Oracle International Corporation

Alberto Parravicini

Hassan Chafi

Sungpack Hong

Davide Bartolini

Rhicheek Patra

Matthias Brantner

Lukas Stadler

Arnaud Delamare

Jinha Kim

Techniques described herein allow for accurate translation of natural language (NL) queries to declarative language. A syntactic dependency parsing tree is generated for an NL query, which is used to map tokens in the query to logical data model concepts. Relationship-type mappings are completed based on relationship constraints. Final mappings are identified for any relationship tokens that are associated with multiple candidate mappings by identifying which candidate mappings have the lowest cost metrics. An NL query-specific query graph is generated based on the mapping data for the NL query and the logical data model. The query graph represents an NL query-specific version of the logical data model where grammatical dependencies between NL query words are translated to the query graph. A query graph is annotated with information, from the mapping data, that is not represented paths in the query graph. The query graph is used generate a computer-executable translation of the NL query.

Natural language query translation based on query graphs

According to an embodiment, a method includes converting a knowledge base into a graph. In this embodiment, the knowledge base contains a plurality of entities and specifies a plurality of relationships among the plurality of entities, and entities in the knowledge base correspond to vertices in the graph, and relationships between entities in the knowledge base correspond to edges between vertices in the graph. The method may also include extracting a plurality of vertex embeddings from the graph. An example vertex embedding of the plurality of vertex embeddings represents, for a particular vertex, a proximity of the particular vertex to other vertices of the graph. Further, the method may include performing, based at least in part on the plurality of vertex embeddings, entity linking between input text and the knowledge base.

Named entity disambiguation using entity distance in a knowledge graph

Techniques are provided for an automated method of adding out-of-bound access prevention in GPU kernels executed in a managed environment. In an embodiment, a system of computers compiles a GPU kernel code function that includes one or more array references that are memory address dependent. The system of computers compiles the kernel code function by generating a rewritten GPU kernel code module that includes, within the function signature of the rewritten GPU kernel code module, a respective array size parameter for each array reference of the one or more array references included in the GPU kernel code function. The system of computers further compiles the kernel code function by adding bounding protection instructions to the one or more potential out-of-bound access instructions in the rewritten GPU kernel code module. The potential out-of-bound access instructions comprise instructions that reference each respective array size parameter of the one or more array references. Afterwards, the rewritten GPU kernel code module is loaded in a virtual machine. Loading the rewritten GPU kernel code module in the virtual machine comprises modifying a host application to automatically transmit, from the host application, one or more input array size values. The one or more input array size values is referenced by the one or more potential out-of-bound-access instructions.

Automatic out-of-bound access prevention in GPU kernels executed in a managed environment

Techniques are described herein for performing named entity disambiguation. According to an embodiment, a method includes receiving input text, extracting a first mention and a second mention from the input text, and selecting, from a knowledge graph, a plurality of first candidate vertices for the first mention and a plurality of second candidate vertices for the second mention. The present method also includes evaluating a score function that analyzes vertex embedding similarity between the plurality of first candidate vertices and the plurality of second candidate vertices. In response to evaluating and seeking to optimize the score function, the method performs selecting a first selected candidate vertex from the plurality of first candidate vertices and a second selected candidate vertex from the plurality of second candidate vertices. Further, the present method includes mapping a first entry from the knowledge graph to the first mention and mapping a second entry from the knowledge graph to the second mention. In this embodiment, the first entry corresponds to the first selected candidate vertex and the second entry corresponds to the second selected candidate.

Growing community of inventors

Lesmo, Italy

Alberto Parravicini