Documentation Index
Fetch the complete documentation index at: https://docs.snorbe.deskrex.ai/llms.txt
Use this file to discover all available pages before exploring further.
Knowledge Graph
The Knowledge Graph is a visual “map” of research results that Snorbe has collected and analyzed. Circular dots (nodes) represent units of information, while lines represent connections between them. In short, it’s like a bird’s-eye view diagram of all your research.Understanding the Graph View
When you open the Knowledge Graph, you’ll see numerous circular dots connected by lines. It might look complex at first glance, but the concept is simple.Circular Dots (Nodes)
Units of information handled in your research, such as companies, technologies, papers, patents, and people. Colors represent the type of information.
Lines (Edges)
Relationships between pieces of information. The type of connection—“related to,” “cites,” “competes with”—depends on the context.
Information Types (Node Colors)
Node colors indicate what type of information they represent.| Color | Information Type |
|---|---|
| Light Blue | Agent Run (research unit) |
| Green | Public Source (web information) |
| Orange | Private Source (uploaded files) |
| Gray | Entity (information unit) |
Related Information Groups (Communities)
Information that is strongly related to each other is grouped together as a community. For example, a community about “semiconductor materials” might include material manufacturers, related patents, and application technologies. Each group is color-coded, so you can see at a glance “how much information exists in each field.” You can also use the community filter to display only specific groups. Community boundaries are displayed as ellipses. As information grows, the boundary expands, making it easier to visually grasp the outline of each group.Search Functionality
When searching for information in the Knowledge Graph, two methods are combined.Keyword Search
Infers related terms from your input keywords and searches for entities (names, companies, technical terms, etc.) on the graph.
Vector Search
Searches by semantic similarity. Finds information that is close in meaning even if the words are different, like “apple” and “fruit.”
Basic Operations
You can perform the following operations in the graph view.Zoom
Use the mouse wheel or pinch gestures to zoom in and out. Zoom out to see the big picture, zoom in for details.
Pan
Drag the graph to move the area you want to see to the center of the screen.
Select Node
Click on a circular dot to display detailed information about it in the side panel.
View Relationships
When you select a node, other information connected to it is highlighted.
Click a Node to See Details
Click on a circular dot that interests you, and a detail panel will open on the right. Here you can see:- Name and type of information
- Summary and description
- Related information (list of directly connected entities)
- Research history (which agent run collected this information)
- References to source data (DOI for papers, URLs for companies, etc.)
Switching Between 2D and 3D
The graph can be switched between 2D and 3D display. In 3D mode, clusters of information appear in three dimensions, making it easier to grasp the overall structure of large graphs.Auto-Arranging Nodes
When the graph becomes complex, use the alignment feature to automatically rearrange nodes. Grouping them by entity type (people, organizations, locations, etc.) makes the overall layout much easier to read.Layout is Saved
After you rearrange nodes on the graph, the same layout is preserved when you open it next time. Once organized, you don’t need to redo it every session.Similar Entity Search
The “find similar entities” feature automatically discovers other entities with characteristics similar to the one you’re currently focused on. Example uses:- In sales planning, find “Municipality B with similar challenges to Municipality A”
- In IP strategy, identify “companies with technology areas similar to a competitor”
- In R&D, discover “related technology trends”
Easy Fact-Checking
Snorbe saves the original data from web pages and documents referenced during research. This makes it easy to trace back which source any statement in a report is based on.Source Transparency
Access the original web page or document directly from each node or report entry.
Reuse of Past Data
Information you’ve already researched is stored as embeddings, so you don’t need to repeat the same queries.
White Space Detection
The most powerful feature of the Knowledge Graph is its ability to discover unexplored areas (white spaces).How Unexplored Areas Appear
White spaces appear on the graph as “sparse areas with little information” or “completely blank areas.”Sparse Areas
Areas where dots are widely spaced and lines are few. Related information is fragmented, requiring deeper investigation.
Blank Areas
Areas with no information at all. Areas expected to be related to your research topic but where no data exists.
Why White Spaces Become Visible
With traditional research methods, you review information in list format, making it easy to miss “what’s missing” from the whole picture. The Knowledge Graph visualizes information density, making it easier to notice gaps.Benefits
Using the Knowledge Graph provides the following advantages:Grasp the Big Picture
See at a glance how comprehensive your collected information is. Research progress is also visualized.
Prevent Oversights
Discover “information gaps” that are hard to notice in list format, preventing important oversights.
Discover New Connections
Find unexpected relationships between pieces of information, leading to new hypotheses and ideas.
Common Use Cases
Prevent Research Gaps in Competitive Analysis
When researching competitor technology trends, use the Knowledge Graph to get an overview and discover companies or technology areas you might have missed. Example: While researching battery materials, you had identified major manufacturers, but the Knowledge Graph revealed the presence of university spin-offs.Determine Research Direction with Technology Mapping
Get an overview of a technology field and identify which areas are unexplored to determine your R&D direction. Example: In next-generation semiconductor research, you noticed that “materials development” had plenty of information but “manufacturing processes” was thin, so you decided to dig deeper there.Discover New Business Opportunities
Visualize white spaces in the market to discover areas where competitors haven’t ventured. Example: In healthcare research, you discovered little information at the intersection of “for seniors” and “wearable,” leading to new product ideas.Back to Features
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