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

Generative AI entails a credit–blame asymmetry

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

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

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

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

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

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

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)

Aishwarya Nambissan

Swinburne University of Technology

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

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”?

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

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

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

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…

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

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

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?

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

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

Amir Aryani

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

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?

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

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…

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

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

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

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

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

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

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)

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

Stories by Research Graph on Medium

### Recent Advances in Using Machine Learning with Graphs --- Part 2 {#recent-advances-in-using-machine-learning-with-graphs-part-2}

#### Latest findings in multiple research directions for handling graph construction and network security issues

<figure>
<img src="https://cdn-images-1.medium.com/max/894/0*VZbI8DObQnry3gYZ" />
<figcaption>Source: Generated using DeviantArt’s DreamUp.</figcaption>
</figure>

### Author

· Xuzeng He (**ORCID:**
[0009--0005--7317--7426](https://orcid.org/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).
Nowadays, There is a rising trend in the research of using Machine
Learning techniques on graphs to solve various kinds of problems.

Part 1 of this article can be
found [here](https://medium.com/@researchgraph/recent-advances-in-using-machine-learning-with-graphs-5a4745e3a3d6).

In this post, we introduce two latest findings where the technique of
Machine Learning is applied to Graphs.

### AutoRD

Rare diseases (e.g. Hutchinson-Gilford Progeria Syndrome), also known as
orphan diseases, are relatively uncommon in isolation and individually
sometimes receive less attention in medical research due to their low
prevalence. In the meantime, as Large Language Models (LLMs) have
demonstrated exceptional proficiency in language understanding and
generation, current research is beginning to evaluate the capabilities
of the most powerful LLMs, such as ChatGPT and GPT-4, across various
medical applications.

In the context of rare diseases, where resources are often limited, LLMs
are able to emerge as valuable tools for extracting information about
these conditions, showcasing their utility in enhancing medical
knowledge systems. For instance, a useful tool called **Automated Rare
Disease Mining** was introduced recently for extracting information
about rare diseases and constructing corresponding knowledge graphs.
This system can process unstructured medical text as input and output
extraction results and a knowledge graph.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*AneLqn1wU6fMBDMp" />
<figcaption>The AutoRD framework processes medical texts as input data
and outputs entities related to rare diseases and rare disease triples,
which are the results of the extraction process. Subsequently, it
constructs a knowledge graph based on these extraction results. During
the entity and relation extraction steps, ontologies-enhanced large
language models (LLMs) are utilised to enhance performance. (Lang Cao et
al., 2024) Article Link: <a
href="https://arxiv.org/abs/2403.00953">https://arxiv.org/abs/2403.00953</a></figcaption>
</figure>

The framework of AutoRD includes data preprocessing, entity extraction,
relation extraction, entity calibration, and knowledge graph
construction.

#### **Data Preprocessing**

- **Segmenting long documents**: Split lengthy documents into smaller
 segments to fit with the LLM being used
- **Processing medical knowledge data**: extract disease names,
 definitions, symptoms, and signs along with their definitions from
 given medical ontology files
- **Preprocessing RareDis dataset**: Correct errors in the original
 dataset annotation and split into training, test, and validation sets
 for evaluation.

#### Entity Extraction

- **Extract medical terms**: Use a string-match algorithm with negation
 detection on a medical ontology (Mondo) to find basic medical terms
 and use LLMs with a prompt to find additional medical terms, including
 those not directly in the ontology.
- **Categorise entities**: Uses LLMs with a prompt to categorise all
 extracted medical terms into entities.

#### Relation Extraction

- Use LLMs with a prompt to extract relations between identified
 entities

#### Entity Calibration

- Use LLMs with a prompt to re-analyse relationships between all
 extracted entities.
- Filter out entities identified as unrelated based on the combined
 information from entity extraction and relation extraction.

#### Knowledge Graph Construction

- Use Neo4j graph database to store and manage the relationships between
 entities as triples (entity-relation-entity) by adding them one by one
 using Neo4j API.

