Semantic Scholar - Detailed Review
Summarizer Tools
Semantic Scholar - Product Overview
Introduction to Semantic Scholar
Semantic Scholar is a free, AI-powered search and discovery tool developed by the non-profit Allen Institute for Artificial Intelligence. It is designed to help researchers, students, publishers, and librarians efficiently discover and comprehend scientific literature across all domains.
Primary Function
The primary function of Semantic Scholar is to assist users in quickly assessing the significance and relevance of scientific papers. It achieves this by leveraging advanced machine learning algorithms and natural language processing techniques to extract meaningful insights from the literature. This enables users to make informed decisions and stay updated on the latest research in their fields.
Target Audience
Semantic Scholar is targeted at researchers, students, publishers, and librarians who need to manage and analyze large volumes of scientific literature. The tool is particularly useful for those looking to stay current with the latest developments in their field, identify influential papers, and discover related research.
Key Features
Quick Paper Assessment
Semantic Scholar provides concise summaries, key figures, and citations to help users quickly grasp the main ideas of a paper and determine its relevance to their research.
Tracking Trends
The tool identifies emerging trends and impactful papers, ensuring users stay informed about the most recent developments in their area of interest.
Efficient Reading
It helps users manage their reading lists by offering personalized recommendations and efficient search capabilities. This streamlines the research workflow and allows users to prioritize the papers they need to read.
Extracting Key Information
Semantic Scholar extracts paper abstracts, figures, and citations, providing a summary of a paper’s main points and the research that influenced it. It also identifies key entities such as authors, journals, and conferences.
Linking Papers Through Citations
The tool visualizes the network of citations, showing how research is connected and which papers are most influential in a field.
Author Profiles
Semantic Scholar offers detailed author profiles, including publication history, metrics, co-authors, and affiliated institutions. This helps users assess an author’s expertise and contributions to the academic community.
Alerts and Notifications
Users can create email alerts for new papers, citations, and topics in their field of study, helping them stay up-to-date with the latest research.
Field of Study Classification
Papers are classified into specific fields of study using a machine learning model based on the paper’s title and abstract, making it easier to find relevant literature.
By leveraging these features, Semantic Scholar significantly enhances the efficiency and effectiveness of scientific research, making it an invaluable tool for scholars and researchers.
Semantic Scholar - User Interface and Experience
User Interface Overview
The user interface of Semantic Scholar is designed to be intuitive and user-friendly, particularly for those in the academic and research communities.
Search and Discovery
Semantic Scholar features advanced search capabilities that allow users to find papers quickly and efficiently. Users can search for specific keywords, authors, journals, or conferences, and the tool provides relevant results with concise summaries, key figures, and citations. This makes it easy to assess the relevance and significance of a paper at a glance.
Summarization Tools
One of the standout features is the summarization capability. Semantic Scholar generates concise summaries of paper abstracts, key figures, and citations. The TLDR (Too Long; Didn’t Read) feature provides single-sentence summaries of papers, helping users make quick decisions about which papers to read further. These summaries are generated using advanced NLP techniques and are available right on the search results page.
Personalized Experience
The “My Library” feature allows users to save papers to a personal library, organize them, and add annotations such as notes, highlights, and comments. This personalized repository helps users manage their research materials effectively and keeps their important papers easily accessible.
Augmented Reading
Semantic Scholar’s Semantic Reader is an augmented PDF reader that enhances the reading experience. It provides definitions of key terms, contextual citations, and links to related papers, making it easier for users to engage with complex topics and extract greater value from their reading.
Citations and Context
The tool links papers together through citations, visualizing a network of research connections. This helps users see which papers are most influential in a field and how different pieces of research are related, providing a broader context to any given paper.
Ease of Use
The interface is user-friendly, with intuitive web interfaces that allow users to easily submit papers and access generated summaries. The search and summarization features are designed to save time and increase efficiency, making it easier for users to review more content in less time.
Feedback and Support
Users can provide feedback on features like TLDRs, which helps in continuously improving the tool. This feedback loop ensures that the user experience is constantly refined to meet the needs of researchers and academics.
Conclusion
Overall, the user interface of Semantic Scholar is streamlined to facilitate efficient research workflows. It combines powerful AI-driven tools with a straightforward and accessible design, making it an invaluable resource for anyone looking to streamline their academic reading and research process.
