Knowledge Graphs for knowledge landscaping, discovery and inference Discussions

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. When users leave Knowledge Graphs for knowledge landscaping, discovery and inference reviews, G2 also collects common questions about the day-to-day use of Knowledge Graphs for knowledge landscaping, discovery and inference. These questions are then answered by our community of 850k professionals. Submit your question below and join in on the G2 Discussion.