Some content of this application is unavailable at the moment.
If this situation persists, please contact us atFeedback&Contact
1. (WO2019063449) SEMANTIC SEARCH ENGINE AND VISUALIZATION PLATFORM
Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

C LAI M S

What is claimed is:

1. A method comprising:

receiving a natural language query;

generating one or more parse trees based on the natural language query using one or more natural language processing procedures;

generating a structured query based on the one or more parse trees, the structured query generated based on an identified data source type;

executing a search on the data source using the structured query, the

execution of the search resulting a result set;

identifying a visualization type based on a type of data included within the result set; and

generating a visualization based on the visualization type and the result set.

2. The method of claim 1, wherein the one or more natural language processing procedures include lexical, syntactical, and semantic procedures.

3. The method of claim 2, wherein the lexical procedures include one or more of a tokenization, stemming, part-of-speech tagging, plural identification, abbreviation expansion, entity recognition, stop word identification, and spell-checking procedure.

4. The method of claim 2 or 3, wherein the syntactical procedures include one or more of an object identification, attribute identification, value identification, Boolean operation identification, aggregation identification, period aggregation identification, distinct operator identification, comparator identification, and positive or negative operator identification procedures.

5. The method of any one of claims 2 to 4, wherein the semantic procedures include one or more of an invalid interpretation detection, duplicate

interpretation detection, repeat pattern detection, and ranking procedures.

6. The method of any one of the preceding claims, wherein generating one or more parse trees comprises generating multiple parse trees and ranking the multiple parse trees using a genetic algorithm.

7. The method of any one of the preceding claims, wherein generating a structured query further comprises:

expanding the one or more parse trees into an internal structured query; and

translating the internal structured query into the structured query based on identified data source type.

8. The method of any one of the preceding claims, wherein identifying a visualization type further comprises:

analyzing data in the result set and a structure of the data in the result set to identify a set of features associated with the result set; and

identifying a set of visualization candidates associated with the set of features using a set of rules associating features to visualization candidates.

9. The method of claim 8, further comprising selecting one of the

visualization candidates using a Case-Based-Reasoning approach.

10. A device comprising:

a processor; and

a non-transitory memory storing computer-executable instructions therein that, when executed by the processor, cause the device to perform the operations of:

receiving a natural language query;

generating one or more parse trees based on the natural language query using one or more natural language processing procedures;

generating a structured query based on the one or more parse trees, the structured query generated based on an identified data source type;

executing a search on the data source using the structured query, the execution of the search resulting a result set;

identifying a visualization type based on a type of data included within the result set; and

generating a visualization based on the visualization type and the result set.

11. The device of claim 10, wherein the one or more natural language processing procedures include lexical, syntactical, and semantic procedures.

12. The device of claim 11, wherein the lexical procedures include one or more of a tokenization, stemming, part-of-speech tagging, plural identification, abbreviation expansion, entity recognition, stop word identification, and spell-checking procedure.

13. The device of claim 11 or 12, wherein the syntactical procedures include one or more of an object identification, attribute identification, value

identification, Boolean operation identification, aggregation identification, period aggregation identification, distinct operator identification, comparator

identification, and positive or negative operator identification procedures.

14. The device of any one of claims 11 to 13, wherein the semantic procedures include one or more of an invalid interpretation detection, duplicate

interpretation detection, repeat pattern detection, and ranking procedures.

15. The device of any one of claims 10 to 14, wherein generating one or more parse trees comprises generating multiple parse trees and ranking the multiple parse trees using a genetic algorithm.

16. The device of any one of claims 10 to 15, wherein generating a structured query further comprises:

expanding the one or more parse trees into an internal structured query; and

translating the internal structured query into the structured query based on identified data source type.

17. The device of any one of claims 10 to 16, wherein identifying a

visualization type further comprises:

analyzing data in the result set and a structure of the data in the result set to identify a set of features associated with the result set; and

identifying a set of visualization candidates associated with the set of features using a set of rules associating features to visualization candidates.

18. The device of claim 17, further including instructions causing the device to perform the operation of selecting one of the visualization candidates using a Case-Based-Reasoning approach.

19. A system comprising:

a query analyzer comprising at least one server configured to receive a natural language query and generate one or more parse trees based on the natural language query using one or more natural language processing procedures;

a query engine comprising at least one application server configured to generate a structured query based on the one or more parse trees, the structured query generated based on an identified data source

type and execute a search on the data source using the structured query, the execution of the search resulting a result set; and

a visualization engine comprising at least one application server

configured to identify a visualization type based on a type of data included within the result set and generate a visualization based on the visualization type and the result set.

20. The system of claim 19, further comprising a plurality of data connectors, wherein the data connectors are configured to expand the one or more parse trees into an internal structured query and translate the internal structured query into the structured query based on identified data source type.