Answers to the most frequently asked questions about Open Knowledge Maps.
Q1 How do you define "most relevant" when you are talking about most relevant papers?
At the moment, we are using the relevance ranking provided by - depending on your choice - either the PubMed API or the BASE API. Both of them mainly use text similarity between your query and the article metadata to determine the relevance. PubMed has a detailed description of their relevance ranking. BASE uses Lucene (via Solr), which describe their ranking as well on this page.
Q2 Why are you only using the top 100 papers to create the map?
We want to keep the number of papers to a manageable amount. 100 papers are already 10 times more content than is presented on a standard search results page. Nevertheless, we are investigating on how to enable the exploration of larger amounts of content, while keeping cognitive load to a minimum. At the moment, you can drill deeper into a topic by providing a more specific search query. One way to do this is to expand your query with the topic of a sub-area.
Q3 Are the maps generated based on full text analysis or on metadata analysis?
The grouping of papers is based on article metadata. Currently, we use titles, abstracts, authors, journals, and subject keywords to create a word co-occurrence matrix between articles. On top of this matrix, we perform clustering and ordination algorithms. The labels for the sub-areas (bubbles) are generated from the subject keywords of the articles in this area. In cases where they are missing from the metadata, we approximate them from abstract and title. More information can be found in this article.
Q4 Why does the overview visualization work better for some research topics than others?
The visualization 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 visualization. We have a number of routines in place to improve your chances of getting a useful map, but we do not always succeed. If you come across a map that needs improvement, we'd love to hear from you at firstname.lastname@example.org.
Q5 How did Open Knowledge Maps come about?
Open Knowledge Maps was founded by Peter Kraker in 2015. Peter had worked on knowledge domain visualizations in his PhD and developed the first version of the open source visualization framework Headstart out of frustration with the existing discovery tools for scientific knowledge. In January 2016, Peter posted a Call for Collaborators on his blog, which brought a first team of volunteers together. Since 2016 Open Knowledge Maps is a registered non-profit organization.
Q6 Can Open Knowledge Maps connect to my data source?
Absolutely! Open Knowledge Maps is based on the open source software Head Start, which is able to create knowledge maps from a wide variety of data, including text, metadata and references. If you have a collection that you would like to visualize with Open Knowledge Maps, check out our docs to get started, or contact us on email@example.com for a joint project.
Q7 How is Open Knowledge Maps funded?
We are a non-profit organization run by a group of dedicated volunteers. Currently, we are looking for funding for our roadmap to realize the full potential of the idea. If you are interested in funding this effort, please contact us on firstname.lastname@example.org.
Q8 How can I contribute?
You can contribute in a number of ways:
we love to hear your feedback and ideas as this helps us to improve
Open Knowledge Maps. If you like the project, please spread the word as far as you can :)
Q9 Are you available for collaborations and joint projects?
No doubt! Just drop us a line on email@example.com
You couldn't find an answer to your question? Get in touch and we will get back to you as soon as we can.