Semantic Article Search for STEM Research

Find relevant academic papers using natural language and concept-based search, not just keywords.

Overview

The AI-powered article search engine in SciScoper allows researchers to find academic papers based on meaning, not just keywords. Unlike traditional databases that rely on title and abstract matching, SciScoper’s semantic search understands natural language queries and retrieves research based on conceptual relevance. Users can search for specific methods, datasets, or findings across their own collection or integrated public databases. This enables students to discover relevant research papers they may have missed with manual search strategies, helping them build stronger literature foundations and avoid redundancy in their work.

Key Features

  • Search using full questions or concepts, not just keywords
  • Retrieve papers even if the phrasing or terminology differs
  • Filter by year, domain, method, or citation count
  • Preview semantic summaries before reading full texts
  • Link directly to your PDF library or open-access databases

Benefits

  • Find more relevant and diverse literature faster
  • Save time scanning irrelevant results
  • Support systematic review workflows with precision
  • Integrate with your Zotero or BibTeX library

How It Works

  1. Type a question or research topic (e.g., “impact of microplastics on marine DNA”)
  2. SciScoper analyzes and retrieves the most semantically relevant papers
  3. Preview summaries or citations for each result
  4. Export, cite, or save to your workspace

Frequently Asked Questions

What sources does the search index include?

SciScoper indexes open-access scientific repositories and allows private PDF uploads for personalized search.

Can I use Boolean or keyword queries?

Yes — but our semantic engine also supports full questions and topic-based exploration for deeper insight.

Search Smarter with SciScoper

Try semantic search and surface more relevant science in seconds.