Concept searching

You can use Concept searching to find information without a precisely phrased query by applying a block of text against the database to find documents of similar conceptual content. This can help prioritize or find important documents.

Concept searching is very different from keyword or metadata search. A concept search performed in Relativity Analytics reveals conceptual matches between the query and the document. It identifies matches within your data set quickly and efficiently, so you can focus on the concepts that you deem important. The following table illustrates the differences between standard searching and concept searching.

Standard Method Analytics Method

Finds the presence (or absence) of a query (term or block of text)

Derives the potential meaning of a query

Simply looks for matches of query and indexed docs

Attempts to understand semantic meaning and context of terms

Incorporates Boolean logic

Incorporates mathematics

With standard Keyword Search, people frequently experience “term mismatch,” where they use different terms to describe the same thing.

Using concept searching, you can submit a query of any size and receive documents that contain the concept the query expresses. The match isn't based on any specific term in the query or the document. The query and document may share terms, or they may not, but they share conceptual meaning.

Every term in an Analytics index has a position vector in the concept space. Every searchable document also has a vector in the concept space. These vectors, which are close together, share a correlation or conceptual relationship. Increased distance indicates a decrease in correlation or shared conceptuality. Two items that are close together share conceptuality, regardless of any specific shared terms.

During concept searching, you create text explaining a single concept (called the concept query) and submit it to the index for temporary mapping into the concept space. Analytics uses the same mapping logic to position the query into the concept space as it did the searchable documents.

Once the position of the query is established, Analytics locates documents that are close to it and returns those as conceptual matches. The document that is closest to the query is returned with the highest conceptual score. This indicates distance from the query, not percentage of relevancy—a higher score means the document is closer to the query, thus it is more conceptually related.

In addition, you can use concept searches in conjunction with keyword searches. Since a keyword can have multiple meanings, you can use a concept search to limit keyword search results by returning only documents that contain the keyword used in similar conceptual contexts.

This page contains the following content:

Benefits of concept searching

The following are benefits of concept searching:

Running a concept search in the viewer

To run a concept search from the viewer, perform the following steps:

  1. Select a document from the document list and open it in the Relativity viewer.
  2. Highlight a section of text and right-click the text to display a set of menu options.
  3. Hover over Analytics, then click Concept Search.

The conceptual hits display in the Search Results Related Items pane. The results are sorted by rank.

Note: The minimum coherence rank used for the right-click concept search is .60. This value is not configurable.

back to top

Running a concept search from the Documents tab

To run a concept search from the Documents tab, perform the following steps:

  1. Click the Search With drop-down on the Documents tab, and select an Analytics index from the list.
  2. Perform one or more of the following tasks:
  3. Select any of these optional settings to control how your results are displayed:
  4. Click Search. To stop a long running search, click Cancel Request.

back to top

Send us feedback down arrow