Querying a knowledge graph manually is hard and time consuming
Browsing classic graph visualization usually means you get lost in the amount of data
ContextMinds lets you visualize just the context you are interested in.
ContextMinds shows knowledge as concepts (nodes) and relationships (links) between them. That means it can natively show RDF or other graph data.
You start by selecting a few concepts you are interested in. ContextMinds automatically finds related concepts - the context. As you add them to your visualization, they are automatically interlinked and more specific context is found.
That lets you quickly put together all that is relevant to your research problem!
Add your insights to the background knowledge right as you are exploring it.
At any time you can include your own concepts and relationships to the visualization and they get immediately available for future exploration by you or your colleagues. They are integrated into the knowledge graph on the fly!
Watch ContextMinds in Action
ContextMinds is freely available with data from Wikipedia (more exactly, DBpedia) loaded as a background knowledge graph. Let us show you what you can do with it!
Contact us and we will load your data as the background knowledge graph.
Smart graph traversal
In ContextMinds, you create views, which we call context schemas, which are basically small subgraphs extracted from the large knowledge graph. The knowledge graph in our demo is created from DBpedia.Start by typing
You start by text-searching for a concept (node), which will be the starting point of the visualization. ContextMinds then displays a list of selected most related concepts linked to it. You select the one you are interested in and add it to your context schema. The link between the first and the newly added concept is retrieved and displayed automatically.Follow the path of related concepts
The list of related concepts is updated to show concepts related to both the first and the second concept. If you continue selecting related concepts you are basically traversing a path in the knowledge graph. The continually updated list of related concepts that are in the neighborhood of those already present in the context schema lets you choose the direction of exploration. Instead of adding all related concepts (nodes) at once, which would immediately clutter the visualization, you choose only those relevant in the context of your current research.Data properties included
When using RDF data like in our DBpedia example, each concept represents an RDF object. Literals linked to it are shown in a box on the right when the concept (object) is selected.Types shown as well
Types of objects (concepts) are shown as tags in a box on the right when a concept is selected. Concepts of the same type (objects that are instances of the same class) have the same color, which further enhances readabilty of the visualization.Create and share as many views as you need
Each result of graph exploration, the context schema, is stored in a shared workspace. You can return to it and continue exploration, share it with anyone using a link, or export it to image and use however you like.You can thus create many context schemas showing the same problem from different points of views.
And don't forget you can add your own concepts, relationships to the context schema. They immediately become part of the knowledge graph!
Lightning fast proprietary database
ContextMinds uses a proprietary database based on Lucene and a key-value store. It is fine-tuned for the contextual graph traversal and can retrieve related concepts immediately even for knowledge graphs of the size of DBpedia or bigger. At the same time it offers fast fulltext search.Find what they have in common
At any time, you can select two concepts and let ContextMinds immediately show how they are related - retrieve the links between them. Watch it in action!Summary - as a context schema!
