Semantic Scholar

What is Semantic Scholar?

Semantic Scholar is a free academic search and paper discovery platform developed by the Allen Institute for Artificial Intelligence. It utilizes natural language processing, machine learning, and academic networks to assist researchers in finding papers, understanding abstracts, tracking citations, managing reading lists, and receiving personalized recommendations on an ongoing basis.

It is not a tool for generating in-depth research reports; rather, it is an academic infrastructure based on the metadata of actual papers and their citation relationships. The official product page indicates that it covers more than 214 million papers from various disciplines, 2.49 billion citation relationships, and 79 million authors.

Interdisciplinary paper search

Users can search by paper title, keywords, author, and topic, and they can narrow down the results using filters such as journal or conference, author, publication type, and date range. The search results display the author, year, citation count, abstract, open access status, and a link to the PDF version.

A wide search range does not mean that every record is complete. Papers may lack abstracts, full texts, citation information, or standard author details;

A formal review still needs to be supplemented with Web of Science, Scopus, PubMed, and discipline-specific databases.

TLDR single-sentence AI summary

TLDR is a one-sentence summary that Semantic Scholar generates automatically for papers; it uses very brief wording to outline the research objectives and main findings, enabling users to quickly determine whether a paper is relevant among the search results.

Currently, TLDR covers nearly 60 million papers in computer science, biology, and medicine, and can also be accessed through APIs. It is generated by NLP models, and may omit details such as methods, samples, and limitations; therefore it cannot replace abstracts or the full text of articles.

High-impact citations

Highly Influential Citations uses machine learning to analyze the relationships between citation contexts, the number of times a paper is cited, and the papers themselves, thereby identifying those citations that have a significant impact on current research. When faced with hundreds of references, it helps users to focus their attention on the most important works.

“High impact” is a classification of models; it does not indicate the quality of a paper, the accuracy of its conclusions, or the necessity of citing it. Citation practices vary significantly across different disciplines, and researchers still need to assess these matters on their own.

Reference types and reference context

On some paper pages, the citation references are categorized into types such as background, methods, and results, with relevant excerpts from those cited papers shown. This allows users to more easily understand why one paper cites another, rather than having to rely solely on numbers.

Automatic classification may misinterpret the author’s tone and negative relationships. When making a decision as to whether there is support, expansion, or questioning in a text, it is necessary to read the entire context of the original paper.

Paper details page

The paper pages display the title, authors, place of publication, year, abstract, TLDR, topic, references, cited papers, charts, and related research. Users can further filter subsequent documents by year, PDF status, and citation impact.

Semantic Scholar aggregates various academic sources, and occasionally duplicate entries, incorrect years, or merged authors may appear. The DOI, journal volume and issue numbers, as well as page numbers, should be verified against the publisher’s website.

Semantic Reader

Semantic Reader is an enhanced paper reader that can be used with most arXiv papers. It understands the structure of papers and combines the main text with Semantic Scholar’s academic network, providing an outline, citation cards, a TLDR summary, and contextual information while reading.

Readers are designed primarily to improve the experience of online reading, but they do not guarantee that all PDF files will be accessible. Documents with complex formatting, scanned versions, supplementary materials, and full texts subject to copyright restrictions still require access to the original source.

Highlight for Goal, Method, and Result

For most English-language arXiv papers in the field of computer science, Semantic Reader can automatically highlight the research objectives, methods, and results, enabling users to skim through them quickly. Users can adjust the number of highlights and their transparency.

Highlighting is an AI-based classification method that cannot replace the design of research studies or the interpretation of results. The limitations of a paper, the baseline settings, and statistical details often need to be examined in detail.

Context definition

The reader can provide definitions for terms and abbreviations marked with annotations, based on the context of the current document, thereby reducing the time spent leaving the page to look up meanings. This is particularly useful when reading across different disciplines.

The automatic definitions may differ from the author’s specific usage; for key terms, it is necessary to refer to the definitions given in the paper, the cited literature, or authoritative textbooks.

Quote card

When reading the main text, users can directly view the basic information and a TLDR of the cited paper to decide whether to open it. If the cited paper is already saved in the personal library, the system can also highlight the relevance based on the user’s research activities.

Highlighting and notes

Semantic Reader supports highlighting and note-taking through Integration with Hypothesis. Users need a corresponding account in order to save and manage annotations, and they can also collaborate using Hypothesis’s sharing features.

The content of annotations is governed by the data and accessibility settings of third-party services. It is necessary to check the privacy settings before recording unpublished research ideas.

Library literature database

After logging in, it is possible to save papers in an online library, create custom folders, and export citations in bulk. Public folders can be shared with collaborators, who can then copy them to their own literature databases.

