Last year, I built the foundations of the project “Dashboard for Language/National Knowledge Graphs” and the dashboard is up and running live for the latest-core during the time. This year, as a student, I want to extend this project wherein the user centric approach would be used to improve users retention rate in DBpedia. At present, the dashboard shows the general statistics of the latest-core, wherein there are no user specific activities. However, in the databus, users can maintain the metadata of their own collections which gives them more flexibility to deal with different versions of the data cores. Similarly, having the platform with user authentication and customized visualization ability will help users to analyze their data without leaving the DBpedia.
As there are hundreds of users who use DBpedia’s data/knowledge graphs to achieve their goals, it is important to maintain the data quality and user specific operations. The goal here is to provide more personalized experience when using DBpedia’s data. This can be achieved by reducing the time users spend in exporting the data from DBpedia and importing in their respective data analysis tools.
1) User Retention: Users that rely on DBpedia’s data, are fetching from the core by querying and lastly analyzing it on other platforms. With that being said, there is not much interaction between users and DBpedia’s knowledge graphs as processing is done completely outside of DBpedia which leaves DBpedia, just a data-hub. However, implementing this feature will help the community to have a platform where users can manage as well as analyze their data without shifting away from DBpedia. With this, users can leverage all the support from the community in terms of data processing.
2) Better Control Over Knowledge Graph Statistics: As users will query and filter the data to visualize it in their ways, it gives more flexibility for custom operations. Additionally, users will be able save their files (output of queries) in their buckets (folders) on the dashboard portal itself.
1) Refine the current version of the project and make necessary changes to adopt the new feature and increase stability which includes real-time sync with public latest-core so that dashboard has real-time updates as soon as latest-core is updated.
2) Measuring the feasibility with mentors to determine required resources to implement the feature.
Karan Kharecha (GSoC Student)
data science, data engineering, data visualization, knowledge graph, user centric