Open Knowledge Maps
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About
Open Knowledge Maps is the world's largest AI-based search engine for scientific knowledge. It dramatically increases the visibility of research findings for science and society alike. It provides a visual interface that dramatically increases the visibility of research findings. The AI pipeline combines natural language processing methods to aggregate and display publications according to similarity in topics. The tool helps to get an overview of a research topic, separate the wheat from the chaff, identify relevant concepts, and find open content.
Platform
Features
• visual interface
• knowledge maps
• clustering of similar documents
• open access documents highlighting
• ai-based search engine
FAQs
How do you define "most relevant" when you are talking about most relevant documents?
At the moment, we are using the relevance ranking provided by - depending on your choice - either the PubMed API or the BASE API.
Why are you only using the top 100 documents to create the map?
We want to keep the number of documents to a manageable amount. 100 documents are already 10 times more content than is presented on a standard search results page.
Why are there important documents missing in my map?
At the moment, we are using the top 100 documents from the selected data source to create the map.
Why does it take up to 30 seconds to create a knowledge map?
While you wait, each query is processed live: first, it is sent to the selected data provider (either PubMed or BASE), which returns the most relevant results for it.
How do you ensure the quality of the research included in a knowledge map?
We use only trusted data providers such as BASE, PubMed and OpenAIRE. These providers carefully review the data sources to make sure that they only include academic content.
Are the maps generated based on full text analysis or on metadata analysis?
The grouping of documents is based on article metadata. Currently, we use titles, abstracts, authors, journals, and subject keywords to create a word co-occurrence matrix between articles.
How are the area titles created?
Area titles are created from subject keywords of documents that have been assigned to the same area.
Why are area titles sometimes in different languages?
In some cases, area titles can be multilingual, when there are documents in different languages in the knowledge map or when keywords are provided in more than one language.
What does the placement of the areas (bubbles) and the documents mean?
In general, the placement of areas (bubbles) can be interpreted as follows: Closeness of areas implies subject similarity. The closer two areas, the closer they are subject-wise.
What determines the size of the areas and the documents?
The calculation of the size of areas and documents differs between data integrations. In our PubMed integration, the size of a paper is determined by the citation count of the paper.
Why does the overview visualisation work better for some research topics than others?
The visualisation depends on the search results that we get for a given query. If there are for example not enough articles on the topic, or if the metadata quality is low, this will impact the visualisation.
How should I cite Open Knowledge Maps?
To cite an individual map, please use the citation provided for each map. Please click on the cite button on the left hand side of each map.
Where can I find more information on the background of Open Knowledge Maps?
Please see our Github page for a list of relevant research documents and project reports.
How can I include my repository / data source on Open Knowledge Maps?
Open Knowledge Maps uses BASE as its main data source. You can check if your data source is already indexed by BASE on this page.
How did Open Knowledge Maps come about?
Open Knowledge Maps was founded by Peter Kraker in 2015.
Pricing Plans
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