Library is suitable for discovering and lightly organizing documents, but it cannot fully replace reference managers such as Zotero and EndNote in terms of PDF management, citation tools, and duplicate removal functions.

Research Feeds

Once the Research Feed is enabled in the Library folder, the system will recommend new studies based on the papers that have been saved. Officials recommend adding at least 5 relevant papers first and providing feedback on any recommendations that are not relevant, in order to improve the quality of these suggestions.

It is recommended to keep updating starting from the day after, or to receive updates via email. The more focused the folder’s topic is, the more relevant the results will generally be.

Integrating multiple fields reduces the accuracy of recommendations.

Papers, authors, and recommendation alerts

Users can create new citation alerts for papers, keep an eye on the authors’ new papers and citations, and also receive Research Feed emails. The Research Dashboard displays all these research updates in one place.

Merging authors with the same name and their respective details can lead to distracting noise. It is necessary to verify the institution, field of expertise, and the author’s homepage before paying attention to them.

Author’s homepage and claim page

Semantic Scholar creates an aggregated homepage for authors, listing their papers, citations, and collaborators. Researchers can claim their own author page, manage the affiliation of their papers, and receive notifications when their work is cited.

Personal evaluations should not be based solely on the total number of citations. The field of study, career stage, order of authors, and data coverage all influence these figures.

Quote Export

The paper pages and search results support citation styles such as BibTeX, MLA, APA, and Chicago; the Library also allows for batch exports. It is suitable for integrating found papers into writing or literature management processes.

The automatic format may lack page numbers, volume/issue numbers, or special characters; it must be checked against the original paper’s and the journal’s formatting requirements before submission.

Open access and PDF

The platform displays the status of whether the content is legally available in open access, as well as the links to the PDF versions; some of this information comes from sources such as Unpaywall. Semantic Scholar does not bypass publishers’ paywalls, nor does it guarantee that the full text of each article is available.

If downloading is not possible, you can use the subscription provided by your institution, interlibrary loan, the author’s archive, or legally available versions.

Free policy

The Semantic Scholar website, paper search functions, the library, feeds, and core reading features are all available free of charge; there is no paid membership option intended for ordinary researchers. The service is operated by the non-profit research organization AI2.

Being free does not mean there are no restrictions on speed, access, or data usage. For large-scale data collection, it is necessary to use the official APIs and datasets, rather than automating visits to web pages.

Academic Graph API

The Academic Graph API provides access to papers, authors, citations, references, journals and conferences, SPECTER2 vectors, as well as open-access information. Developers can perform searches using keywords, or by Corpus ID or DOI, and retrieve multiple entries for specific fields at once.

Only the fields that are actually needed should be requested; pagination, caching, and backoff mechanisms should be used to avoid unnecessary, large-scale repeated calls.

Recommendations API

The Recommendations API can suggest similar studies based on a set of positive papers and optional negative papers, making it suitable for developing tools for literature discovery, reading lists, and topic tracking.

Recommendation algorithms are not a proof of the completeness of a systematic review. Applications should indicate the basis for recommendations, allow users to provide feedback, and provide access to professional databases.

Datasets API

The Datasets API enables batch uploading, downloading, and retrieval of incremental changes from the Semantic Scholar academic graph; it is suitable for institutions and development teams that wish to store data locally and run custom queries.

The volume of data is very large; before downloading it, it is necessary to assess the costs related to storage, updating, licensing, and removing duplicates. Different parts of the bulk data may have their own licensing rules and restrictions on the use of the entire text.

Semantic Scholar usage guide

Complete a basic task.

1. Clarify the issue, time frame, location, source priority, and output format;
2. Upload materials for which you have usage rights on Semantic Scholar or enter a search query;
3. First, a framework is established through interdisciplinary paper searches, and then evidence is added using AI-generated TLDR summaries.
4. It is necessary to distinguish between factual information from the source, the author’s opinions, and AI-generated conclusions.
5. Check each item for dates, numbers, the original location, and any conflicting evidence;
6. The conclusions are manually revised, the verification time is recorded, and then they are published;

Create reusable professional workflows

1. Break down complex topics into four categories of questions: background, data, comparison, and conclusions;
2. A fixed set of research steps is established, comprising interdisciplinary paper searches, AI-generated TLDR summaries in single sentences, and high-impact citations.
3. Give priority to using the official website, research papers, regulatory documents, and raw data;
4. A second person is assigned to review conclusions that are considered high-risk;
5. Save queries, evidence, versions, and unresolved issues;
6. Re-run after the data changes and update the conclusions;

API keys and rate limits

• Most endpoints can be accessed without authentication, but unauthenticated users share the public bandwidth, resulting in lower stability;
• After applying for a free API key, the official tutorials indicate that a default rate of one request per second is available, and it is possible to request a higher limit based on the project’s requirements.
• When a 429 error occurs, the speed should be reduced and exponential backoff should be applied;
• APIKey must not be placed in public front-ends or code repositories;

S2ORC Open Research Corpus

S2ORC is the Semantic Scholar Open Research Corpus, which provides paper metadata, citation relationships, and partial full texts for scientific text mining and NLP research. It is recommended to use the Datasets API to obtain regularly updated versions of this corpus.

