1. Introduction

In the ever-expanding universe of scholarly literature, navigating through millions of research papers to find relevant, impactful, and accessible information is a monumental challenge for academics, students, and industry researchers. The digital era has ushered in a new generation of academic search engines and discovery tools designed to tackle this information overload. Among the front-runners are Semantic Scholar and CORE, two powerful platforms that offer distinct approaches to organizing and delivering scientific knowledge.

Semantic Scholar, developed by the Allen Institute for AI, leverages artificial intelligence to understand the content and context of academic papers, providing researchers with intelligent summaries and citation analysis. Conversely, CORE (Connecting Repositories) positions itself as the world's largest aggregator of open access research papers, focusing on providing unrestricted access to a vast collection of scholarly works from repositories and journals globally.

This article provides a comprehensive comparison of Semantic Scholar and CORE, delving into their core features, technical capabilities, user experience, and overall value proposition. We will analyze their strengths and weaknesses to help you determine which tool is better suited for your specific academic and research needs, whether you prioritize AI-driven insights or unfettered access to open-access content.

2. Product Overview

2.1 Semantic Scholar Overview

Semantic Scholar is a free, AI-powered research tool developed by the Allen Institute for AI (AI2) and publicly released in 2015. Its primary mission is to use advanced techniques in artificial intelligence, including natural language processing and machine learning, to help scholars navigate the overwhelming volume of scientific literature. Unlike traditional keyword-based search engines, Semantic Scholar analyzes the content of papers to identify key information, connections, and the underlying context of research.

The platform indexes over 200 million papers across all fields of science. Key features include "TLDR" summaries that provide a one-sentence overview of a paper, influential citation identification, and an augmented PDF reader called the "Semantic Reader." These tools are designed to help users quickly assess a paper's relevance and significance, making the research process more efficient.

2.2 CORE Overview

CORE, which stands for "Connecting Repositories," is a service managed by The Open University and Jisc in the United Kingdom. Its fundamental goal is to aggregate open access research papers from institutional repositories, journals, and archives around the world into a single, searchable platform. As of 2024, CORE provides access to hundreds of millions of scholarly articles, with a significant portion available as full text.

The platform's core strength lies in its unwavering commitment to open access. By harvesting metadata and full-text content, CORE aims to maximize the visibility, accessibility, and reuse of research outputs. It serves a diverse audience, including researchers, academic institutions, companies, and the general public, providing powerful services for content discovery, compliance monitoring, and data analysis through APIs and datasets.

3. Core Features Comparison

Both Semantic Scholar and CORE offer robust feature sets tailored to the needs of the academic community, but their approaches and strengths differ significantly.

Feature	Semantic Scholar	CORE
Primary Focus	AI-driven semantic search and paper analysis	Aggregation of open access full-text articles
Database Size	Over 200 million papers	Over 400 million articles indexed
AI-Powered Features	TLDR Summaries Semantic Reader Influential Citation Identification Personalized Research Feeds	Text and data mining for metadata enhancement Recommender system CORE-GPT (Q&A platform in development)
Search Functionality	Semantic search based on intent and context Advanced filters (author, date, etc.)	Keyword and metadata-based search Filters for institution, subject, and language
Citation Analysis	Identifies highly influential citations Provides citation graphs and context	"Cited by" feature is limited Focus is on aggregation, not citation metrics
Full-Text Access	Links to publisher sites and PDFs when available	Direct access to millions of full-text open access articles

3.1 AI-Driven Discovery vs. Open Access Aggregation

Semantic Scholar's key differentiator is its use of AI-powered tools. The TLDR feature automatically generates concise, one-sentence summaries of papers, allowing researchers to quickly grasp the core findings without reading the entire abstract. The Semantic Reader enhances the reading experience by providing contextual information, definitions, and inline citation details. These features are designed to accelerate the literature review process by adding a layer of intelligent analysis on top of the search results.

