Objective
Create a research paper repository with citation tracking and semantic search. This enables researchers to discover related papers, track citations, and explore research topics efficiently.
Step 1: Create Papers Table
Create a table for research papers.
CREATE TABLE research_papers (
id INTEGER PRIMARY KEY,
paper_pdf PDF,
title VARCHAR(500) NOT NULL,
abstract TEXT,
publication_type VARCHAR(50),
journal_name VARCHAR(255),
conference_name VARCHAR(255),
publication_date DATE,
doi VARCHAR(100) UNIQUE,
keywords VARCHAR(500),
field_of_study VARCHAR(100),
citation_count INTEGER DEFAULT 0,
abstract_embedding VECTOR(384),
is_open_access BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
Step 2: Create Authors Table
Create a table for paper authors.
CREATE TABLE paper_authors (
id INTEGER PRIMARY KEY,
paper_id INTEGER NOT NULL,
author_name VARCHAR(255) NOT NULL,
author_order INTEGER NOT NULL,
affiliation VARCHAR(255),
email VARCHAR(255),
orcid VARCHAR(50),
is_corresponding BOOLEAN DEFAULT FALSE,
FOREIGN KEY (paper_id) REFERENCES research_papers(id)
);
Step 3: Create Citations Table
Track paper citations.
CREATE TABLE paper_citations (
id INTEGER PRIMARY KEY,
citing_paper_id INTEGER NOT NULL,
cited_paper_id INTEGER NOT NULL,
citation_context TEXT,
citation_type VARCHAR(50),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (citing_paper_id) REFERENCES research_papers(id),
FOREIGN KEY (cited_paper_id) REFERENCES research_papers(id)
);
Step 4: Insert Sample Papers
Add research paper records.
INSERT INTO research_papers (id, title, abstract, publication_type, journal_name, publication_date, doi, keywords, field_of_study, citation_count, is_open_access) VALUES
(1, 'Deep Learning for Natural Language Processing: A Comprehensive Survey',
'This survey provides a comprehensive review of deep learning techniques applied to natural language processing tasks including text classification, named entity recognition, and machine translation.',
'journal', 'Journal of Machine Learning Research', '2023-06-15', '10.1234/jmlr.2023.001',
'deep learning,NLP,neural networks,transformers', 'Computer Science', 245, TRUE),
(2, 'Transformer Architectures: Attention Mechanisms and Beyond',
'We present an analysis of transformer architectures focusing on self-attention mechanisms and their variants. Our study examines computational efficiency and performance trade-offs.',
'conference', NULL, '2023-09-20', '10.1234/conf.2023.002',
'transformers,attention,deep learning,efficiency', 'Computer Science', 180, TRUE),
(3, 'Federated Learning for Healthcare: Privacy-Preserving Machine Learning',
'This paper explores federated learning applications in healthcare settings, enabling collaborative model training while preserving patient data privacy.',
'journal', 'Nature Medicine AI', '2023-11-10', '10.1234/nmai.2023.003',
'federated learning,healthcare,privacy,machine learning', 'Healthcare AI', 95, FALSE),
(4, 'Graph Neural Networks for Molecular Property Prediction',
'We introduce a novel graph neural network architecture for predicting molecular properties with applications in drug discovery and materials science.',
'journal', 'Journal of Chemical Information', '2023-08-05', '10.1234/jci.2023.004',
'graph neural networks,molecular,drug discovery,chemistry', 'Computational Chemistry', 120, TRUE),
(5, 'Reinforcement Learning in Robotics: A Practical Guide',
'A practical guide to implementing reinforcement learning algorithms for robotic control tasks, covering simulation-to-reality transfer and sample efficiency.',
'conference', NULL, '2024-01-15', '10.1234/conf.2024.005',
'reinforcement learning,robotics,control,simulation', 'Robotics', 45, TRUE),
(6, 'Large Language Models: Capabilities and Limitations',
'We analyze the capabilities and limitations of large language models, examining emergent abilities, reasoning capabilities, and failure modes.',
'journal', 'AI Magazine', '2024-02-01', '10.1234/aim.2024.006',
'large language models,GPT,emergent abilities,AI safety', 'Computer Science', 320, TRUE);