation (RAG) is an AI framework that tries to
improve the response of the LLM models by providing context knowledge
from external sources. Additional benefits of RAG include:

- Enables getting insights into private data without sharing or using it
 for training.
- Reduces the chances of 'Hallucination' and incorrect responses.
- Reduces the need to train the model on new data continuously.

### Sample Data

For our exercise, we will use OpenAI, combined with the Australian
National Graph Database, to create an RAG pipeline.

The [Australian National Graph
Database](https://researchgraph.org/national-graph/) is a vast knowledge
graph with around 11 million nodes. We can't possibly fit all the data
into the OpenAI context. So, in the first phase, we will retrieve a
subset of the graph and then use this graph as a context for the OpenAI
and to answer prompts.

We use a Virtual Machine (VM) with a Neo4j server running the graph
database as a Neo4j server. The [graph
schema](https://researchgraph.org/schema/) includes five primary node
types that describe the research ecosystem: Researchers, Publications,
Datasets, Grants and Organisations.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*UocexTQHeuX3_VAwSJ03CQ.png"
alt="Research Graph Schema: https://researchgraph.org/schema/" />
<figcaption>Research Graph Schema. <a
href="https://researchgraph.org/schema/">https://researchgraph.org/schema/</a></figcaption>
</figure>

### Step 1

Firstly, we must establish the connection to the Neo4j database. Also,
you might notice that we use an OpenAI API key. This key can be
generated at the OpenAI portal. A tutorial is available
[here](https://elephas.app/blog/how-to-create-openai-api-keys-cl5c4f21d281431po7k8fgyol0).

 import os
 import json
 import pandas as pd
 import configparser
 from neo4j import GraphDatabase

 config = configparser.ConfigParser()
 config.read('config.ini')

 # Set the OpenAI API key env variable manually
 os.environ["OPENAI_API_KEY"] = config['DEFAULT']['openai-key']

 # DB credentials
 url = config['DEFAULT']['url']
 username = config['DEFAULT']['username']
 password = config['DEFAULT']['password']

### Step 2

We get a subset of the graph from the larger dataset by querying Neo4j.
The output of the graph is retrieved in JSON format.

 def run_query(cypher_query):
 with GraphDatabase.driver(url, auth=(username, password)) as driver:
 with driver.session() as session:
 result = session.run(cypher_query)
 return result.data()

 cypher_query = """
 MATCH (n:organisation)-[r]-(g:grant)
 where g.source='arc.gov.au' RETURN * LIMIT 100
 """

 result = run_query(cypher_query)

Note: We have used a limit of 100 nodes in the response to limit the
size of the returning JSON file. This would impact the LLM functionality
if it exceeds the context window size.

We create a system prompt by providing background knowledge about the
graph database and the task. Along with it, we pass the JSON subgraph
as context.

 system_prompt = f'''
 You are a helpful agent designed to fetch information from a graph database.
 Data
 {json.dumps(result)}

 This is the output of the Neo4j cypher which returns nodes and relationships. n is the organisations, g is the grants
 and r is the relationships between the grants and organisations.

 Based on this data answer the user prompts and return the output as json.

 '''

Users can then enter the prompt to get insights from the subgraph. For
example, in the prompt below, we are trying to find the grant with the
most relationship to organisations.

 prompt = "Help me find the grants which is related to the most organisations"

### Step 3

We pass the system and user prompts to the OpenAI model. In this case,
we use "gpt-4--1106-preview". More information is available on the
[OpenAI page on Models](https://platform.openai.com/docs/models).

 from openai import OpenAI
 client = OpenAI()

 def define_query(prompt, model="gpt-4-1106-preview"):
 completion = client.chat.completions.create(
 model=model,
 temperature=0,
 response_format= {
 "type": "json_object"
 },
 messages=[
 {
 "role": "system",
 "content": system_prompt
 },
 {
 "role": "user",
 "content": prompt
 }
 ]
 )
 return completion.choices[0].message.content

OpenAI provides the grant details with the most connections to
organisations as a JSON output.

 print(define_query(prompt))

### Output

This is the output provided by OpenAI RAG.