Experimental results show that AutoRD is able to achieve
state-of-the-art performance in terms of efficiency when constructing
rare disease knowledge graphs, opening up exciting possibilities for
future research.

For example, a possible scenario of how AutoRD works for a text about
Hutchinson-Gilford progeria syndrome could be:

**Input medical text:**

Hutchinson-Gilford progeria syndrome(HGPS) is a rare and fatal genetic
disease that causes children to age rapidly. Symptoms typically begin in
the first few months of life and include failure to thrive, wrinkled
skin, and hair loss. There is currently no cure for HGPS, but research
is ongoing.

**AutoRD Output:**

1. **Entity Extraction:** AutoRD starts by extracting entities from the
 medical text. It uses a combination of string matching and LLM
 prompts. In this example, it found several entities related to rare
 diseases, including "Hutchinson-Gilford progeria syndrome",
 "disease", "wrinkled skin", "failure to thrive", and "hair loss".
2. **Relation Extraction:** Next, AutoRD uses LLMs to extract relations
 between the identified entities. It found that "Hutchinson-Gilford
 progeria syndrome" is a type of "disease" and that "disease" can
 have "hair loss" as a symptom.
3. **Knowledge Graph Construction:** Finally, AutoRD constructs a
 knowledge graph to represent the relationships between the entities.
 In this example, the knowledge graph would show that
 "Hutchinson-Gilford progeria syndrome" is a subclass of "disease"
 and that "disease" is associated with "hair loss".

### NEGSC

With the exponential growth in user hosts and network services, the
frequency and the complexity of cyberattacks are increasing.
Fortunately, Network Intrusion Detection Systems (**NIDSs**) have proven
their reliability in detecting and mitigating cyber-attacks, whose main
tasks are to capture malicious network traffic flow and apply the
identification outputs to achieve precise and prompt responses. In
recent studies, Graph Neural Networks (**GNNs**), which are a powerful
deep learning method for graph-structured data, have been introduced
into NIDS due to their suitability for representing the network
traffic flows.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*QaFKYXmJY8yK48n1" />
<figcaption>Converting Netflow-based data into graph representation. An
arrow along with nodes indicates a network traffic flow from the source
host to the destination host. Normal and attack flows are denoted by
black and red arrows, respectively, where different shades of red arrows
indicate different types of attacks. (Renjie Xu et al., 2024) Article
Link: <a
href="https://arxiv.org/abs/2403.01501">https://arxiv.org/abs/2403.01501</a></figcaption>
</figure>

Although Graph Neural Networks (GNNs) are used to analyse network
traffic data (represented as a graph) to detect malicious activity, they
might not fully utilise the rich information present in network flow
data (data about traffic flow between network nodes). To further improve
the performance of GNN, a self-supervised graph representation learning
method for identifying malicious attacks and their specific types,
NetFlow-Edge Generative Subgraph Contrast (**NEGSC**), is proposed.

NEGSC does not require pre-labeled data (which can be scarce for
malicious attacks). It learns by itself by contrasting "good" and
"anomalous" subgraphs within the network traffic graph. It goes beyond
the basic graph structure and considers the specific features present in
network flows (e.g., packet size, source and destination IP addresses).
This allows for a more nuanced understanding of the traffic patterns.
NEGSC focuses on analysing the immediate neighbors (directly connected
nodes) of a central node. This helps identify suspicious patterns in
local traffic flows that might indicate an attack. It employs a learning
framework called Generative Subgraph Contrast(**GSC**) and generates
*contrastive subgraphs* for a given central node. These subgraphs
represent possible "good" and "anomalous" traffic patterns around that
node. It then uses a loss function to compare the generated contrastive
subgraphs. If the real traffic data around a node deviates significantly
from the "good" subgraph, it might indicate a potential attack.

