Abstract: A system and methods rank results of database queries. An automated approach for ranking database query results is disclosed that leverages data and workload statistics and associations. Ranking functions are based upon the principles of probabilistic models from Information Retrieval that are adapted for structured data. The ranking functions are encoded into an intermediate knowledge representation layer. The system is generic, as the ranking functions can be further customized for different applications. Benefits of the disclosed system and methods include the use of adapted probabilistic information retrieval (PIR) techniques that leverage relational/structured data, such as columns, to provide natural groupings of data values. This permits the inference and use of pair-wise associations between data values across columns, which are usually not possible with text data.

Abstract: This application describes methods for searching digital information such as digital documents (e.g., web pages) and computer databases, and specific search techniques such as authority ranking and information retrieval (IR) relevance ranking in keyword searches. In some implementations, the technique includes analyzing digital information viewed as a labeled graph, including nodes and edges, based on a flow of authority among the nodes along the edges, the flow of authority being derived at least in part from different authority transfer rates assigned to the edges based on edge type schema information. In some implementations, the system includes an object rank module configured to generate multiple initial rankings corresponding to multiple query keywords, each of the multiple initial rankings indicating authority of nodes in a graph with respect to each respective query keyword individually; and a query module configured to combine the multiple initial rankings in response to a query.

Abstract: A system and methods rank results of database queries. An automated approach for ranking database query results is disclosed that leverages data and workload statistics and associations. Ranking functions are based upon the principles of probabilistic models from Information Retrieval that are adapted for structured data. The ranking functions are encoded into an intermediate knowledge representation layer. The system is generic, as the ranking functions can be further customized for different applications. Benefits of the disclosed system and methods include the use of adapted probabilistic information retrieval (PIR) techniques that leverage relational/structured data, such as columns, to provide natural groupings of data values. This permits the inference and use of pair-wise associations between data values across columns, which are usually not possible with text data.