Semantic Scholar - Key Features and Functionality
Semantic Scholar
Semantic Scholar is an AI-powered research tool developed by the Allen Institute for AI, and it offers several key features that make it an invaluable resource for researchers, students, and academics.
Semantic Search
Semantic Scholar uses AI to go beyond traditional keyword-based searches. Instead of relying solely on keywords, the tool leverages machine learning and large language models to comprehend the context and meaning behind your search queries. This approach provides more relevant and high-quality search results, helping you find the most pertinent information quickly and efficiently.
Citation Graphs
This feature allows you to visualize how research papers are connected through citations. By tracing these connections, you can identify influential works, see the development of ideas over time, and gain insights into the broader research landscape. This visual representation helps in identifying key papers and authors that have significantly impacted the field.
Paper Recommendations
Semantic Scholar offers personalized paper recommendations based on your search history and preferences. This ensures you don’t miss critical research in your area of interest. The tool suggests relevant papers, helping you stay updated with the latest developments and making your literature review process more efficient.
Quick Paper Assessment
Semantic Scholar provides concise summaries of paper abstracts, key figures, and citations. This feature allows you to quickly assess the significance and relevance of a paper to your research. You can get a glance at the main ideas, key visualizations, and the research that influenced the paper, saving you time and effort in deciding whether to read the full paper.
Tracking Trends
The tool uses advanced machine learning algorithms to identify emerging trends and impactful papers in your field. This keeps you up-to-date with the latest research, ensuring you are informed about the most recent developments and can make more informed decisions in your research endeavors.
Efficient Reading and Organization
Semantic Scholar helps you manage your reading lists effectively by providing personalized recommendations and efficient search capabilities. You can organize and prioritize the papers you need to read, streamlining your research workflow. Additionally, you can create a library to store your papers, set alerts for new papers or citations, and access PDFs directly from the platform.
Research Question Decomposition and Evidence Aggregation
When used in conjunction with Elicit, another AI tool, Semantic Scholar can break down complex research questions into manageable sub-questions. Elicit also helps aggregate evidence from multiple sources, providing a comprehensive view of the research landscape. This feature is particularly useful for literature reviews, as it ensures you have a structured and efficient way to gather and synthesize research findings.
Interactive Reports
Elicit allows you to create dynamic reports that update as new research is published. This keeps your literature review current and ensures you are always aware of the latest developments in your field.
Identifying Key Entities and Indexing Papers
Semantic Scholar can identify key entities in scientific papers, such as authors, journals, and conferences. It also indexes papers by research area, making it easier to find papers related to your interests. This feature helps in quickly assessing the credibility of a paper and staying updated with the latest research in your area of expertise.
Alerts and Notifications
You can set up alerts to receive notifications when new papers are published in your area of interest or when a specific paper receives a new citation. This feature ensures you stay informed about new research and can quickly incorporate new findings into your work.
Overall, Semantic Scholar integrates AI to provide a comprehensive and efficient way to search, organize, and analyze scientific literature, making it an indispensable tool for researchers and academics.
Semantic Scholar - Performance and Accuracy
Performance and Accuracy of Semantic Scholar’s Summarizer
Semantic Scholar, a product of the Allen Institute for Artificial Intelligence (AI2), has made significant strides in summarizing scientific literature, particularly in the field of computer science.Key Performance Metrics
- The summarizer uses an abstractive model based on transformer architecture, which has been pre-trained on a generic corpus of text and fine-tuned on a specific dataset called SciTldr. This dataset contains approximately 5,400 pairs of scientific papers and their corresponding one-sentence summaries.
- The model achieves a high level of compression, summarizing papers that average 5,000 words into 21-word summaries, which is a compression ratio of about 238 times. This outperforms other abstractive methods, which typically achieve a compression ratio of around 36.5 times.
Accuracy and Engagement
- Human reviewers have judged the summaries generated by Semantic Scholar to be more informative and accurate than those produced by previous methods. This indicates a high level of factual accuracy and engagement, as the summaries effectively capture the essence of the papers.
Limitations and Areas for Improvement
- One notable limitation is that the current model is primarily trained on computer science papers. There are plans to expand its capabilities to handle papers from other fields, which will enhance its utility for a broader range of researchers.
- Sometimes, the one-sentence summaries can overlap too much with the paper titles, reducing their utility. The team is working to update the model’s training process to penalize such overlap and avoid repetition.
