What is Schema-Aware Semantic Search?

Schema-Aware Semantic Search with FactEngine

There are multiple ways that Schema-Aware Semantic Search has been delivered; this article focuses on how it is achieved using FactEngine.

Let us has a look at what Schema-Aware Semantic Search is.

The term, “Semantic Search”, generally means:

Returning results for a natural language query over a knowledge store where the results are returned based on the meaning, gist or context of that query;

The content of the knowledge store may vary depending on the implementation of semantic search.

For example, the knowledge store could be:

1. Unstructured Data: E.g. documents (The contents of PDF files, word processor or text based documents), or such things as email, Tweet or social media exchanges; or
2. Structured Data: E.g. A knowledge graph, product database, or any other database; or
3. Random facts that are in some way structured. E.g. As in attempts to amalgamate structured data and unstructured data in burgeoning and experimental ‘neural databases’;

NB FactEngine operates over structured data and where unstructured data and/or random facts may have their meta-data stored in a run-of-the-mill database as structured data….we call that ‘structured unstructured data’.