In order to get the best out of Lennuf, it's good to understand how it works.

Traditional (keyword) search mainly works like a funnel where you search various terms to reach the one source you are looking for.

You type in various terms until you find what you are looking for.

With Lennuf, the user provides the search engine with a phrase on which they are trying to gather more information or conduct research.

With Lennuf, you type in one term, which leads to various options.

The lists of information that Lennuf serves up are called datasets. Each dataset has a header (or heading), a body (containing information), and an optional footer. Some datasets will return nested information; this means that each header may return various sub-headers, and each sub-header will have its own information.

  • The header contains the heading of the dataset.
  • The body contains the information contained in the dataset.
  • The sub-headings contain information contained in the various parts of the body.
  • The footer returns information about the dataset (if available).

Standard vs. Deep search

A normal search only searches for and returns headers containing that search term. A deep search returns the contents of the datasets for that search term.

How to effectively search Lennuf

We classify search terms into three categories:

  1. Broad search, e.g., "BMW"
  2. Narrow search, e.g., "BMW Models"
  3. Exact search, e.g., "BMW 120i" or "BMW E87"

A broad term in the case above should return various datasets about BMW, cars, subsidiaries, company info, etc. for users to click through and find more information.

A narrow search for "BMW models" will return a dataset containing sub-headers pertaining to all BMW models. Most times, this will be a function of the platform (see semantic categories below).

An exact search for "BMW 120i" should return information about the 120i, including the car's specs, safety rating, workshop manual, etc. (If you first did a broad search, it would have taken two clicks to get to this information; if you did a narrow search, it would have taken one click.)

Semantic Categories

Lennuf will have a feature called "semantic categories," which will guide people to find out more about a particular subject (it is also helpful if people are not sure or do not know exactly what they are looking for).

How datasets are displayed

Dataset Structure

A dataset can have various levels called Headers (Six in the example below). Each header represents a level and how deep you are. Header 1 represents the highest level, while Header 6 represents the lowest level of a header. Each header will have contents (text, images, PDF, video, and audio). Header 1 will be the main header and will contain other headers, which can each have their own contents. A header 1 below that will show up below and not inside the first header 1 and can have its own contents.

Header 1 Content

Header 2 Content

Header 3 Content

Header 4 Content

Header 5 Content

Header 6 Content

A new header on the same level (header 1) as the first will show up below and not inside.

The following dataset

Header 1a: Top

Header 2a: Subheader of "Header 1a"

Header 1b: Same level as "Header 1: Top"

Header 2b: Subheader of "Header 1b"

Will look like this:

Header 1a Content

Header 2a Content

Header 1b Content

Header 2b Content