In contrast, CORE's primary mission is to be the most comprehensive open access repository. Its main feature is the sheer scale of its aggregated content. Researchers can find and download millions of full-text articles that might otherwise be behind paywalls. While it uses AI for tasks like metadata enrichment and recommendations, its user-facing features are centered on discovery and access rather than deep semantic analysis of individual papers.

4. Integration & API Capabilities

For developers, institutions, and other platforms, the ability to integrate with these tools programmatically is crucial. Both Semantic Scholar and CORE offer powerful APIs, but with different focuses.

4.1 Semantic Scholar API

Semantic Scholar provides a robust and well-documented REST API that allows programmatic access to its rich academic graph. The API is organized into several key services:

• Academic Graph API: This is the core service for retrieving detailed data about papers, authors, citations, and venues.
• Recommendations API: Given a specific paper, this service suggests other semantically similar papers.
• Datasets API: This provides links to download bulk datasets of the Semantic Scholar Academic Graph (S2AG) for large-scale offline analysis.

The API is highly regarded for its ease of use, quality of data, and responsiveness, making it a popular choice for building third-party applications like Connected Papers and Litmaps.

4.2 CORE API

CORE also offers a powerful API designed to provide access to its vast collection of open access content. Its key features include:

• Comprehensive Data Access: The API allows developers to search, filter, and retrieve metadata and full-text URLs for millions of articles.
• Data Dumps and Datasets: Like Semantic Scholar, CORE provides its entire dataset for bulk download, enabling large-scale text and data mining research.
• Repository Tools: CORE offers tools specifically for institutions, such as the Repository Dashboard, which helps managers monitor and validate their aggregated content.

CORE's API is a critical piece of infrastructure for the open access ecosystem, powering services for plagiarism detection, research trend analysis, and institutional compliance monitoring.

5. Usage & User Experience

The user interface (UI) and overall user experience (UX) of each platform reflect their core philosophies.

Semantic Scholar offers a modern, clean interface focused on presenting complex information in a digestible way. The search results page is rich with information, prominently featuring TLDR summaries and influential citation counts. The paper detail pages are well-organized, making it easy to navigate through figures, tables, and the citation network. User research is a foundational part of their product development, ensuring the platform meets the evolving needs of scholars.

CORE, following a recent update, has a redesigned UI that is cleaner and more intuitive. The experience is centered on an efficient search and filtering process. Users can easily narrow down results by year, language, repository, and other facets. The focus is less on visual analytics and more on providing direct, uncomplicated access to full-text research papers.

6. Customer Support & Learning Resources

Semantic Scholar provides extensive documentation for its API, including tutorials, code examples, and a FAQ page. As a project from the non-profit Allen Institute for AI, direct user support is primarily community-driven, but their documentation is comprehensive enough for most users and developers to get started.

CORE offers support for its various stakeholders, including institutions and commercial partners. They provide documentation for their API and services, along with a blog that announces updates and showcases use cases. For repository managers, the CORE Repository Dashboard provides direct feedback and control over their content.

7. Real-World Use Cases

• Semantic Scholar: An ideal tool for a PhD student conducting a literature review. They can use TLDRs to quickly screen dozens of papers, identify the most influential citations in their field to understand foundational work, and use the Recommendations API to find related papers they might have missed.
• CORE: A university librarian uses the CORE Repository Dashboard to ensure their institution's open access repository is correctly indexed and compliant with funder mandates. A data scientist could download the CORE dataset to train a large language model on a massive corpus of scientific text.

8. Target Audience

While both platforms serve the broader research community, their ideal users differ slightly:

• Semantic Scholar: Best for individual researchers, graduate students, and academics who need to efficiently process large amounts of information and understand the landscape of a research field. Its AI-driven features are particularly useful for those at the beginning of a research project.
• CORE: Primarily targets open access advocates, librarians, institutional repository managers, and developers who need access to a large, aggregated corpus of full-text open access content. It is an indispensable infrastructure provider for the open science community.