- Free summarizer tools, including Semantic Scholar, often lack advanced features such as customization options, multiple summary view formats, detailed analytics, and integration support. These limitations can restrict the tool’s flexibility and depth of analysis.
User Experience
- While the summaries are generally coherent and logical, users may still need to review them carefully to ensure all key points are covered. It is recommended to ask follow-up questions to refine the summaries if necessary.
Conclusion
Semantic Scholar’s summarizer is a powerful tool for researchers, offering highly compressed and accurate summaries of scientific papers. However, it has some limitations, particularly in terms of its current scope and the need for further customization and integration features. As the tool continues to evolve, it is likely to become even more valuable for those seeking to quickly grasp the key points of complex research papers. Semantic Scholar - Pricing and Plans
Semantic Scholar Overview
Semantic Scholar is notably straightforward and generous in its pricing structure, making it highly accessible to a wide range of users.
Key Points
- Free to Use: Semantic Scholar is completely free, with no hidden fees or subscriptions. This makes it accessible to students, independent researchers, and anyone interested in exploring scientific literature.
Features Available
- Despite being free, Semantic Scholar offers a comprehensive set of features, including:
- Access to over 200 million papers across various disciplines.
- AI-powered search capabilities that use natural language and machine learning techniques to find relevant results.
- The ability to create email alerts for new papers, authors, and topics.
- Personalized research feeds based on user ratings.
- The option to save papers in a personal library.
- Automatic creation and management of author pages.
- Extraction of paper abstracts, figures, and citations for quick assessment.
No Tiers or Plans
- There are no different tiers or plans available on Semantic Scholar. The service is uniformly free for all users, with all features accessible without any cost or subscription requirements.
Conclusion
In summary, Semantic Scholar provides a comprehensive and free service, eliminating any need to consider different pricing plans or tiers. This makes it an invaluable resource for anyone looking to explore and engage with scientific literature.
Semantic Scholar - Integration and Compatibility
Semantic Scholar Overview
Semantic Scholar, an AI-backed search engine for academic publications, offers several integration and compatibility features that make it a versatile tool for researchers and academics.
Integration with Zotero
One of the key integrations of Semantic Scholar is with Zotero, a popular reference management software. Users can utilize the Zotero extension on their browser to save paper information, URLs, PDFs, and TLDRs (if available) directly to their Zotero library. This integration works across various pages on Semantic Scholar, including the Semantic Reader, individual paper pages, and search results pages, allowing users to bulk save multiple papers efficiently.
Search and Filtering Capabilities
Semantic Scholar’s search engine is integrated with several graph structures, such as the Microsoft Academic Knowledge Graph, Springer Nature’s SciGraph, and the Semantic Scholar Corpus. This allows for semantic searches that identify relationships between words and topics, enabling users to find relevant papers without using Boolean operators. Results can be filtered by subject area, date range, author, publication name, and the availability of a PDF link.
Compatibility with Multiple Devices and Platforms
Semantic Scholar is accessible via web browsers, making it compatible with a wide range of devices, including desktops, laptops, tablets, and smartphones. Users can search, save, and summarize papers without the need for specific software installations, as the platform operates through an intuitive web interface.
Summarization and AI-Powered Features
The platform includes AI-powered summarization tools that can condense complex academic papers into shorter summaries. These summaries are generated using natural language processing (NLP) and machine learning algorithms, which help users quickly grasp the main points of a paper. This feature is particularly useful for streamlining the academic reading and writing process.
Partnerships and Content Integration
Semantic Scholar has over 50 direct partnerships with publishers, data providers, and aggregators, providing content from more than 500 academic journals, university presses, and scholarly societies. This extensive content integration ensures that users have access to a broad range of academic publications, enhancing the discoverability and accessibility of scholarly content.
Conclusion
In summary, Semantic Scholar integrates well with reference management tools like Zotero, offers advanced search and filtering capabilities, and is accessible across various devices and platforms. Its AI-driven summarization features and extensive content partnerships make it a valuable resource for researchers and academics.