Experimental results show that this approach significantly outperforms
the existing self-supervised learning method in terms of [Accuracy,
Precision, Recall and
F1](https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9)
when given a binary classification scenario.

### Conclusion

The usage of Machine Learning has witnessed significant advancements,
particularly when combined with various kinds of graphs. This can open
multiple exciting possibilities for different research directions. In
this post, we discussed two latest findings for the use of Machine
Learning when combined with graphs. Moreover, we have also shared some
promising scopes that could be used for future exploration of these
approaches. Through continuous investigation and refinement, we believe
that the use of graphs can open up exciting opportunities for us along
with different machine learning techniques.

### References

- Cao, L., Sun, J. and Cross, A. (2024). AutoRD: An Automatic and
 End-to-End System for Rare Disease Knowledge Graph Construction Based
 on Ontologies-enhanced Large Language Models. *arXiv (Cornell
 University)*. doi:https://doi.org/10.48550/arxiv.2403.00953.
- Xu, R., Wu, G., Wang, W., Gao, X., He, A. and Zhang, Z. (2024).
 Applying Self-supervised Learning to Network Intrusion Detection for
 Network Flows with Graph Neural Network. *arXiv (Cornell University)*.
 doi:https://doi.org/10.48550/arxiv.2403.01501.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=88e27d32ed9f){width="1"
height="1"}

Latest findings in multiple research directions for handling graph
prediction and optimization

<figure>
<img src="https://cdn-images-1.medium.com/max/894/0*sb13bRUAq0OUErOQ" />
<figcaption>Source: Generated using DeviantArt’s DreamUp</figcaption>
</figure>

### Author

· Xuzeng He (**ORCID:**
[0009--0005--7317--7426](https://orcid.org/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 graphs (e.g.
Wikipedia). Nowadays, there is a rising trend in the research of using
Machine Learning techniques on graphs to solve various kinds of
problems.

In this post, we introduce two latest findings where the technique of
Machine Learning is applied to Graphs.

### UFKGC

Knowledge graphs (**KGs**) represent information pieces consisting of
entities and their relations, usually organised in the form of
triples ---** head entity**, **relation**, and **tail entity**. For
example, a KG might show that "Chicago Bulls" (head entity) is "located"
(relationship) in "America" (tail entity).

In real-world scenarios, relations in the already-existing KGs generally
suffer from the so-called long-tail problem, meaning that a large number
of relations only occur occasionally in a KG (limited number of sets of
head and tail entities for these relations), which makes the traditional
KG completion methods impractical with these insufficient
training samples.

Most KGs contain many more **rare relations** than **common relations**.

- A common relation, like "located in," might connect countless cities
 and countries.
- Rare relations, on the other hand, might describe very specific
 things, like "known for the invention of the stapler" (person,
 stapler). These relations only connect a handful of entities.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*-SavSdENTjH-SCVr" />
<figcaption>An example of the few-shot KG completion task. (a) The task
is to predict tail entities on KG. (b) The task utilises only K triples
for the specific relation to predicting others. (Qian Li et al., 2018)
Article Link: <a
href="https://arxiv.org/abs/2403.04521">https://arxiv.org/abs/2403.04521</a></figcaption>
</figure>

In this case, the few-shot KG completion (**FKGC**) task, which predicts
the tail entity from a given head entity and a specific relation as well
as a few triples having the same relation, has gained wide attention.
FKGC aims to predict the missing tail entity in a KG, but with a
different approach: it only uses **a few examples** (called "few-shot")
of the specific relation it's dealing with. These few examples are
provided as a set of triples (head, relation, tail) that share the same
relation you are trying to predict for.

