Dmytro Dolgopolov and Elena Romanova.
Abstract: FINRA has many millions of documents and database records that staff need to search through to find information relevant to regulatory activities. Searching across the large set of documents and structured database records using relevance ranked text search does not present items together that the users know are related. Relevance ranking discriminates using TF/IDF, and related techniques, but does not bring together items that are not related by relevance.
The solution was to build a structured and navigable visual representation of the data returned by the underlying multiple query engines. Text mining and semantic web techniques were used extensively to build the enhanced metadata and create the linkages among the data objects needed in order to support the visual navigation paradigm. The resulting knowledge graph gives users the ability to see semantically related items.
Keywords: Knowledge Graph; Semantic Web; RDF store; Text Mining; Enterprise Search; Graph Analysis