Sensemaking for Broad Topics via Automated Extraction and Recursive Search

The availability of vast amounts of diverse information related to a broad topic makes it difficult and time-consuming for users to find and digest the right information regarding various low-level topics within the broader space. Current approaches to addressing these challenges include providing curated topical pages, relevant query refinement suggestions, list of subtopics, etc. However, these approaches do not scale and offer inadequate support for sensemaking. This disclosure describes automated techniques that extract information from online information sources by using a query related to a high-level topic to recursively formulate additional queries for subtopics to construct a hierarchical set of topics related to the broad query. The results can be utilized to provide a user interface using the hierarchical topic levels which can make it faster and easier for users to understand and navigate information regarding a high-level topic

Automated Extraction of Pivot Topics for Sideways Expansion of Search Scope

Users benefit from mechanisms that can help them refine their queries to facilitate searching for information connected to their underlying intent. Apart from refinements to narrow the scope of a query, users can benefit from suggestions that can help them pivot their information seeking by expanding their search sideways to related topics. This disclosure describes computational techniques for automated determination of suitable topics and/or queries for helping users expand the scope of their information search by pivoting to topics related to their query. The techniques involve selecting a meta-query, performing query expansion, identifying, aggregating, and deduplicating related entities. The identified entities are clustered and ranked to enable selection of particular entities that can be shown to users as pivot topics