Enterprise data is locked away in silos. As a result people spend too much time looking for information – or they spend too little and make decisions based on incomplete information. The simplest remedy is to pull all the silos into a common full-text search index, but doing so realizes only part of the opportunity. The rest lies in automatically finding and representing the relationships among the objects in the index. Relationships take many forms, ranging from schematized references to allusions in text. All these relationships can be recorded in the index, thus combining structured, semi-structured, and unstructured relationships together in a normalized representation, forming bridges between data silos. The result is a graph, which makes it possible to make queries like “find all x’s related to y.” For example the graph could be used to find all the people or discussions related to a particular method. The graph is stored in a SQL-based search index that also has a rich notion of time and history. The index is exposed to the user in three ways: a search portal, an implicit query sidebar, and object-based search commands in client applications. The index improves search in several ways, allowing for richer filtering, scoring, and search results presentation. These ideas can turn siloed data into working knowledge, making the enterprise work more efficiently.