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Best 3 knowledge graphs Tools - 2025

Lettria ,InfraNodus ,Graphzila , are the best paid / free knowledge graphs tools.

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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.

knowledge graphs Insights

  • India Traffic 13.4K
  • France Traffic 6.9K
  • Mexico Traffic 6.5K
  • United States Traffic 15.4K
  • United Kingdom Traffic 9.1K
  • Canada Traffic 670
  • Vietnam Traffic 1.1K
  • Average Traffic 33.4K
3 Tools

knowledge graphs already has over 3 AI tools.

100.3K Total Monthly Visitors

knowledge graphs already boasts over 100.3K user visits per month.

0 tools traffic more than 1M

knowledge graphs already exists at least 0 AI tools with more than one million monthly user visits.

What is the top 10 AI tools for knowledge graphs?

Core Features Price How to use
InfraNodus

InfraNodus uses AI and network thinking to analyze and visualize text, gaining insights and improving perspective.

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.

Graphzila

Transform text into visual 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.

Lettria

"Lettria is a no-code AI platform that helps users structure and analyze text data effectively."

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.

Newest knowledge graphs AI Websites

  • Lettria

    "Lettria is a no-code AI platform that helps users structure and analyze text data effectively."

    AI Product Description Generator No-Code&Low-Code AI Advertising Assistant AI Chatbot AI Tools Directory
  • InfraNodus

    InfraNodus uses AI and network thinking to analyze and visualize text, gaining insights and improving perspective.

    AI Data Mining AI Productivity Tools
  • Graphzila

    Transform text into visual knowledge graphs.

    AI Knowledge Graph

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

  • 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?

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    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

    {/if]
  • 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?
A knowledge graph represents and connects entities, their attributes, and relationships in a graph structure to enable knowledge representation and reasoning.
How is a knowledge graph different from a relational database?
Knowledge graphs focus on relationships and semantic linking between entities while relational databases use rigid table-based schemas. Knowledge graphs are more flexible for integrating diverse data.
What are some common use cases for knowledge graphs?
Common uses include semantic search, question answering, recommendation systems, data integration, drug discovery, and financial analysis.
How are knowledge graphs implemented?
Knowledge graphs are typically stored in graph databases and accessed via APIs and query languages like SPARQL. Ontologies define the schema. NLP and entity linking map data to the graph.
What knowledge graphs are most well known?
Google's Knowledge Graph powers their search results. Facebook, Microsoft, Amazon, and IBM also use knowledge graphs. Open knowledge graphs include DBpedia and Wikidata.
What are some key challenges with knowledge graphs?
Challenges include ontology design, entity disambiguation, data quality assurance, graph scale and performance, and incorporating knowledge graphs into downstream applications effectively.

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