Microsoft Technology Licensing, LLC

Stuart M Bowers

Chris Demetrios Karkanias

Brian Scott Aust

Thomas Jackson

David G Campbell

Allen L Brown, Jr

David B Wecker

Brandon T Hunt

Burton Jordan Smith

Silvia C Vega

Dave Campbell

Allen Brown

Tom Jackson

Jim Karkanias

Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Expressing and executing semantic queries within a relational database

Technology is described that includes a method of feature specification via semantic queries. The method can include the operation of obtaining a data set having an identifier for each data row and a plurality of data features for each data row. A semantic query can be received that can be applied to the dataset that is usable by a machine learning tool. A entity feature map can be supplied that has entities and associated features for use by the machine learning tool. Further, a query structure can be analyzed using the entity feature map to identify input from the dataset for the machine learning tool.

Feature specification via semantic queries

A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.

Efficient probabilistic reasoning over semantic data

The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.

Model for hosting and invoking applications on virtual machines in a distributed computing environment

Described are techniques to facilitate temporal features in a semantic data store. Information about lifetimes of facts in a semantic store is maintained. Even when a fact is logically deleted, a physical record is kept available. The record of a logically deleted or invalid fact has associated lifetime information. For example, valid-from and valid-to time values. The record of a fact not yet deleted may have a valid-from time value indicating when it was created, became valid, etc. Queries against the semantic store may specify a timeslice (a point in time or a time range). The lifetime information can be used to satisfy such time-specific queries. Because records are maintained after they are logically deleted, it is also possible to accurately query a past state of the semantic store. Even if such a query is run at different times, same results may be obtained.

Temporal binding for semantic queries

Described is a technology comprising a query processing pipeline in which a SPARQL query is processed into an intermediate LINQ query, which is then processed by a LINQ provider. The LINQ provider decides which instructions correspond to flat database queries, and routes those instructions a database engine (e.g., SQL server) for querying a database. Other instructions are provided to a reasoning engine for processing, e.g., by performing a graph traversal and/or database queries. The pipeline may include a parser that parses the query into an abstract syntax tree, and an optimizer that processes the abstract syntax tree into a LINQ query, including by reordering LINQ instructions and/or associating a flag with each of the instructions that indicates whether to query the database or provide the instruction to a reasoning engine.

Query submission pipeline using LINQ

A semantic query may refer to a logical rule, where the rule is defined in terms of constituent expressions. In order to execute the semantic query efficiently, occurrences of the rule may be expanded by replacing the rule with its constituent expressions. Expansion may be performed repeatedly, until only grounded expressions remain. Expressions are grounded when they refer to tables or views that are represented in an underlying database. Once the rule has been reduced to grounded expressions, the semantic query processor may formulate a relational query in terms of the grounded expressions. If the relational query takes into account the various grounded expressions to which the rule reduces, then the portion of the semantic query that refers to the rule may be processed without an excessive number of round trips to the relational database.

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Redmond, WA, United States of America

Stuart M Bowers