However, uncertainty usually arises when the model is provided with a
limited number of samples. To further enhance the performance of FKGC, a
framework called Uncertainty-aware Few-Shot Knowledge Graph Completion
(**UFKGC**) was proposed recently to model uncertainty for a better
understanding of the limited data and expand the data under the same
distribution.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*QnFmZChHuJCSpxwz" />
<figcaption>The framework of UFKGC.(Qian Li et al., 2018) Article Link:
<a
href="https://arxiv.org/abs/2403.04521">https://arxiv.org/abs/2403.04521</a></figcaption>
</figure>

The framework of UFKGC contains three different modules, including
Uncertainty Representation, UR-GNN(uncertainty-aware relational graph
neural network), and Uncertainty Optimization.

**Uncertainty Representation** --- *Estimate the uncertainty of
entities/relations in the form of Gaussian Distribution.*

- Consider a knowledge graph statement like "Albert Einstein wrote the
 Theory of Relativity." There might be some uncertainty about other
 works Einstein wrote (less certain) compared to the well-established
 fact that he wrote the Theory of Relativity (more certain).
- This module aims to quantify this uncertainty by representing it as a
 Gaussian Distribution, a mathematical way to describe the probability
 of different values occurring.

**UR-GNN **--- *Get better integration using the learned Gaussian
Distribution*

- This module builds upon the concept of a Graph Neural Network
 (**GNN**), a type of AI model adept at learning from interconnected
 data like a knowledge graph.
- The "uncertainty-aware" part comes in here. The UR-GNN incorporates
 the uncertainty information (Gaussian Distributions) estimated in the
 previous module.
- By considering this uncertainty, the UR-GNN can potentially achieve a
 better understanding of the relationships between entities in the KG,
 even with limited data.

**Uncertainty Optimization **--- *Using multiple sampling from the
learned distributions to evaluate the completion prediction*

- This module leverages the uncertainty information to improve the final
 prediction of the missing entity.
- It utilizes a technique called "multiple sampling." The UR-GNN model,
 informed by the uncertainty, proposes multiple possible tail entities
 (the missing part) for a given head entity and relation, considering
 the probability distribution learned earlier.
- By evaluating these multiple proposals, the Uncertainty Optimization
 module aims to identify the most likely tail entity to complete the
 knowledge graph statement accurately.

Experimental results show that UFKGC achieves state of the art results
in terms of accuracy on two FKGC benchmark datasets, opening exciting
possibilities for future research.

### Bilevel Optimization

Graph machine learning covers a wide range of modelling tasks involving
graph-structured data, where cross-instance/node dependencies are
reflected by edges. Cross-instance or node dependencies refer to the
fact that information about one node can be crucial for understanding
another node, especially if they're connected by an edge. For instance,
in a social network, knowing your friend's interests (one node) might
influence what content you see (another node). Among all the graph
machine learning techniques, Graph Neural Networks (**GNNs**) have
emerged as a promising class of predictive models for handling tasks
such as node classification (assign labels to nodes in the graph) or
link prediction (predict the existence of a link between two entities).

However, the graph machine learning models in the past have primarily
been motivated from diverse perspectives, without necessarily a single,
transparent lens with which to evaluate their commonalities. In this
case, the idea of operating from a unifying vantage point afforded by
bilevel optimization was recently proposed.

Here, bilevel optimization refers to an optimization problem
characterised by two interconnected levels, whereby the optimal solution
of a lower-level objective serves as input features to an upper-level
decision. For example, we can consider the following scenario:

**Upper-level Problem** --- *Teacher*

- A teacher wants to design a quiz that maximises the average score of
 the students in the class. The teacher can choose the difficulty level
 and the number of questions for the quiz.

**Lower level Problem **--- *Student*

- Each student aims to maximise their individual score on the quiz by
 choosing how much time to allocate for studying and which topics to
 focus on.

In this scenario, the study efforts and topic choices made by individual
students can influence their quiz scores, thus impacting the teacher's
objective of maximising the class's average score as input features.

Based on the same idea, a broadly applicable bilevel optimization
framework called **BloomGML** was proposed, which is capable of handling
generic GNN tasks besides node classification. Experimental results show
that BloomGML achieves state-of-the-art performance in terms of accuracy
when given tasks such as node classification, which can provide avenues
for future research.