 {
 "grants": [
 {
 "arc_for_primary": "1117",
 "local_id": "LP200301123",
 "start_year": "2023",
 "purl": "http://purl.org/au-research/grants/arc/LP200301123",
 "source": "arc.gov.au",
 "type": "grant",
 "arc_scheme_name": "Linkage Projects",
 "title": "Making Australia resilient to airborne infection transmission.",
 "arc_announcement_administering_organisation": "Queensland University of Technology",
 "url": "http://dataportal.arc.gov.au/NCGP/API/grants/LP200301123",
 "arc_funding_at_announcement": "898013",
 "arc_grant_status": "Active",
 "arc_for": "040101,1117,111705,120404",
 "r_number": "158880661",
 "end_year": "2026",
 "participant_list": "Lidia Morawska,Duzgun Agdas,Laurie Buys,Keith Grimwood,Graham Johnson,Guy Marks,Alexander Paz,Thomas Rainey,Zoran Ristovski,Kirsten Spann,Bo Xia,Patrick Chambers,Shaun Clough,Stephen Corbett,Charles dePuthod,Neal Durant,Travis Kline,Peter McGarry,Ross Mensforth,Clive Paige,John Penny,Brad Prezant,Uma Rajappa,David Ward,Scott Bell,Giorgio Buonanno,Mark Jermy,Jose Jimenez,Claire Wainwright,Sandra Glaister,greg Bell,Louisa O'Toole",
 "key": "arc/LP200301123",
 "related_organisations_count": 24
 }
 ]
 }

We can compare it visually with the graph from Neo4j and see that RAG
provided the correct output.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*PkIQl8fupR-TdiwppqdJkg.png" />
</figure>

We have tested this mini pipeline with other examples, and the
simplicity of the pipeline makes it functional as long as the size of
the subgraph produced in Step 2 does not exceed the context window of
the OpenAI model.

### Conclusion

The integration of OpenAI RAG and Neo4j paves the way for innovative
solutions in knowledge graph applications, and recommendation systems.
RAG is efficient in grounding the LLM on the most recent data, and
verified information and reduces the expense of retraining the model
when new data is available.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=3a183df816ce){width="1"
height="1"}

A novel approach to improving the efficiency of text search in graph
databases utilizing Neo4j, OpenAI, and Typesense.

### Authors

* Nakul Nambiar (ORCID: [0009–0009–9720–9233](https://orcid.org/0009-0009-9720-9233))
* Aishwarya Nambissan (ORCID: [0009–0003–3823–6609](https://orcid.org/0009-0003-3823-6609))

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*3_mdx5SSgImdy1c2RZzl1A.png" />
<figcaption>Created using DALL-E</figcaption>
</figure>

The ability to use cutting-edge tools and frameworks is essential for
staying ahead in the ever-changing field of technology. When it comes to
natural language processing, search capabilities, and graph databases,
**LLM** (Large Language Models), **Typesense**, and **Neo4j** work
together as a powerful trio that offers a cohesive and effective
solution.

As we explore further, we will learn about the unique advantages of
Typesense, Neo4j, and LLM and how their combined powers may open up a
new world of possibilities. This blog is your resource for
comprehending, putting into practice, and optimizing the potential of
this influential trio, whether you're a developer looking for creative
solutions, a data scientist seeking improvements in analytics, or a
business professional trying to maximize information retrieval.

### What is Neo4j and Typesense?

Neo4j is a well-known graph database that specializes in managing
densely linked data. Because of its superior relationship management
capabilities, it is a well-known option for applications with complex
data models. Conversely, Typesense is an extremely quick, open-source
search engine that can quickly search through and retrieve a lot of
text-based data.

While Neo4j is a powerful tool for traversing relationships and querying
graph data, its built-in text search features are not effective for
handling large amounts of text. Here, we will use Typesense to enhance
Neo4j's text search capabilities by using a customized solution for
effective and quick search operations.

### Text Search Challenges in Neo4j

**Memory**: Neo4j relies heavily on memory. Thus, it may consume a lot
of memory for intensive text search queries.

**Lack of Specialised Text Search Features**: Neo4j is a graph database,
and its main function is to handle and store graph-based data. As
compared to other search tools, it lacks some specialized text search
features, which may have a negative impact on the result.

