← Back to cohort

Hamza Mahmood

FAST · 2021 · i17 - 0054

—

Phone

—

GitHub

—

Academic

Program

—

CGPA

—

Year

2021

Education

—

Address

—

DOB

—

Career

Current role

—

Target role

—

Skills

Neo4j, Python 3.8, Flask, Jupyter Notebook, Google Colab, PyCharm, HTML, CSS, Javascript

Interests / quote

Our Project, Athena, is an automated graph based Knowledge Extraction System that aims to solve the challenges of linked data by providing users the ability to retrieve and extract meaningful information without the need of having expert domain knowledge. Our system has successfully achieved the conversion of raw text from various genres to a well structured Graph format. This has enabled our users to structure, store, retrieve and analyse data quickly, in runtime. Additionally, the live Graph Database will be queryable; The main medium for handling these queries will be Cypher, the robust language backed by Neo4j. Once a cypher query is formulated, information is passed along from the knowledge graph to the user via a query interface in an efficient manner.

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.

Athena 

Our Project, Athena, is an automated graph based Knowledge Extraction System that aims to solve 
the challenges of linked data by providing users the ability to retrieve and extract meaningful 
information without the need of having expert domain knowledge. Our system has successfully 
achieved the conversion of raw text from various genres to a well structured Graph format. This has 
enabled our users to structure, store, retrieve and analyse data quickly, in runtime. Additionally, the 
live Graph Database will be queryable; The main medium for handling these queries will be 
Cypher, the robust language backed by Neo4j. Once a cypher query is formulated, information is 
passed along from the knowledge graph to the user  via a query interface in an efficient manner. 
Features of Athena include: 
➢ Knowledge graph construction at runtime  
➢ Automatic conversion of unstructured text to a structured form 
➢ Robust Querying options 
➢ User friendly interface 
Conversion of raw unstructured text to a Knowledge Graph 








Technology Used: 
Neo4j, Python 3.8, Flask, Jupyter Notebook,Google Colab, 
 PyCharm, HTML,CSS, Javascript 
Supervisor Name: 
Dr. Omer Beg 
Group Members:   
Hamza Mahmood (i17 - 0054) 
Azfar Bakht (i17 - 0158)                    
Dania Zahid (i17 - 0175)

AI enrichment

Hamza Mahmood contributed to Athena, an automated knowledge extraction system that converts unstructured text into structured graph formats using Neo4j and Cypher. The project involved building a runtime knowledge graph with a user-friendly interface for efficient data retrieval and analysis.

Skills (AI)

["Python", "Neo4j", "Cypher", "Flask", "Knowledge Graphs", "HTML", "CSS", "JavaScript", "Jupyter Notebook", "Google Colab"]

Status: ai_done

Provenance

Source file: Graduate Directory FAST School of Computing 2021 (1st Final) (1).pdf
From job #24 page 194
Created: 1778170889