Semantic Scholar - Customer Support and Resources
Customer Support
While the sources do not provide detailed information on dedicated customer support channels such as phone numbers, email addresses, or live chat options, it is reasonable to assume that support might be available through general contact methods provided by the Allen Institute for AI, which develops Semantic Scholar. However, this information is not explicitly mentioned in the sources.Additional Resources
Semantic Scholar offers several resources that can significantly aid users:Quick Paper Assessment and Summaries
Semantic Scholar provides concise summaries of paper abstracts, key figures, and citations. This feature helps users quickly assess the relevance and significance of a paper before reading it in full, saving time and effort.Semantic Reader
The Semantic Reader is an augmented PDF reader that enriches the reading experience by providing definitions of key terms, contextual citations, and links to related papers. This tool makes research papers more accessible and helps users engage more effectively with the content.Research Feeds
Semantic Scholar’s Research Feeds is an adaptive research recommender that uses AI to learn what papers users care about and recommends the latest relevant research. This feature helps scholars stay up-to-date with the latest developments in their field.Advanced Search Capabilities
The tool offers advanced search features that allow users to find the most relevant and up-to-date information on their research topic. Users can search for specific keywords, authors, or papers related to certain conferences, making the search process easy and effective.Key Entity Identification
Semantic Scholar can identify key entities in scientific papers, including authors, journals, conferences, and more. This helps users quickly assess the credibility of a paper and gain a comprehensive overview of its content. While these resources are highly beneficial for researchers and students, for any specific support queries or technical issues, users might need to refer to the general contact information of the Allen Institute for AI or look for community forums and support groups related to Semantic Scholar. Semantic Scholar - Pros and Cons
Advantages of Semantic Scholar
Efficient Search and Discovery
Semantic Scholar is an AI-powered research tool that uses natural language processing and machine learning algorithms to provide highly relevant search results. Unlike traditional keyword-based searches, it understands the meaning behind your queries, offering more precise and high-quality results.
Quick Paper Assessment
The tool allows users to quickly assess the significance of a paper by providing concise summaries, key figures, and citations. This feature helps researchers determine the relevance and value of a paper for their own work without having to read the entire document.
Tracking Trends and Related Papers
Semantic Scholar keeps users updated on the latest research in their field by identifying emerging trends and impactful papers. It also links papers together through citations, showing how research is connected and highlighting influential works.
Efficient Reading and Organization
The tool helps users manage their reading lists effectively by providing personalized recommendations and efficient search capabilities. Users can save papers to a personal library, annotate them, and organize them for easy access and reference.
Author Profiles and Metrics
Semantic Scholar offers detailed author profiles, including publication history, metrics, co-authors, and affiliated institutions. It also provides paper-level metrics like citations and influential scores, as well as author-level metrics like the h-index, helping users evaluate the credibility and impact of papers and authors.
Semantic Reader and APIs
The Semantic Reader is an augmented PDF reader that enhances the reading experience by providing definitions, citations, and links to related papers. Additionally, Semantic Scholar offers APIs for search, academic graphs, and more, which are useful for developing research applications.
Disadvantages of Semantic Scholar
Limited Language Support
Semantic Scholar primarily supports English-language scientific papers, which can limit its usefulness for researchers working with non-English sources. This limitation can be significant for those who rely on non-English literature for their research.
Potential for Inaccuracies
Despite its advanced AI algorithms, there is a risk of inaccuracies or misunderstandings in the summaries generated by Semantic Scholar. This can lead to misinterpretations or oversights, especially in nuanced or context-dependent research.
Lack of Contextual Understanding
The AI algorithms may struggle to fully grasp the context of scientific papers, potentially leading to misinterpretations or oversights. Contextual understanding is crucial in scientific research, and this limitation can affect the accuracy of the summaries provided.
Potential for Bias
Semantic Scholar’s AI algorithms may be subject to biases or limitations in the data they analyze, which can result in biased or incomplete summaries of scientific papers. This is a critical consideration for researchers who need accurate and unbiased information.
No Direct Integrations
Semantic Scholar lacks direct integrations with popular reference and citation managers, which can make it challenging for users to seamlessly incorporate their research findings into their existing workflows.
By considering these advantages and disadvantages, researchers can make informed decisions about how to effectively use Semantic Scholar in their academic and scholarly work.
Semantic Scholar - Comparison with Competitors
Unique Features of Semantic Scholar
- AI-Powered Summaries: Semantic Scholar uses an abstractive model based on transformer architecture to generate one-sentence summaries (TLDRs) for scientific papers, particularly in the computer science domain. This model is trained on a dataset called SciTldr, which includes over 5,400 pairs of papers and summaries, and an additional 20,000 pairs of papers and their titles.