In this article, we are using a Neo4j Graph database that uses this
[schema](https://researchgraph.org/schema) to demonstrate the challenges
for text search and potentially a working solution. The graph includes
five primary objects including research publications, researchers,
organizations, academic grants, and research data collections, capturing
the collaborative network of academic works.

The following is a subset of the schema that contains information
relevant to this article.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*6l7kfZAin3Bwp_gZdU6U1g.jpeg" />
<figcaption>Publication and grant schema from <a
href="https://researchgraph.org/schema/">Research Graph
Meta Model</a></figcaption>
</figure>

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*lrtN5375h6OAuwtGN9KNYA.jpeg" />
<figcaption>Example of the Research Graph schema</figcaption>
</figure>

To illustrate the difficulty at hand, if we wish to find research grants
and publications associated with 'Artificial Intelligence', we will
encounter the following issues:

\(a\) We will need to find all alternative or relevant terms (e.g. AI,
ML, \...)

\(b\) We will need to have a (computationally) expensive text index
search across Neo4j nodes as Neo4j does not particularly perform well
for text search.

To address this issue we are employing a text-based search engine called
Typesense. But first, let\'s look at the advantages and capabilities of
both tools in our toolbox.

### Advantages of Neo4j

**Flexibility:** The graph model is adaptable and may quickly change to
reflect new relationships and data structures.

**Cypher Query Language:** Neo4j uses cypher query language, which is
expressive, readable, and more suitable for graph databases. It makes
retrieving complex patterns from graph databases easier.

**Performance:** Neo4j's underlying architecture is optimized to query
nodes and relationships in a graph efficiently, thus improving the
performance.

**Scalability:** Neo4j supports horizontal scalability. It lets you add
more machines to handle large datasets when needed.

**Transactional Support:** Neo4j ensures data consistency and integrity
in transactional operations by adhering to the ACID (Atomicity,
Consistency, Isolation, Durability) standards.

### Advantages of Typesense

**Speed**: As a search engine designed just for text queries, Typesense
performs very well in terms of speed. We can offload the text-search
tasks to Typesense and increase the speed of the application.

**Rich Text Search**: Typesense provides various advanced text search
features, typo tolerance, faceted, hybrid, and fuzzy search. Hence, it
can accommodate typos and potential errors in user inputs.

**Scalable**: Typesense can scale horizontally. Thus, it can handle
large datasets and a huge volume of queries.

**Easy Integration**: Typesense has simple APIs and connectors for
different programming languages, making integrating with other platforms
and software accessible.

### Vector Search

Typesense can use various small LLM models to create embeddings for the
text and then use these embeddings to do a nearest neighbor
search (KNN).

An embedding for a document is an alternate representation of data as
floating point values. These embeddings are generated using Machine
Learning models. In principle, two similar documents would have
embeddings "closer" to each other.

For example, using OpenAI, we can convert text to embeddings using
OpenAI APIs as below:

 from openai import OpenAI
 client = OpenAI()

 response = client.embeddings.create(
 input="Your text string goes here",
 model="text-embedding-ada-002"
 )

 print(response.data[0].embedding)

The generated embeddings will be as follows:

 {
 "data": [
 {
 "embedding": [
 -0.006929283495992422,
 -0.005336422007530928,
 ...
 -4.547132266452536e-05,
 -0.024047505110502243
 ],
 "index": 0,
 "object": "embedding"
 }
 ],
 "model": "text-embedding-ada-002",
 "object": "list",
 "usage": {
 "prompt_tokens": 5,
 "total_tokens": 5
 }
 }
 

Typesense converts text in each document using the LLM model mentioned
in the schema.

Here are some common models you can use to generate these document
embeddings:

- [Sentence-BERT](https://www.sbert.net/)
- [E-5](https://medium.com/@hansahettiarachchi/unleashing-the-potential-of-embedding-model-e5-revolutionizing-natural-language-comprehension-3f1516489048)
- [CLIP](https://github.com/openai/CLIP)
- [OpenAI's Text Embeddings
 model](https://platform.openai.com/docs/api-reference/embeddings/create)
- [Google's
 PaLM API](https://developers.generativeai.google/products/makersuite)
- [Google's
 Vertex API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings)

You can find the list of built-in models supported by Typesense
[here](https://huggingface.co/typesense/models/tree/main).