### Conclusion

The usage of Machine Learning has witnessed significant advancement,
particularly when combined with various kinds of graphs. This can open
multiple exciting possibilities for different research directions. In
this post, we discussed two latest findings for the use of Machine
Learning when combining different graphs. Moreover, we have also shared
some promising scopes that could be used for future exploration of these
approaches. Through continuous investigation and refinement, we believe
that the use of graphs can open up exciting opportunities for us along
with different machine learning techniques.

### References

\[1\] Li, Q., Guo, S., Chen, Y., Ji, C., Sheng, J., & Li, J. (2024).
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge
Graph Completion.

\[2\] Zheng, A. Y., He, T., Qiu, Y., Wang, M., & Wipf, D. (2024).
BloomGML: Graph Machine Learning through the Lens of Bilevel
Optimization.

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=5a4745e3a3d6){width="1"
height="1"}

### How to use GROBID to extract text from PDF

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*uyD7hkSBZGCOn4wqu2TgMw.png" />
<figcaption>Created using DALL.E on 13 Feb 2024</figcaption>
</figure>

### Author

* Aland Astudillo (ORCID: [0009-0008-8672-3168](https://orcid.org/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. The ability to efficiently and
accurately read the content of PDF documents using automated tools and
transform this data into a structured model provides many opportunities
for the integration of PDF collections to expert systems, and
AI-assisted data pipelines such as chatbots.

In this article, we will discuss GROBID capabilities, explain how to
install it, and explore how to use it in a Python script or Jupyter
Notebook.

### **What is GROBID?**

GROBID stands for GeneRation of Bibliographic Data and is a machine
learning library for extracting, parsing, and re-structuring raw
documents such as PDF into structured XML/TEI encoded documents. It has
a particular focus on technical and scientific publications. TEI stands
for Text Encoding Initiative (<https://tei-c.org/Guidelines/>). It
defines and documents a markup language for representing the structural,
renditional, and conceptual features of texts.

### How it works?

GROBID is a machine-learning library that extracts information from PDF
files. The goal of GROBID is to help the text mining process,
information extraction, and semantic analysis of scientific publications
by transforming them into machine-friendly, structured, representations
with predictable structure.

Despite the large amount of PDF files and their extensive use among all
fields, it is still a big challenge to process them because they are
encoded in a vast variety of different publisher formats, often
incomplete or inconsistent. In short, PDF files are difficult to exploit
and use.

In a nutshell, GROBID uses a cascade of "sequence labelling models" to
parse a document. Each model from the collection of models is in charge
of identifying a small amount of labels to classify areas and
information in the document. The final models produce 55 labels where
each piece of identified information from the document will be
structured. More information about how this library has been developed
and the machine learning models are in this link:
<https://grobid.readthedocs.io/en/latest/Principles/>

### How to install GROBID

Here we explain the installation of the GROBID tool as a service in a
Linux machine (Ubuntu for example). The same steps apply to a Windows
system using the Windows Subsystem for Linux tool (WSL version 2).

For Linux, in the terminal with super user (sudo) rights create a folder
and once inside, proceed to download the last version of GROBID using
the wget tool. The current version of GROBID is v 0.8.0. After that,
unzip the file:

 > wget https://github.com/kermitt2/grobid/archive/0.8.0.zip
 > unzip 0.8.0.zip

For Windows, you can use Docker containers to install and run the GROBID
service locally. More documentation is available at
<https://grobid.readthedocs.io/en/latest/Run-Grobid/>

Note: At the time of writing this article, GROBID requires JAVA(JDK) and
it works fine with JDK version 1.11 up to version JDK 1.17. Other recent
JDK versions should work correctly. To check your JAVA installation, in
the terminal use the following command:

 > java -version

If you do not have any JAVA installed, proceed to install JAVA using the
following line in the terminal.

