Description
Citrus Search provides a novel approach to navigating scientific literature, moving beyond traditional text queries that often result in numerous false positives. By leveraging similarity-based search, researchers can gain a comprehensive overview of their field with a single query. The core functionality involves selecting "seed papers" – foundational or highly relevant articles – and then exploring publications that are closely related.
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What constitutes "related" is determined by user-selectable similarity measures. The "Citation Network" measure identifies papers with similar connections within the citation graph, revealing influential works and their connections. The "Content" measure, on the other hand, finds papers with similar concepts, ideas, and research questions by analyzing their titles and abstracts. This is powered by sophisticated graph and text-based machine learning techniques that compute the similarity between papers.
Citrus Search indexes a vast dataset provided by Semantic Scholar's Open Research Corpus, which encompasses over 200 million publications and approximately 2 billion citations. This extensive coverage ensures that users can discover a wide range of relevant research. The platform presents key contributions at a glance, often visualized on a timeline, which is crucial for understanding the evolution of a research area and ensuring no significant papers are overlooked. This is particularly beneficial for identifying work that might use different terminology or taxonomies than standard text searches would capture.
The service is under active development, with a commitment to continuous improvement based on user feedback. Researchers experiencing bugs or having suggestions for enhancements are encouraged to use the provided feedback form or contact the development team directly via email. Citrus Search aims to be an indispensable tool for academics, students, and R&D professionals seeking to efficiently and effectively explore complex research landscapes.
- CategoryAI Research Assistants
- DeploymentCloud
- PlatformsWeb
Citrus Search's Core Features
Similarity-based search for scientific literature
Select seed papers to initiate search
Explore closely related publications
Citation network similarity measure
Content-based similarity measure (title and abstract)
Graph and text-based machine learning techniques
Overview of research fields
Timeline visualization of important contributions
Indexes Semantic Scholar's Open Research Corpus
Covers over 200 million publications
Includes around 2 billion citations
Helps avoid missing relevant papers
Addresses limitations of traditional text queries
How to use Citrus Search?
Select Seed Papers: Choose foundational or highly relevant articles to start your search.
Choose Similarity Measure: Decide whether to prioritize citation network connections or content similarity.
Explore Related Work: Review the list of publications identified as closely related to your seed papers.
Gain Research Overview: Utilize the timeline view to understand the landscape of important contributions.
Refine Search: Adjust seed papers or similarity measures for more targeted results.
Provide Feedback: Share insights or report issues to aid in active development.
Citrus Search's Use Cases
- Literature Review
- Research Discovery
- Identify Key Contributions
- Avoid Missing Papers
- Explore Research Trends
- Seed Paper Analysis
