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Keenious is an AI-based tool that allows you to search for similar or related articles based on either an article or written text. The AI analyses the downloaded article, a link to the article, a selected passage from the article or a written text and recommends similar or related content. Keenious runs in either a web browser or Word application and is licensed to the University of Helsinki until the end of year 2024.
Keenious can currently only analyse texts in English, but one can use texts in almost 100 different languages (cross-language search). Keenious uses a translation program called Microsoft Azure, where the text is translated to English and then analyzed. Although some of the terms or nuances may be translated a bit off, the context of the text is well captured which matters most to the analysis and consequently search results. Note, however, that there is no length limit for English texts, while for non-English texts the maximum length is 20 000 characters. You may do daily 10 cross-language searches.
Keenious application works for all disciplines.
Its data comes from OpenAlex, which contains a very large amount (~hundreds of millions) of metadata (but not full texts) of both open and non-open access journal articles.Book articles will be included later on. OpenAlex includes material predominantly in English.
Keenious Webinar given by the developers in April 2023. Length 1.10h, subtitled.
Keenious are excited to be hosting this introductory webinar for the Keenious Research Recommendation tool.
They will be exploring some of the challenges people face when looking for relevant research, how Keenious can help, and how to use the tool. You'll also be able to ask questions.
This is an introductory session, so no experience with Keenious is required!
Register via the form, and you'll receive a calendar invite. This webinar is for researchers at the University of Helsinki.
https://forms.gle/UFnptV64UmTjLpAT8
Keenious is a very versatile tool for mapping research. Unlike most common information retrieval methods, it is not based on inventing keywords, but on analysing the text you already know or the text you write yourself and based on this analysis by AI. Thus, Keenious is useful at least for the following purposes:
Questions about Keenious? Need technical support? Would your faculty or department like a presentation on Keenious?
Please, contact: kirjasto@helsinki.fi
Has Keenious been useful to you? Is there anything you would like to improve? Do you have any thoughts on the different ways of using it?
Tell us! All feedback is helpful. The address is kirjasto@helsinki.fi