 > sudo apt install default-jdk

It is a good practice to check Java after installation to ensure the
right version of Java is installed and ready to operate on your system.

### **How to run GROBID**

Once you downloaded and unzipped the "*grobid"* file, proceed to the
folder and run the grewed file. In the terminal ensure to use "./"
before the command "*grewed".*

 > cd grobid-0.8.0

 > ./grewed run

The first time that you run the service, it will download and install
all the required dependencies and then it will run the service. It will
be completed by printing a message "**:grobid-service:run"** as
presented in the following screenshot.

<figure>
<img
src="https://cdn-images-1.medium.com/max/920/1*gEsGJ87IUzEovXwcFRDCzg.png" />
</figure>

### **How to use GROBID**

After running the GROBID service, you can connect to the created server
in the following direction using your browser:

<http://localhost:8070/>

This follows the default configuration, but you can change the port in
the config file. The image below shows how it looks the first time that
you access the web layout.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1003/1*N5URo-rzuOb_1Cu15yEKEA.png" />
</figure>

The TEI menu will allow you to choose among the different services to
call and to upload a file with the button "Select file". The following
functionalities are available as a service:

- Header extraction and parsing
- References extraction and parsing
- Citation contexts recognition and resolution
- Full-text extraction and structuring
- PDF coordinates
- Parsing of references
- Parsing of names
- Parsing of affiliation and address
- Parsing of dates
- Consolidation and resolution of the extracted bibliographical
 references
- Extraction and parsing of patent and non-patent references in patent
 publications
- Extraction of funders and funding information

Once you upload a PDF document and choose the required service, you can
press the Submit button

<figure>
<img
src="https://cdn-images-1.medium.com/max/995/1*m3zDNnvFesFQuQ7Bv0U3yA.png" />
</figure>

After a few seconds, the web will show you the XML structure of the
output depending on what service you have selected. You can download the
resulting XML structure in an XML file. The image below shows the result
of processing the full-text document for a given paper. For example, in
the image below we used the article *Pan et al., 2024 Unifying Large
Language Models and Knowledge Graphs: A Roadmap. arXiv:2306.08302*
downloaded from arXiv (<https://arxiv.org/abs/2306.08302>)

<figure>
<img
src="https://cdn-images-1.medium.com/max/993/1*xTfpktk_4nH1dOPqoETvkg.png" />
</figure>

### **How to use GROBID Python Client**

The GROBID Python client is a handy tool that can process efficiently
and concurrently a set of PDF files in a given folder using GROBID as a
service without requiring the web option. It includes a command line for
processing PDFs and writing the results in a given folder. It allows us
to use this tool inside Python scripts. To use this client, It assumes
that you are running GROBID service, as we saw in the previous steps.

The repository of this client is here
<https://github.com/kermitt2/grobid_client_python/blob/master/grobid_client/grobid_client.py>

To install the GROBID Python client to use in your scripts or Jupyter
Notebook, in your Python environment, you can use the pip manager in a
given environment:

 !pip install grobid_client_python

If you want to use this client in a script or a Jupyter Notebook, you
must import the GROBID client class. Remember that it assumes that the
GROBID service is already running.

 from grobid_client.grobid_client import GrobidClient

 client = GrobidClient(config_path="./config.json")

Once the client connects to the service it will show you the following
message.

 "GROBID server is up and running"

### **How to analyse a bunch of PDF documents**

After checking the connection, you can call the process method to
analyse a bunch of documents in a folder. The process method has 3
required parameters and other optional parameters. In the following
example we are using the service "**processFulltextDocument**" for
processing all the pdf files in the folder "**resources/test_pdf**" and
get the xml files output on the folder "**resources/test_out/**".
Additionally, we set the consolidate citation option and the TEI
coordinates to "true".

 client.process("processFulltextDocument", 
 "./resources/test_pdf", 
 output="./resources/test_out/", 
 consolidate_citations=True, 
 tei_coordinates=True, 
 force=True)

In the defined folder, the output will be the XML files for each PDF
file with the re-structured information.