For example, below is a schema for a collection of grants that creates
embeddings for the attribute "grant-summary" using the
"all-MiniLM-L12-v2" LLM model for creating the embeddings. The created
embeddings are stored as a new attribute called embedding in the same
document.

 grant_schema = {
 "name": "grant",
 "fields": [
 {"name": "grant-summary", "type": "string"},
 {"name": "flat-field-of-research", "type": "string"},
 {
 "name": "embedding",
 "type": "float[]",
 "embed": {
 "from": [
 "grant-summary"
 ],
 "model_config": {
 "model_name": "ts/all-MiniLM-L12-v2"
 }
 }
 }
 ]
 }

### Demo

We will make use of OpenAI, Typesense, and Neo4j to get relevant data
for a keyword from the Neo4j graph database.

**Resources**: We have a Virtual Machine (VM) with a Neo4j server
running with the grants data embedded on the "grant-summary" attribute.
Typesense is running on the same VM on port 8108. Lastly, we need an
OpenAI API key that can be generated from [OpenAI](https://openai.com/).

In this example, we will search for a keyword and all related related
terms. We will use OpenAI to generate the related words to the keyword.
We will then use these related words to do a semantic search in the
Typesense database to get a list of IDs. Using this list of IDs, we can
then query the Neo4j database to get the graph.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*5ng10X_cLfH_1o2rvK-cwQ.jpeg" />
<figcaption>Pipeline steps for OpenAI-Typesense-Neo4j integrated
search</figcaption>
</figure>

1. First, we accept the user input, which is the keyword that needs to
 be searched.

2\. Then, this keyword is passed to OpenAI, which generates a set of 5
keywords that are related to the given keyword.

 prompt = "We are searching academic articles for research related to a
 keyword presented by the user. Recommend five alternative terms 
 that find related papers to the given keyword. Only create keywords 
 and no other description in your response. Produce CSV"

 user_prompt = "The keyword I want search for articles is" + search_query_term


 import openai

 completion = openai.ChatCompletion.create(
 model="gpt-4-1106-preview",
 messages=[ 
 {"role": "system", "content": prompt},
 {"role": "user", "content": user_prompt}
 ]
 )

For the keyword "Artificial Intelligence", OpenAI generated the
following output below:

 {
 "role": "assistant",
 "content": "\"machine learning\", \"neural networks\", \"cognitive computing\", \"natural language processing\", \"robotics\""
 }

3\. The generated keywords are then searched in Typesense to get a set
of DOIs related to the keywords.

 import typesense

 client = typesense.Client({
 'nodes': [{
 'host': 'localhost', 
 'port': '8108', 
 'protocol': 'http'
 }],
 'api_key': api_key,
 'connection_timeout_seconds': 300
 })

 search_parameters = {
 'searches': [],
 
 }

 for term in completion.choices[0].message["content"].split(','):
 search_object = {
 'q': term.strip(),
 'query_by': 'embedding',
 'exclude_fields': 'embedding',
 'limit': 2,
 'collection': 'publications'
 }
 search_parameters["searches"].append(search_object)

 common_search_params={}
 data = client.multi_search.perform(search_parameters, common_search_params);
 dois = []
 for search_result in data['results']:
 for doc in search_result['hits']:
 dois.append(doc['document']['DOI'])
 print(doc['document']['DOI'] , ' ' , doc['document']['title'])

4\. DOIs can then be easily queried from the Neo4j database to get the
related data and graph.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*27ltmtCCpQPnPvkj6uuetw.png" />
<figcaption>Graph for the keyword “Artificial Intelligence”</figcaption>
</figure>

### Conclusion

To sum up, the integration of Neo4j, OpenAI, and Typesense is a
combination that tackles the difficulties related to text search in
graph databases.

With this integration, developers can take advantage of the benefits of
each of the platforms, making text search more effective, scalable, and
feature-rich. For those looking to improve the search capabilities of
their apps, the Neo4j-OpenAI-Typesense combination seems to be a strong
option as businesses struggle with ever-more complicated data models.

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