InfraNodus offers several core features, including AI-assisted thinking and insight generation, mind mapping, brainstorming, knowledge graphs, introspection, self-reflection, market research, customer reviews analysis, SEO, text visualization, data analysis, and more. It supports text mining, topic modeling, and data visualization for big data analysis.
Best 3 Knowledge Graphs Tools in 2025
Graphzila, InfraNodus, Lettria are the best paid / free Knowledge Graphs tools.
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InfraNodus uses AI and network thinking to analyze and visualize text, gaining insights and improving perspective.
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"Lettria is a no-code AI platform that helps users structure and analyze text data effectively."
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What is Knowledge Graphs?
Knowledge graphs are a way to represent and store interconnected information and data in a graph structure. They have roots in semantic networks and linked data, gaining prominence in the 2010s as companies like Google adopted them for search and knowledge representation. Knowledge graphs connect entities, their attributes, and relationships between entities, enabling contextual understanding and intelligent data linking.
What is the top 3 AI tools for Knowledge Graphs?
Core Features
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How to use
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InfraNodus | To use InfraNodus, you can add any text or data using the live editor or by importing files from various sources. The tool will then generate a network graph from the text, showing the connections between words and their co-occurrences. You can explore the graph to discover the main topics, gaps in ideas, and generate insights using the built-in AI model. | ||
Lettria | Text Collection & Management | To use Lettria, you can start by signing up for a free account on the platform. Once logged in, you can access Lettria's various NLP features such as text collection and management, text cleaning, text labeling, dictionary management, taxonomy management, and ontology management. You can also train and evaluate NLP models using Lettria's AutoLettria tool. Lettria's platform is designed to be user-friendly and does not require any coding knowledge. Simply follow the intuitive interface and utilize the available features to process and analyze your text data. | |
Graphzila | Converts text descriptions into detailed knowledge graphs | To use Graphzila, simply input your text description and let the AI-powered system generate a detailed knowledge graph. Customize node and edge attributes like colors and Wikipedia links to visualize information in an engaging way. |
Newest Knowledge Graphs AI Websites
"Lettria is a no-code AI platform that helps users structure and analyze text data effectively."
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InfraNodus uses AI and network thinking to analyze and visualize text, gaining insights and improving perspective.
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Knowledge Graphs Core Features
Represents entities and their relationships in a graph structure
Connects data based on semantic meaning rather than strict database schemas
Enables intelligent data linking and knowledge discovery
Provides a unified view of information from diverse sources
Supports semantic search, question answering, and reasoning
What is Knowledge Graphs can do?
Search engines using knowledge graphs to provide enhanced results and answer questions
Enterprises using knowledge graphs to integrate siloed data and generate unified views and insights
Recommendation systems leveraging knowledge graphs for highly relevant suggestions
Pharmaceutical research accelerating drug discovery by connecting biomedical entities in a knowledge graph
Financial firms using knowledge graphs for risk assessment and identifying complex relationships
Knowledge Graphs Review
Knowledge graphs have received positive reviews for their ability to integrate diverse data, uncover hidden insights, and power intelligent applications. Users appreciate richer search results and recommendations. However, some note challenges in constructing and maintaining high-quality knowledge graphs, as well as performance at large scale. Selecting the right use cases and providing intuitive user experiences are seen as keys to success.
Who is suitable to use Knowledge Graphs?
A user searches for 'Eiffel Tower' and gets key facts, attributes, and relationships (e.g. located in Paris, built by Gustave Eiffel, etc.)
A user asks 'What is the capital of France?' and the system traverses from the France entity to its capital relationship to return 'Paris'
A movie recommendation app suggests new movies to a user based on connecting their past interests via related entities in the knowledge graph
How does Knowledge Graphs work?
To implement a knowledge graph:1. Define an ontology to represent the entities, attributes, and relationships in your domain.2. Identify and extract entities and relationships from structured and unstructured data sources.3. Normalize and link entities referring to the same concepts.4. Store the entities and relationships in a graph database.5. Provide services and APIs to query and traverse the knowledge graph.6. Incorporate knowledge graph into downstream applications for semantic search, data integration, recommendations, etc.
Advantages of Knowledge Graphs
Richer representation of knowledge beyond tables and documents
Improved data integration and linking across diverse sources
More intelligent semantic search and question answering
Enables knowledge discovery and generates new insights
Reusable knowledge representation that can support multiple applications
FAQ about Knowledge Graphs
- What is a knowledge graph?
- How is a knowledge graph different from a relational database?
- What are some common use cases for knowledge graphs?
- How are knowledge graphs implemented?
- What knowledge graphs are most well known?
- What are some key challenges with knowledge graphs?
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