### **How to analyse a single PDF file**

For analysing one PDF file at a time, you can use the method
**process_pdf**, with similar parameters as the **process** method:

 service_name = "processFulltextDocument"

 pdf_file = "./pdf_examples/input/0046d83a-edd6-4631-b57c-755cdcce8b7f.pdf"

 rsp = client.process_pdf(service_name, pdf_file, 
 generateIDs=True, 
 consolidate_header=True, 
 consolidate_citations=True, 
 include_raw_citations=True, 
 include_raw_affiliations=True, 
 tei_coordinates=True, 
 segment_sentences=True)

The following text snippet shows the top section of the "rsp" variable
that holds the results from the PDF processing.

 ('./pdf_examples/input/0046d83a-edd6-4631-b57c-755cdcce8b7f.pdf',
 200,
 '<?xml version="1.0" encoding="UTF-8"?> 
 <TEI xml:space="preserve" xmlns="http://www.tei-c.org/ns/1.0" 
 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" 
 xsi:schemaLocation="http://www.tei-c.org/ns/1.0 https://raw.githubusercontent.com/kermitt2/grobid/master/grobid-home/schemas/xsd/Grobid.xsd" 
 xmlns:xlink="http://www.w3.org/1999/xlink"> 
 <teiHeader xml:lang="en"> 
 <fileDesc> 
 <titleStmt> 
 <title level="a" type="main" xml:id="_nrS3kCw">Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site</title> 
 <funder ref="#_ZTFTHCu"> 
 <orgName type="full">German Federal Ministry for Education and Research [BMBF</orgName> 
 </funder> 
 <funder ref="#_xTkqs2w"> 
 <orgName type="full">German Research Council</orgName> 
 </funder> 
 <funder ref="#_dV4wQd4 #_dVJJnCd"> 
 <orgName type="full">Federal Ministry for Education and Research</orgName> 
 </funder> 
 </titleStmt> 
 <publicationStmt> 
 <publisher>Springer Science and Business Media LLC</publisher> 
 <availability status="unknown">
 Copyright Springer Science and Business Media LLC 
 </availability> 
 <date type="published" when="2016-03-08">2016-03-08</date> 
 </publicationStmt> 
 <sourceDesc> 
 <biblStruct> 
 <analytic> 
 <author> 
 <persName coords="1,56.69,234.15,85.88,9.71"><forename type="first">Cristiano</forename>
 <surname>De Marchis</surname></persName> 
 <email>cristiano.demarchis@uniroma3.it</email> 
 <affiliation key="aff0"> 
 <note type="raw_affiliation"><label>1</label> Division of Functional and Restorative Neurosurgery, Department of Neurosurgery, Eberhard Karls University, Otfried-Mueller-Str.45, 72076 Tübingen, Germany</note> 
 <orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName> 
 <orgName type="department" key="dep2">Department of Neurosurgery</orgName> 
 <orgName type="institution">Eberhard Karls University</orgName> 
 <address> 
 <addrLine>Otfried-Mueller-Str.45</addrLine> 
 <postCode>72076</postCode> 
 <settlement>Tübingen</settlement> 
 <country key="DE">Germany</country> 
 </address> 
 </affiliation> 
 </author> 
 …

(Note: The result was truncated)

As you can see, the information is organised hierarchically, using
standardised tags \<tag\> to identify each type of information of the
publication. For instance, the tag *\<titleStmt\>* contains sub-tags
holding the title of the article.

