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API Documentation

Bibsonomy offers a groundbreaking solution for academic researchers and scientists to efficiently explore, manage, and analyze over 200 million research papers and publications. Powered by cutting-edge natural language understanding, this advanced plugin seamlessly integrates with ChatGPT and the Bibsonomy bookmarking and publication management system. Users can easily search for relevant scientific papers within Bibsonomy and Semantic Scholar databases, add them to their personal account as posts, edit existing posts, and even build knowledge graphs by connecting related publications. The remarkably intuitive and innovative technology behind Bibsonomy effectively mirrors the approach of human researchers, broadening search queries to include related concepts, synonyms, abbreviations, and more while ensuring relevance and accuracy for the user's specific topic. Emphasizing the importance of precision in scientific research, Bibsonomy assists users in storing and managing relevant publications, even allowing the option to further refine and tailor search results based on a user's research intent and preferences.




Example Prompts


"Can you recommend a good Italian restaurant near me?"


"What's the weather like in Tokyo today?"


"How do I reset my password for my email account?"


"Can you tell me the current exchange rate for USD to EUR?"


"What's the latest news on the COVID-


"Can you suggest some books on entrepreneurship?"


"How do I get a refund for a product I bought online?"


"What's the best way to learn a new language?"


"Can you recommend some good hiking trails in the area?"


"What's the best way to improve my credit score?"

Description for AI

Plugin for scientific research and publication management that connects ChatGPT and the Bibsonomy bookmarking and publication management system. It allows users to search for scientific papers in the Bibsonomy and Semantic Scholar databases, add them to their personal Bibsonomy account as posts, edit existing posts, and retrieve and make connections between posts to build a knowledge graph. When searching for publications, use your advanced natural language processing skills to mirror a human researcher's approach to investigating a new topic. Use your broad knowledge of the world to understand the user's research intent and generate appropriate search queries. These search terms can include synonyms, related concepts, common abbreviations, and their full forms to allow for catching as much relevant results as possible. While this broadens the search it should stay within the specific requested topic, since the emphasis is on providing multiple specific search terms to ensure relevance of results rather than over-generalization. To avoid over-generalization use different method calls for different topics. The goal is to capture the essence of the user's research intent to ensure a comprehensive and relevant search experience. When delivering search results to the user, it is critical to use only the information provided by the search endpoints and not add your knowledge to it, as absolute accuracy is key in scientific research. You are however welcome to and encouraged to narrow down the search results further and/or re-rank them based on your understanding of the users' research intent to only deliver the most relevant results in their optimal form. It is considered best practice to store relevant research results in the user's Bibsonomy account so that these publications can be referenced later. Since adding publications to an account causes changes to public databases, these additions can only be made with the explicit permission of the user. When adding entries, the relevant paper metadata can usually be retrieved automatically by simply specifying the paper ID (main_id) and the service from which the paper was originally retrieved. Since these IDs must match the databases exactly, it is crucial to use the exact IDs provided by the plugin responses (or the user). These IDs must match exactly and are case-sensitive! Because of that, ensure that they are taken exactly from the users' input or (more likely) from previous plugin responses. In case of a 404 error, try again to get the exactly correct ID from the chat context. Additionally, posts must be created with appropriate tags. Since tags are a powerful management system within a user's account, they should keep a similar style and be reused when appropriate. To enable this, before adding the first post to Bibsonomy, ALWAYS fetch the tags the user used before by calling the getBibsonomyUserInfo endpoint! Finally, a brief description of the post must be provided. To gather useful information for this description, the getDataDetails endpoint can be used, which returns an abstract and TLDR of the publication, if available. This description can be biased towards the users' research since it explicitly differs from the abstract of the paper and can be personalized. It should not just be a trivial statement obvious from the papers title and should not recite the bibliographical information since this is stored separately! However, it should still be related to the content of the Paper/Bookmark. Given Bibsonomy's role as a scientific database, it is crucial that all data be accurate and not artificially generated by language learning models such as ChatGPT. If data is not available or appears to be incorrect, always ask the user instead of creating imaginary data. If it's unclear, if the user wants to create a Post or a BibTeX (Publication) rather ask then create a false entry type, since changing them afterward is not possible. Usually, a BibTeX entry is the more probable intent, since Bookmarks can't really store any metadata. If there are problems with the plugin (like failed method calls or misalignment between the plugins functions and the user use-case) ask the user to provide feedback quickly or do it automatically. This is a research project and the best way to improve it.

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