---
title: Knowledge Graphs for knowledge landscaping, discovery and inference Reviews
meta_title: 'Knowledge Graphs for knowledge landscaping, discovery and inference Reviews
  2026: Details, Pricing, & Features | G2'
meta_description: Filter reviews by the users' company size, role or industry to find
  out how Knowledge Graphs for knowledge landscaping, discovery and inference works
  for a business like yours.
date_modified: '2024-10-06'
parent_category:
  name: Analytics Tools & Software
  url: https://www.g2.com/categories/analytics-tools-software
---


# Knowledge Graphs for knowledge landscaping, discovery and inference Reviews
**Vendor:** Nifty.Works  
**Category:** [Other Analytics Software](https://www.g2.com/categories/other-analytics)
## About Knowledge Graphs for knowledge landscaping, discovery and inference
Knowledge graphs model to turn information into knowledge. We offer a knowledge graph pipeline for knowledge landscaping services. We enable big-data insights also for teams and organisation with little IT capacity: we leverage small IT resources with a stack comprising scientific python, redis, sql. By comparison, we benchmarked a data-model of 100M correlations on neo4j, orientDB ( graph databases ) based on 8GB RAM servers and on SQL on a low-level server with 2GB RAM, allowing to cutting server costs 10x. Full-text search is empowered by elasticsearch.






- [View Knowledge Graphs for knowledge landscaping, discovery and inference pricing details and edition comparison](https://www.g2.com/products/knowledge-graphs-for-knowledge-landscaping-discovery-and-inference/reviews?open_modal_url=%2Fproducts%2Fknowledge-graphs-for-knowledge-landscaping-discovery-and-inference%2Fwishlists%3Fhost_path%3D%252Fproducts%252Fknowledge-graphs-for-knowledge-landscaping-discovery-and-inference%252Freviews%26source%3Dpdp_avatar&section=pricing&secure%5Bexpires_at%5D=2026-08-10+22%3A14%3A45+-0500&secure%5Bsession_id%5D=5180af3c-c5df-46a0-9f53-06601b3649eb&secure%5Btoken%5D=d248fc433007dadb7437cccf171369103a3ee3c42f7834c121e7f0425cccf3e4&format=llm_user)


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