Getting Started with InfoRapid KnowledgeBase Viewer — A Beginner’s OverviewInfoRapid KnowledgeBase Viewer is a lightweight, Windows-based application designed for exploring, visualizing, and querying knowledge graphs exported in common formats such as GraphML, GML, RDF, and CSV. If you’re new to graph data or to InfoRapid specifically, this guide will walk you through the essentials: installation, supported formats, loading and navigating graphs, basic querying and filtering, visualization techniques, simple analysis, and tips for effective use.
What is InfoRapid KnowledgeBase Viewer?
InfoRapid KnowledgeBase Viewer is a desktop tool for viewing and interacting with knowledge bases and graph-structured data. It focuses on easy exploration — letting you inspect nodes and edges, visualize relationships, and perform basic searches and filters without needing advanced database or programming knowledge. It’s especially useful for small-to-medium datasets, quick inspections, and presentations.
System requirements and installation
- Operating system: Windows 10 or later (32-bit and 64-bit).
- Java: Some InfoRapid products require Java; the Viewer is distributed as a native Windows executable but may depend on Java runtime for certain features. Check the official download page for the latest dependency notes.
- Disk space: Minimal — the application itself is lightweight; graph datasets determine storage needs.
Installation steps (typical):
- Download the installer or ZIP package from the official InfoRapid website.
- Run the installer and follow prompts (or unzip and run the executable if using the portable version).
- Launch the application from the Start menu or the executable file.
Supported file formats
InfoRapid KnowledgeBase Viewer can import a range of graph and tabular formats. Common supported formats include:
- GraphML (.graphml) — widely used XML-based graph format.
- GML (.gml) — plain text graph format.
- RDF / Turtle / N-Triples (.rdf, .ttl, .nt) — for semantic web data.
- CSV (.csv) — for edge lists or node tables when mapped appropriately.
- Other common graph formats (check the current app documentation for updates).
When preparing data, ensure node IDs and edge relations are clearly defined. CSV imports often require a mapping step where you specify which columns represent source, target, and any properties.
Loading your first graph
- Open InfoRapid KnowledgeBase Viewer.
- Use File → Open (or drag-and-drop) to load a GraphML, GML, RDF, or CSV file.
- If prompted, map CSV columns to node/edge fields (source, target, labels, properties).
- Once loaded, the main graph canvas displays nodes and edges. The side panels show properties and controls.
If the graph is large, the app may provide options to limit initial rendering (e.g., show the largest connected component or a sampled subset).
Navigating the interface
The basic interface typically includes:
- Graph canvas: central area where nodes and edges are drawn.
- Toolbar: quick actions for zoom, layout, selection, and search.
- Side panels / inspector: shows properties of selected node or edge (labels, attributes).
- Layers/filters panel: enable or disable visibility of subsets.
- Legend / style settings: controls for color, shape, and size mappings.
Use mouse controls to pan (drag), zoom (scroll), and select (click/box-select). Double-clicking a node often centers and expands its immediate neighborhood.
Visualization and layouts
Visual clarity matters. InfoRapid offers multiple layout algorithms:
- Force-directed (spring) layout: good for general-purpose, organic placement.
- Circular / radial layout: emphasizes hierarchical or cyclical structures.
- Hierarchical / layered: useful for directed acyclic graphs.
- Grid / orthogonal: for structured, tabular presentations.
Styling tips:
- Map node size to a numeric attribute (degree, weight) to highlight importance.
- Use color to represent categories or types.
- Show/hide labels based on scale to avoid clutter.
- Use edge thickness or color to encode relationship strength.
Searching, filtering, and queries
Basic search:
- Text search: find nodes or edges by label or attribute values.
- Attribute filters: show only nodes with attributes meeting criteria (e.g., type = Person).
Neighborhood exploration:
- Expand neighbors of a selected node (1-hop, 2-hops) to progressively reveal context.
- Use degree-based filters to hide extremely high-degree nodes if they overwhelm the visualization.
RDF/SPARQL:
- If the data is RDF and the Viewer supports SPARQL, you can run SPARQL queries to extract subgraphs or attribute-driven selections. Consult the app’s query panel or documentation for exact syntax and capabilities.
Basic analysis features
InfoRapid typically includes lightweight analysis tools such as:
- Degree distribution: identify hubs and leaf nodes.
- Connected components: find isolated subgraphs.
- Shortest paths: compute paths between two nodes (useful for relationship tracing).
- Simple statistics: counts of nodes/edges by type, attribute summaries.
For heavier analysis (centrality measures, community detection on large graphs), export to a specialized tool (Gephi, NetworkX, Neo4j) after initial exploration.
Exporting and sharing
Common export options:
- Save the graph in GraphML/GML for later use.
- Export images (PNG, SVG) of the current view for reports or presentations.
- Export filtered subgraphs or CSV tables of nodes/edges for use in other tools.
When exporting visuals, consider increasing resolution or using SVG to keep labels crisp for print.
Practical example: Inspecting a small RDF dataset
- Load the RDF/Turtle file into the Viewer.
- Use the inspector to identify node types (e.g., Person, Organization).
- Apply a filter to display only Persons and their direct relationships.
- Run a shortest-path query between two Person nodes to see connections.
- Export the resulting subgraph as GraphML for deeper analysis in another tool.
Troubleshooting common issues
- Slow rendering with large graphs: try sampling, hiding labels, or using a layout that handles large graphs efficiently.
- Missing labels after import: ensure the label column/attribute is correctly mapped during import.
- CSV mapping confusion: re-open import settings and explicitly set source/target columns and node identifiers.
- Layouts look messy: apply a different layout and use the “fit to screen” or “center” commands.
Tips for efficient use
- Start with small subgraphs; expand neighborhoods as needed.
- Maintain consistent attribute names (label, type, id) across datasets for easier reuse.
- Use colors and sizes sparingly to avoid cognitive overload.
- Save custom layout and style presets for repeating tasks.
Alternatives and complementary tools
InfoRapid KnowledgeBase Viewer is great for quick visual exploration. For heavier workflows consider:
- Gephi — powerful open-source graph visualization and analysis.
- Neo4j Browser / Bloom — for interactive querying and graph databases.
- NetworkX / igraph (Python/R) — programmable analysis and custom metrics.
Conclusion
InfoRapid KnowledgeBase Viewer is a practical, approachable tool for beginners to explore graph-structured data, inspect RDF and GraphML files, and perform lightweight queries and visualizations. Start with small datasets, learn the layout and filter tools, and use exports to integrate with more powerful analysis tools when needed.
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