9. Pricing Strategy Analysis

Both Semantic Scholar and CORE are fundamentally committed to open access and provide their core services for free.

• Semantic Scholar is completely free to use. There are no subscriptions or premium tiers for its search features, AI tools, or API access. This aligns with the Allen Institute for AI's non-profit mission to contribute to the public good.
• CORE also offers its search engine and data access for free to the public, true to its open access mission. They offer membership and partnership models for institutions and commercial entities that require more specialized services, support, or use of their data for commercial applications.

10. Performance Benchmarking

Direct performance comparisons are complex as the tools measure success differently.

• Semantic Scholar's performance can be benchmarked by the relevance and quality of its search results and AI-generated summaries. While it has a smaller database than Google Scholar, its focus is on the quality of metadata and the utility of its AI features. User feedback often praises the tool for its ability to surface highly relevant papers that keyword searches might miss.
• CORE's performance is measured by the comprehensiveness of its aggregation and the uptime of its services. Its key metric is the number of open access papers it makes available. By indexing content from thousands of providers, it aims to be the most complete collection of open access research globally.

11. Alternative Tools Overview

• Google Scholar: The largest and most well-known academic search engine. It excels in raw database size but lacks the advanced AI features of Semantic Scholar and the dedicated open-access aggregation of CORE. Its metadata can sometimes be inconsistent, and it has no public API.
• Scopus & Web of Science: Subscription-based, curated citation databases that are the gold standard for bibliometric analysis. They offer powerful analytics and high-quality metadata but are behind expensive paywalls, limiting access.
• Dimensions: A modern research platform that indexes publications, grants, patents, and clinical trials. It offers a more holistic view of the research lifecycle but many of its advanced features require a subscription.

12. Conclusion & Recommendations

Choosing between Semantic Scholar and CORE depends entirely on your research priorities. The two tools are not mutually exclusive but rather complementary, serving different but overlapping needs within the academic ecosystem.

Choose Semantic Scholar if:

• You need to quickly understand a new field of research.
• You value AI-powered tools like automated summaries and influential citation analysis to make your literature review more efficient.
• You are a developer looking to build an application on top of a high-quality academic knowledge graph.

Choose CORE if:

• Your top priority is finding and accessing full-text open access research papers.
• You are an institution or developer that needs a comprehensive, aggregated dataset of scholarly articles for text mining or large-scale analysis.
• You are a librarian or repository manager focused on maximizing the visibility and compliance of your institution's research output.

Ultimately, Semantic Scholar is an intelligent discovery research tool designed to help you understand the literature, while CORE is a vast library designed to help you access it. For the modern researcher, leveraging the strengths of both platforms will lead to the most comprehensive and efficient research workflow.

13. FAQ

Q1: Is Semantic Scholar completely free to use?
Yes, Semantic Scholar and its API are completely free to use, with no hidden fees or premium subscription tiers.

Q2: Can I access the full text of every paper on CORE?
CORE aggregates metadata for hundreds of millions of articles, but not all have a full-text version available. However, it provides access to the largest collection of full-text open access articles available globally.

Q3: Which tool is better for a comprehensive literature review?
Both are valuable. A good workflow would be to start with Semantic Scholar to identify key papers and influential authors, then use CORE to find open access versions of those papers and related works from institutional repositories.

Q4: How does the data quality of Semantic Scholar compare to CORE?
Semantic Scholar places a strong emphasis on correcting and enriching metadata through AI, resulting in high-quality, structured data ideal for analysis. CORE's data quality can vary as it aggregates from thousands of different sources, but it also employs processes to enhance and validate metadata.

Q5: Can I use the Semantic Scholar or CORE API for a commercial product?
The Semantic Scholar API is generally open for use, and they encourage the development of new tools. CORE offers specific partnership models and licenses for commercial use of its data and API. It's essential to review the terms of service for both platforms before commercial implementation.