System to generate curated ontologies
View Patent ↗Example embodiments relate to a network-based ontology curation system employed for receiving a request to view a data object, curating an ontology associated with the data object on-the-fly based on attributes of the request that include device and user characteristics.
1. A system comprising:
one or more processors of a machine; and
a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
receiving a request for a data object from a requesting device, the request including one or more request attributes that include at least a device type of the requesting device;
accessing a database that includes a set of data objects associated with the data object in response to the receiving the request;
retrieving the set of data objects associated with the data object from the database;
prioritizing the set of data objects associated with the data object based on the one or more request attributes, including the device type of the requesting device; and
curating an ontology based on the prioritized set of data objects associated with the data object.
2. The system of claim 1 , wherein the database is a primary database, and the operations further comprise:
receiving the set of data objects at the primary database, the set of data objects including the data object, an identifier of the data object, and a time-stamp of the data object;
parsing the set of data objects to retrieve the identifier and the time-stamp;
assigning a foreign key to the identifier and the timestamp, the foreign key indicating a location of the set of data objects within the primary database; and
indexing the foreign key, the identifier, and the time-stamp at a secondary database.
3. The system of claim 2 , wherein the request includes the identifier of the data object, and the retrieving the set of data objects from the primary database includes:
retrieving the foreign key from the secondary database based on the identifier; and
retrieving the set of data objects from the primary database.
4. The system of claim 2 , wherein the secondary database is a relational database.
5. The system of claim 1 , wherein the request is a first request, the ontology is a first ontology, and the operations further comprise:
training a machine learned model based on the first request and the request attributes;
receiving a second request that includes the request attributes of the first request; and
prioritizing the set of data objects for the second request based on the machine learned model and the request attributes.
6. The system of claim 1 , wherein the request is a first request, and the operations further comprise:
receiving a prioritization configuration associated with the request attributes of the request; and
wherein the prioritizing the set of data objects is based on the prioritization configuration and the request attributes.
7. The system of claim 1 , wherein the operations further comprise:
generating a presentation of the data object based on the ontology; and
causing display of the presentation at the requesting device.
8. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations comprising:
receiving a request for a data object from a requesting device, the request including request attributes that include at least a device type of the requesting device;
accessing a database that includes a set of data objects associated with the data object in response to the receiving the request;
retrieving the set of data objects from the database;
prioritizing the set of data objects based on the request attributes, including the device type of the requesting device; and
curating an ontology based on the prioritized set of data objects associated with the data object.
9. The non-transitory machine-readable storage medium of claim 8 , wherein the database is a primary database, and the operations further comprise:
receiving the set of data objects at the primary database, the set of data objects including at least the data object, an identifier of the data object, and a time-stamp of the data object;
parsing the set of data objects to retrieve the identifier and the time-stamp;
assigning a foreign key to the identifier and the timestamp, the foreign key indicating a location of the set of data objects within the primary database; and
indexing the foreign key, the identifier and the time-stamp at a secondary database.
10. The non-transitory machine-readable storage medium of claim 9 , wherein the request includes the identifier of the data object, and the operations for the retrieving the set of data objects from the primary database further comprises:
retrieving the foreign key from the secondary database based on the identifier; and
retrieving the set of data objects from the primary database.
11. The non-transitory machine-readable storage medium of claim 9 , wherein the secondary database is a relational database.
12. The non-transitory machine-readable storage medium of claim 8 , wherein the request is a first request, the ontology is a first ontology, and the operations further comprise:
training a machine learned model based on the first request and the request attributes;
receiving a second request that includes the request attributes of the first request; and
prioritizing the set of data objects for the second request based on the machine learned model and the request attributes.
13. The non-transitory machine-readable storage medium of claim 8 , wherein the request is a first request, and the operations further comprise:
receiving a prioritization configuration associated with the request attributes of the request; and
wherein the prioritizing the set of data objects is based on the prioritization configuration and the request attributes.
14. The non-transitory machine-readable storage medium of claim 8 , wherein the operations further comprise:
generating a presentation of the data object based on the ontology; and
causing display of the presentation at the requesting device.
15. A method comprising:
receiving a request for a data object from a requesting device, the request including request attributes that include at least a device type of the requesting device;
accessing a database that includes a set of data objects associated with the data object in response to the receiving the request;
retrieving the set of data objects from the database;
prioritizing the set of data objects based on the request attributes, including the device type of the requesting device; and
curating an ontology based on the prioritized set of data objects associated with the data object.
16. The method of claim 15 , wherein the database is a primary database, and the method further comprises:
receiving the set of data objects at the primary database, the set of data objects including at least the data object, an identifier of the data object, and a time stamp of the data object;
parsing the set of data objects to retrieve the identifier and the time stamp;
assigning a foreign key to the identifier and the time stamp, the foreign key indicating a location of the set of data objects within the primary database; and
indexing the foreign key, the identifier, and the time stamp at a secondary database.
17. The method of claim 16 , wherein the request includes the identifier of the data object, and the receiving the set of data objects at the primary database includes:
retrieving the foreign key from the secondary database based on the identifier; and
retrieving the set of data objects from the primary database.
18. The method of claim 16 , wherein the secondary database is a relational database.
19. The method of claim 15 , wherein the request is a first request, the ontology is a first ontology, and the method further comprises:
training a machine-learning model based on the first request and the request attributes;
receiving a second request that includes the request attributes of the first request; and
prioritizing the set of data objects for the second request based on the machine-learning model and the request attributes.
20. The method of claim 15 , wherein the request is a first request, and the method further comprises:
receiving a prioritization configuration associated with the request attributes of the request; and
wherein the prioritizing the set of data objects is based on the prioritization configuration and the request attributes.