- Extreme Summarization: The model achieves a high level of compression, reducing papers averaging 5,000 words to 21-word summaries, a 238-fold reduction. This is significantly better than other abstractive methods.
- Research Feeds and Recommendations: Semantic Scholar provides research feeds and recommendations to help users stay updated with the latest research in their fields.
Potential Alternatives and Comparisons
Felo AI Academic Search
- Multilingual Support: Unlike Semantic Scholar, which primarily operates in English, Felo AI Academic Search offers robust multilingual capabilities, making it more accessible to non-English speaking researchers.
- Interactive Features: Felo AI provides AI-generated answers with citations, mind maps, simplified explanations, and automated presentation generation, which are not available in Semantic Scholar.
- Database Size and Update Frequency: Felo AI has access to over 245 million academic publications with continuous updates, whereas Semantic Scholar hosts around 217 million publications with potentially slower update cycles.
ClickUp
- Project Management Focus: ClickUp’s AI summarization tool is integrated into its project management platform, ClickUp Brain, and is optimized for summarizing project documents, meeting notes, and reports. This is different from Semantic Scholar’s focus on academic papers.
- Task and Deadline Extraction: ClickUp’s tool identifies assigned tasks, deadlines, and project milestones, which is not a feature of Semantic Scholar.
Get Digest
- Extractive Summarization: Get Digest focuses on extracting key sentences from documents, preserving the original wording, which contrasts with Semantic Scholar’s abstractive summarization approach.
- Customizable Summary Length: Users can specify the number of sentences they want included in the summary, offering more flexibility in terms of summary length.
QuillBot and Jasper
- General Text Summarization: Both QuillBot and Jasper are designed for summarizing a wide range of texts, including articles, documents, and general content, rather than focusing specifically on academic papers like Semantic Scholar.
- Multilingual Support and Formality Level: Jasper supports summarization in over 25 languages and allows users to select the formality level of the summarized text, features that are not available in Semantic Scholar.
- High Character Limits: Both QuillBot and Jasper can handle lengthy texts, with QuillBot summarizing up to 25,000 characters and Jasper up to 12,000 characters.
Engagement and Factual Accuracy
Semantic Scholar stands out for its high engagement and factual accuracy in summarizing academic papers, particularly due to its rigorous training on high-quality datasets and human-reviewed summaries. However, for users needing multilingual support, interactive features, or summarization of different types of documents, alternatives like Felo AI Academic Search, ClickUp, Get Digest, QuillBot, or Jasper might be more suitable. Each tool has its unique strengths and use cases, making the choice dependent on the specific needs of the user.
Semantic Scholar - Frequently Asked Questions
Frequently Asked Questions about Semantic Scholar
What is Semantic Scholar and how does it help researchers?
Semantic Scholar is a free, AI-powered search and discovery tool that helps researchers discover and understand scientific literature most relevant to their work. It uses machine learning techniques to extract meaning and identify connections within papers, providing users with quick insights and helping them locate and understand the right research efficiently.
How does Semantic Scholar help users assess the significance of a paper?
Semantic Scholar enables users to quickly assess the significance of a paper by providing concise summaries, key figures, and citations. This includes extracting paper abstracts, figures, and citations, which helps users grasp the main ideas and determine the paper’s relevance to their research before reading it in full.
What are TLDRs on Semantic Scholar, and how do they help researchers?
TLDRs (Too Long; Didn’t Read) are automatically generated single-sentence summaries of the main objective and results of a scientific paper. These summaries, available for papers in computer science, biology, and medicine, help researchers make quick informed decisions about which papers to read, saving time and effort.
How can users track trends and stay updated on the latest research using Semantic Scholar?
Semantic Scholar helps users stay up-to-date on the latest research by identifying emerging trends and impactful papers through advanced machine learning algorithms. It also allows users to set up email alerts for new papers, citations, and topics in their field of study, ensuring they are informed about the most recent developments.
What features does Semantic Scholar offer for managing and organizing research papers?
Semantic Scholar provides several features to help users manage their reading lists. Users can save papers to a personal library, annotate them, and organize them for easy access. The tool also offers personalized recommendations and efficient search capabilities to streamline the research workflow.
How does Semantic Scholar link papers together through citations?