The S2ORC data are subject to licenses such as ODC-By 1.0; when using or redistributing them, it is necessary to include a citation and comply with the terms applicable to each portion of the data. The Open Research Corpus does not mean that the full texts of all papers can be reused freely.

PDF and LaTeX parsing tools

AI2 has made s2orc-doc2json available on GitHub; this tool allows components such as Grobid to convert PDF, JATS, and LaTeX files into S2ORC JSON format. It is suitable for researchers and developers who want to create processes for processing scientific documents.

Parsing tools require separate installation of dependencies, and errors may occur in formulas, tables, and reference links; therefore they cannot be considered equivalent to hosted search products.

Semantic Reader Open Research Platform

Research related to Semantic Reader has made libraries and prototypes such as PaperMage and PaperCraft available for developers to explore intelligent paper-reading interfaces. These components demonstrate some of the technical approaches, but they are not the complete source code of the Semantic Scholar website.

Open-source status

Semantic Scholar’s overall search service, online academic graph, and production infrastructure are not fully open-source applications; however, AI2 has made available S2ORC, the document parser, the Semantic Reader research components, as well as model and paper data resources.

Therefore, the catalog should be labeled as \"The platform is not fully open-source; only some of the data, models, and tools are available under an open-source license.\" It is necessary to check each license before using the respective repositories.

Differences from Google Scholar

Google Scholar has a wide coverage and is good at identifying academic versions of web pages; Semantic Scholar places more emphasis on structured academic maps, TLDR summaries, impact citations, personalized feeds, and open APIs.

The results of the two methods differ, so it is best to use them together for important searches.

Differences from ResearchRabbit and Litmaps

Semantic Scholar is a large-scale search and data infrastructure; ResearchRabbit and Litmaps focus more on conducting visual network analyses and creating project-based overviews starting from key papers.

The latter two also often use the Semantic Scholar API or its data as their underlying source.

Is it suitable for a systematic review?

• SemanticScholar is useful for supplementing findings from papers, conducting citation tracking, and performing keyword searches, but it should not be the only database used for systematic reviews.
• A comprehensive review requires documenting the reproducible search queries, coverage across multiple databases, deduplication, filtering, and quality assessment;

Which users is it suitable for?

• Students and researchers looking for papers, authors, and citation relationships;
• Users who need to quickly filter through a large number of search results;
• Researchers who wish to continue receiving personalized recommendations for papers;
• Those who read arXiv papers and need context citation cards;
• A team responsible for developing tools for academic searching, recommendation, and literature analysis;
• Institutions that use Open Academic Graph for NLP research.

Product advantages

• It covers over 214 million interdisciplinary papers;
• TLDR and highly influential citations improve screening efficiency;
• Library, Feeds, and Alerts constitute a continuous discovery process;
• Semantic Reader improves the reading of online papers;
• The website and the main APIs are available for free use;
• There is an abundance of open data, parsing tools, and research components.

Restrictions and Precautions

• Errors can occur in paper metadata, author disambiguation, citation types, and AI-generated abstracts, and the coverage of the entire text may also be incomplete.
• In short, influence tags and recommendations cannot replace professional judgment.
• The default API rate is limited, and batch data requires significant storage space as well as compliance management;
• Systematic reviews must be combined with other databases;

Frequently Asked Questions

Is Semantic Scholar free?

Free. There is no subscription fee for regular users for website searches, the Library, Research Feeds, reminders, and the main APIs.

How many papers are there on Semantic Scholar?

The official product page currently shows over 214 million papers, 2.49 billion citations, and 79 million authors.

Is TLDR reliable?

TLDR is suitable for quick screening, but since it is generated by AI, it may miss certain constraints; for thorough research, it is necessary to read both the abstract and the full text.

Does Semantic Scholar provide an API?

It offers three types of APIs: Academic Graph, Recommendations, and Datasets. The default rate for free API keys is approximately 1 request per second.

Can I download all the data?

Batch data and incremental updates for academic graphs can be obtained through the Datasets API, but it is necessary to comply with the licensing, attribution, and full-text usage rules.

Is Semantic Scholar open source?

The complete online platform is not entirely open source, but resources such as S2ORC, the document parser, and the Semantic Reader research components are available publicly.