...
Fuse’s Knowledge Intelligence engine recognises keywords and phrases that exist inside an item of content. For example, an item of content might contain the phrase "'Electric Vehicle"'. Even though this phrase contains two very distinct words, Fuse is able to recognise that they are being used jointly, as well as understands the context in which the phrase is being used. These key phrases are added as additional metadata to the original content item. This means if you search for "Documents 'Articles about Electric Vehicles"', any available documents articles containing metadata for the phrase ‘electric vehicle’ will rank higher than other documents articles containing the separate words "'electric" and' and/or "'vehicle"'.
Extracting Entities
Fuse is able to process the text within the content and extract entities. An entity is a piece of information that is present somewhere in the content body that matches predefined categories, such as a person, date, company or file type, which Fuse can transform into a tag. These tags are then used to help users find this content quickly in searches. These tags also allow Fuse to perform query snapping, in which Fuse automatically applies filters and facets to search results based on the entered search query.
For example, a Word document might contain the following sentence: "'John Smith's company, ACME Corp, successfully designed and produced their first electric car in 2020"'. Fuse might scan this sentence within the Word document and add the following tags to make it easier to find:
...
Example 1
If you search for "'Health and safety event at the Excel Centre with John Smith"', Fuse might intuitively apply the following facets to search results:
...
Example 3
If you search for "'LinkedIn courses on web design"', Fuse might intuitively apply the following facets to search results:
...