Semantic Scholar links papers together through citations, visualizing the network of citations to show how research is connected. This feature helps researchers understand which papers are most influential in a field and how different pieces of research are related.
Can users create alerts to stay updated on specific authors, papers, or topics?
Yes, users can create email alerts on Semantic Scholar to stay updated on new papers, citations, or topics. This includes author alerts for new citations or papers, paper alerts for new citations, topic alerts for new papers mentioning a specific topic, and research feed alerts based on user ratings.
How does Semantic Scholar classify papers by Field of Study?
Semantic Scholar uses a machine learning classification model to assign papers to up to three Fields of Study based on the paper’s title and abstract. This classification is currently limited to English-language papers and is most reliable when both the title and abstract are available.
What is the Semantic Reader feature on Semantic Scholar?
The Semantic Reader is an augmented PDF reader that makes papers more accessible by providing definitions of key terms, contextual citations, and links to related papers. This feature enhances the reading experience, making it easier for users to navigate complex topics and extract greater value from their reading.
Can users access and manage their author profiles on Semantic Scholar?
Yes, users can claim and manage their author pages on Semantic Scholar. Author profiles provide information about an author’s publication history, metrics, co-authors, and affiliated institutions, helping users evaluate an author’s credibility and authority.
Is it necessary to create an account to use Semantic Scholar?
No, it is not necessary to create an account to access papers on Semantic Scholar. However, creating an account allows users to create email alerts, generate research feeds, save papers, and claim an author page for better management of their research materials.
Semantic Scholar - Conclusion and Recommendation
Final Assessment of Semantic Scholar
Semantic Scholar is a highly valuable AI-powered research tool that leverages advanced natural language processing and machine learning algorithms to facilitate the discovery and comprehension of scientific literature. Here’s a comprehensive overview of its benefits and who would most benefit from using it.
Key Features and Benefits
- Quick Paper Assessment: Semantic Scholar provides concise summaries, key figures, and citations, allowing users to quickly assess the relevance and significance of a paper for their research.
- Tracking Trends: The tool keeps users updated on the latest research in their field by identifying emerging trends and impactful papers.
- Efficient Reading: It helps users manage their reading lists effectively through personalized recommendations and efficient search capabilities. This feature streamlines the research workflow and ensures users stay informed about recent developments in their area of interest.
- Comprehensive Overviews: Semantic Scholar offers insights into influential papers and trends, enabling users to enhance their knowledge and make informed decisions in their research endeavors.
- Author Profiles: The tool provides detailed author profiles, including publication history, metrics, co-authors, and affiliated institutions. This helps users evaluate an author’s credibility and authority.
- My Library and Annotations: Users can save papers to a personal library, annotate them, and organize their research materials effectively. The annotation feature allows users to add notes, highlights, and comments to papers.
- Advanced Search and Alerts: Creating an account enables users to set up email alerts for new papers, generate research feeds for new paper recommendations, and save papers for later reference.
Who Would Benefit Most
Semantic Scholar is particularly beneficial for:
- Researchers: It helps them stay updated on the latest research, identify key papers, and understand the broader context of their field through citation networks and related paper recommendations.
- Students: The tool is especially useful for upper-level undergraduates and early graduate students who are learning to analyze scholarly articles. Features like the Semantic Reader, which provides definitions, citations, and links to related papers, make it easier for students to engage with complex research.
- Publishers and Librarians: By providing comprehensive overviews and efficient search capabilities, Semantic Scholar aids in the organization and dissemination of scientific literature.
Limitations
While Semantic Scholar is highly effective, it has some limitations:
- Limited Language Support: The tool primarily focuses on scientific literature and may not be as comprehensive for topics in the humanities or social sciences.
Overall Recommendation
Semantic Scholar is an indispensable tool for anyone involved in scientific research. Its ability to provide quick assessments of papers, track trends, and offer comprehensive overviews makes it a time-saving and informative resource. The advanced search features, author profiles, and annotation capabilities further enhance its utility.
For researchers, students, publishers, and librarians in the scientific disciplines, Semantic Scholar is highly recommended. It streamlines the research process, enhances knowledge acquisition, and facilitates better decision-making. However, for those focusing on humanities or social sciences, it may not be as comprehensive, and other tools might be more suitable. Overall, Semantic Scholar is a valuable addition to any researcher’s toolkit.