 <title level="a" type="main" xml:id="_nrS3kCw">
 Multi-contact functional electrical stimulation for hand opening: electrophysiologically driven identification of the optimal stimulation site
 </title> 

Other tags contain information about authors, affiliations, and so on.
For example, the identification of the organisation is in the tag
*\<orgName\>*

 <orgName type="full">
 German Federal Ministry for Education and Research [BMBF
 </orgName> 

Information about the authors is inside the tags *\<author\>* containing
details of each author, complete name (\<forename), last name
(*\<surname\>*), email (*\<email\>*), affiliations (*\<affiliation
key="aff0\"\>*), and all other information like the address of the
institution to which the author is affiliated (*\<address\>*), and all
the information contained in the article about the author.

 <author> 
 <persName coords="1,56.69,234.15,85.88,9.71">
 <forename type="first">Cristiano</forename>
 <surname>De Marchis</surname>
 </persName> 
 <email>cristiano.demarchis@uniroma3.it</email> 
 <affiliation key="aff0"> 
 <note type="raw_affiliation"><label>1</label> Division of Functional and Restorative Neurosurgery, Department of Neurosurgery, Eberhard Karls University, Otfried-Mueller-Str.45, 72076 Tübingen, Germany</note> 
 <orgName type="department" key="dep1">Division of Functional and Restorative Neurosurgery</orgName> 
 <orgName type="department" key="dep2">Department of Neurosurgery</orgName> 
 <orgName type="institution">Eberhard Karls University</orgName> 
 <address> 
 <addrLine>Otfried-Mueller-Str.45</addrLine> 
 <postCode>72076</postCode> 
 <settlement>Tübingen</settlement> 
 <country key="DE">Germany</country> 
 </address> 
 </affiliation> 
 </author>

In the same way, we can retrieve information on the citations of the
article in the tag for references. For example, an article in the list
of references has the following structure

 <biblStruct coords="8,319.75,636.26,214.89,6.62;8,319.75,645.27,210.17,6.62;8,319.75,654.28,118.20,6.62" xml:id="b0">
 <analytic>
 <title level="a" type="main">The influence of functional electrical stimulation on hand motor recovery in stroke patients: a review</title>
 <author>
 <persName xmlns="http://www.tei-c.org/ns/1.0">
 <forename type="first">Fanny</forename>
 <surname>Quandt</surname>
 </persName>
 </author>
 <author>
 <persName xmlns="http://www.tei-c.org/ns/1.0">
 <forename type="first">Friedhelm</forename>
 <forename type="middle">C</forename>
 <surname>Hummel</surname>
 </persName>
 </author>
 <idno type="DOI">10.1186/2040-7378-6-9</idno>
 </analytic>
 <monogr>
 <title level="j">Experimental & Translational Stroke Medicine</title>
 <title level="j" type="abbrev">Exp & Trans Stroke Med</title>
 <idno type="ISSNe">2040-7378</idno>
 <imprint>
 <biblScope unit="volume">6</biblScope>
 <biblScope unit="issue">1</biblScope>
 <biblScope unit="page">9</biblScope>
 <date type="published" when="2014-08-21"/>
 <publisher>Springer Science and Business Media LLC</publisher>
 </imprint>
 </monogr>
 </biblStruct>

In which we can identify the title tag (*\<title level="a"
type="main"\>*), authors information (*\<author\>*), DOI (*\<idno
type="DOI"\>*), and the journal details (*\<monogr\>*) of the referenced
article.

Finally, to get each piece of information from this structure for
further analysis, the next steps involves extracting the required
information based on the tags of the XML structure, which can be done
with different tools to match these tags in the text. That said, the
text is ready for mining all the information.

### **Conclusion**

We have explored the important aspects of GROBID. With its simple
installation process user interface, and scripts, we can put GROBID to
work easier than ever. Whether you are facing mountains of PDFs or
struggling to tame unruly bibliographic data, GROBID is your trusty
companion on the path to research efficiency. Download GROBID today and
unlock the door to a world of streamlined workflows, saving time, and
deeper insights from your research endeavours.

**Materials for further reading:**

- <https://grobid.readthedocs.io/en/latest/>
- <https://github.com/kermitt2/grobid_client_python>

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