Knowledge graphs can capture information about the world in an intuitive way that is often easier to understand, navigate and use than other data models. They provide data discovery, search and analysis using a common, SQL-like syntax. A knowledge graph combines data models and semantics, connecting information from disparate sources, and letting users make beter decisions by finding relevant information faster.
Links between graph entities may be defined as semantic types, allowing users to query data by their relationships instead of matching values. Relationships can be direct or based on Semagraph lookups, Text Search, Classification models, or Inference Query offering a variety of powerful techniques for graph building. Knowledge Graphs address the following data integration challenges:
1. Combine Data from Disparate Silos or Sources
2. Bring Together Structured and Unstructured Data
3. Make Better Decisions by Finding Things Faster
Graph links may be direct or inferred based on Semagraph Indexes. Explains Latent or direct Semantic Attribution..