Scorning describes the process whereby Infinit.e processes queries and returns documents to the Infinit.e UI and the widgets.
When a user performs a query, an objet is returned which includes the documents and their sub-objects, such as entities, associations and aggregations. As part of the document object there are three query enrichment parameters which describe relevance, significance and overall score.
Aggregate significance describes how well the user's query matches the entities in the source documents.
Query Relevance describes how well a document matches the user's query.
The overall Score is a combined normalized significance/relevance score. The top scoring documents are returned to the widgets.
Significance and Relevance
When Infinit.e scores documents it weights significance and relevance scores against each other using the "sigWeight" and "relWeight" parameters of the score object. This weighting can be adjusted using the advanced options as a ratio (default 2:1 in favour of significance) and combined, with the mean score set to 100 (Similar to the "+" stats in baseball, i.e. 120 is 20% higher than average).
Increasing the "sigWeight" field tends to return documents that are longer and don't necessarily strongly relate to the user's query; instead they will tend to return documents that discuss concepts particular to the query.
Increasing the "relWeight" field tends to return documents that are shorter and very strongly relates to the user's query.
For example, for a query on "american politics", the most significant documents would contain discussion of Obama, Palin etc; the most relevant documents would contain the words "american" and "politics" with high frequency compared to other words.
Todo
-entity vs. doc significance
-significance can be impacted by temporal and geo-spatial