Conceptual analytics

Conceptual analytics helps you organize and assess the semantic content of large, diverse and unknown sets of documents. Unlike structured analytics, which relies on the specific structure of the content, conceptual analytics focuses on related concepts within documents, even if they don't share the same key terms and phrases. Using these features, you can cut down on review time by more quickly assessing your document set to facilitate workflow.

After using structured data analytics to group your documents, you can run Analytics operations to identify conceptual relationships present within them. For instance, you can identify which topics contain certain issues of interest, which contain similar concepts, and which contain various permutations of a given term.

To run conceptual Analytics operations, you must first create an Analytics index. See Analytics indexes for more information.

Conceptual analytics helps reveal the facts of a case by doing the following:

  • Giving users an overview of the document collection through clustering
  • Helping users find similar documents with a right-click
  • Allowing users to build example sets of key issues
  • Running advanced keyword analysis

Note: You can configure the physical location of the Analytics indexes and structured analytics sets. For instructions on how to modify this location, see Moving Analytics indexes and structured analytics sets.

See these related help topics:

Structured analytics versus conceptual analytics

Structured analytics and conceptual analytics are different from each other in several ways. Depending on your needs, one or the other may work better for you.

Structured analytics Conceptual analytics
Groups documents that have similar content, but may or may not have similar concepts Groups documents that have similar concepts, even if the words are different
Takes word order into consideration Does not consider word order
Takes into account the placement of words and looks to see if new changes or words were added to a document Uses Latent Semantic Indexing (LSI), which focuses more on concepts than on specific wording changes
Uses a structured analytics set, not an index Uses an Analytics index