SEMANTIC MINING OF DOCUMENTS IN A RELATIONAL DATABASE

Automatically mining entities, relationships, and semantics from unstructured documents and storing these in relational tables, greatly simplifies and unifies the work flows and user experiences of database products at the Enterprise. This paper describes three linear scale, incremental, and fully automatic semantic mining algorithms that are at the foundation of the new Semantic Platform being released in the next version of SQL Server. The target workload is large (10 - 100 million) enterprise document corpuses. At these scales, anything short of linear scale and incremental is costly to deploy. These three algorithms give rise to three weighted physical indexes: Tag Index (top keywords in each document); Document Similarity Index (top closely related documents given any document); and Phrase Similarity Index (top semantically related phrases, given any phrase), which are then query-able through the SQL interface. The need for specifically creating these three indexes was motivated by observing typical stages of document research, and gap analysis, given current tools and technology at the Enterprise. We describe the mining algorithms and architecture, and outline some compelling user experiences that are enabled by these indexes.