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by the Open Knowledge Maps team

Version: 2.0 beta (Update 2017)


Our motivation is that a better way to explore and discover scientific knowledge can benefit each and every one. Getting an overview of a research field and being able to identify a set of relevant findings pertaining to one's information need are prerequisites for research, evidence-based practice and self-directed learning alike. Yet, the tools for exploring and discovering scientific content are seriously lacking. With traditional, list-based search engines, users have to examine articles and their relationships by hand, which is a tedious and time-consuming process. Researchers are struggling with this task, but at least they have a community of peers that supports them and a level of a priori knowledge. People outside academia are usually on their own, and therefore often lost. This is true for policy makers looking to optimize decision-making by using evidence from relevant research, as well as science journalists trying to get an overview of a research area and patients who would like to learn about the newest findings on their illness.

Open Knowledge Maps is an attempt to solve these challenges by providing an open exploration and discovery system that leverages the emerging digital open science ecosystem. We propose to use knowledge maps, a powerful tool for exploration and discovery. Knowledge maps provide an instant overview of a field by showing the main areas of the field at a glance, and papers related to each area (see the example map below). This makes it possible to easily identify useful, pertinent information.


Our goal is to revolutionize discovery of scientific knowledge with Open Knowledge Maps, a visual interface that dramatically increases the visibility of research findings for science and society alike.

We want to provide a large-scale, web-based system of open, interactive and interlinked knowledge maps for every research topic, every field and every discipline. This system will enable users to not only get an overview of a field and identify relevant concepts, but also to discover trends, recognize important researchers, and to understand connections between fields. Furthermore, we will highlight open content, including non-publication resources such as data, video, and images. Users will be able to conduct the whole discovery process in a single browser tab.

In addition, we want to turn discovery into an open and collaborative process. Most people, including researchers, policy makers, students, journalists, librarians and citizens are currently tackling discovery on their own – and therefore repeat the same process over and over again. By sharing the results of our discoveries, we can save valuable time and build on top of each other’s’ knowledge. We want to create a space for collective knowledge mapping where different individuals and communities come together; for example, researchers and medical librarians can collaboratively map the newest research on a certain disease and openly share result of their efforts for the benefit of evidence-based practice and patients affected by this disease.

We believe that it is a critical time that we are creating this platform in. There are several closed solutions for providing visual overviews that are being developed right now. If we do not provide an open alternative in time, we risk being stuck with proprietary solutions and wasted public money for decades.

Existing Work

We are not starting this project from scratch. We have been developing the system as a group of volunteers and have released the results on our website Currently, users can create a knowledge map for a topic of their choice based on either PubMed or the Directory of Open Access Journals. Our software retrieves the 100 most relevant results for a topic and creates a knowledge map based on textual similarity between the records. The map is intended to give users a head start in their literature research.

Overview of 100 DOAJ articles for text and data mining

Open Knowledge Maps has become an international collaboration with team members, advisors and partners from all around the world. For more information on our interdisciplinary team, visit our team page.

Over the past year, we have created a lot of excitement and enthusiasm in the community. We were featured on the front pages of Reddit and HackerNews. Our user base has quickly grown: in November 2016 alone, we recorded over 44,000 visits to the site, and more than 25,000 maps have been created on the site to date. We are actively engaging with our users: we have introduced Open Knowledge Maps at numerous events including MozFest, OpenCon and OpenCon Berlin, and NetzPat Vienna. In total, over 230 people attended our workshops and sessions. The positive feedback was overwhelming: we’ve received hundreds of tweets with enthusiastic responses from users and many users have provided feedback via e-mail and by opening Github issues.

Broad Overview

Over the next two years, we want to further develop Open Knowledge Maps into a collaborative, all-encompassing open discovery system. This includes further broadening our coverage of content sources to provide overviews for all areas of research. We are looking to connect to the BASE search engine, which covers more than 100 million publications. We will also include non-publication resources from archives such as Zenodo to provide an overview of datasets, which are important for research and evidence-based practice alike.

We are going to extend the map visualizations to include additional links between papers, for example citations and facts extracted from open content to improve topic detection and similarity analysis. We will also add integration with existing tools in the open digital ecosystem, including the Open Science Framework, Zotero, and ORCiD, so that Open Knowledge Maps will fit seamlessly into researchers' current workflows. We will also provide an API of our own for other services to use. A strong focus will be on developing collaborative features. On the front end, we will enable an edit mode that allows users to manually add content to the map, modify or add metadata such as tags, and create new areas. The editing history will be preserved in a Wikipedia-like model to allow collaborative building of knowledge maps. For an overview of the collaborative editing functionality, please see this video.

We also want to grow as a community and as an organization. We have already established an interdisciplinary advisory board, and in the coming year we also want to establish a community of enthusiasts to guide the development of Open Knowledge Maps in a human-centered design process. Another concrete action is to bootstrap mapping parties (similar to those in the Open Street Maps project), where people get together to jointly map an underrepresented research field such as a neglected disease.

The Role of Openness

As mentioned before, Open Knowledge Maps is a public good, which we build to support science and society. Openness is therefore at the very core of our idea. We are openly sharing data, source code, and content that is being created. The code is being made available on Github under LGPL v3. The visualizations are released under CC BY. The underlying knowledge structures will be made available in various open formats under CC0, so that they can be easily reused.

Openness will also play an important role in all social activities, which will be organized in the spirit of open knowledge events. Mapping parties, for example, will be free of charge and there will be no restriction for attendees, other than restrictions that pertain to a specific venue (i.e. the number of people that can attend will be determined by the venue’s capacity).

Work Plan

Work is divided into six work packages. A summary of the major work components is illustrated below, along with each team member's area of primary contribution:

Milestones for each work package are shown in the table below:

Work Package Milestones
WP1: Management and coordination M1.1: Yearly report 1 (Q4/17)
M1.2: Yearly report 2 (Q4/18)
WP2: Outreach and dissemination M2.1: Start of community of enthusiasts (Q2/17)
M2.2: Mapping party concept (Q2/18)
WP3: Frontend development M3.1: Map sharing features (Q4/17)
M3.2: Mobile version (Q1/2018)
M3.3: Map editing features (Q4/18)
WP4: Backend development M4.1: Data fusion model (Q2/17)
M4.2: Incorporation of ontologies (Q3/17)
M4.3: User registration (Q1/18)
M4.4: Integration with OSF (Q3/18)
WP5: Content and data sources M5.1: BASE Integration (Q2/17)
M5.2: Backend API (Q4/17)
M5.3 Integration of non-publication res. (Q2/18)
WP6: Expert Review M6.1: User testing report (Q3 /17)


Success Metrics

Metric After year 1 After year 2
Number of covered research artefacts 80 million 150 million
Number of maps generated 100K 300K
Number of registered users N/A 80K
Number of monthly visitors 40K 120K
Number of people participating in the Enthusiasts program 500 2,000
Number of Open Knowledge Maps events worldwide 30 90

High-Level Risks & Mitigation

  • If we build it, will they come? We already see considerable interest in the project (see above). We will also follow best practices, human-centered design and expanding our
  • community of advisors and enthusiasts to guide us in the development.
  • Technical challenges related to building a large-scale system: It will be crucial to address scalability from the start and build it into the core architecture. We will use a distributed agile process and adopt strategies of successful open source projects.
  • Not enough open data available or low data quality: Currently, we assume that more open data and content will be published. We have shown in the past that we are able to create meaningful knowledge maps from very few data points and we are planning to use context information to improve records of low quality.