SYMBOLIC CLUSTERING OF IoT SENSORS FOR KNOWLEDGE DISCOVERY

• US 20190325060A1
• Filed: 04/24/2018
• Published: 10/24/2019
• Est. Priority Date: 04/24/2018
• Status: Abandoned Application

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Fenoglio, Enzo, Latapie, Hugo, Fienga, Angelo
• Application Number:
  US15/960,957
• Publication Number:
  US 20190325060A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/906 Clustering; Classification
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

In one embodiment, a service in a network performs machine learning-based clustering of sensor data from a plurality of sensors in the network, to form sensor data clusters. The service maps the data clusters to symbolic clusters using a geometric conceptual space. The service infers a domain specific language from the symbolic clusters and from a domain specific ontology. The service performs, based on a query structured using the domain specific language, a lookup using the domain specific ontology to form a query response. The service sends the query response that comprises a result of the performed lookup via the network.

Claims

1. A method comprising:

performing, by a service in a network, machine learning-based clustering of sensor data from a plurality of sensors in the network, to form sensor data clusters;

mapping, by the service, the data clusters to symbolic clusters using a geometric conceptual space;

inferring, by the service, a domain specific language from the symbolic clusters and from a domain specific ontology;

performing, by the service and based on a query structured using the domain specific language, a lookup using the domain specific ontology to form a query response; and

sending, by the service, the query response that comprises a result of the performed lookup via the network.

2. The method as in claim 1, wherein the machine learning-based clustering of the sensor data comprises applying an unsupervised multimodal autoencoder to the sensor data.

3. The method as in claim 1, wherein the domain corresponds to a particular location.

4. The method as in claim 1, further comprising:

forming, by the service, the domain specific ontology by combining a base ontology with opinion or intention metadata mined from the domain.

5. The method as in claim 4, further comprising:

mining the opinion or intention metadata from aggregated user messages associated with the domain.

6. The method as in claim 1, wherein inferring the domain specific language from the semantic clusters and from the domain specific ontology comprises:

extracting concepts from the semantic clusters;

treating the extracted concepts as symbolic information; and

applying the domain specific ontology to the symbolic information, to infer the domain specific language.

7. The method as in claim 1, further comprising:

advertising, by the service, a class of sensor data available from one or more of the plurality of sensors, based on the domain specific language.

8. The method as in claim 1, wherein the geometric conceptual space comprises a plurality of quality dimensions, each point in the conceptual space comprising a set of quality values.

9. An apparatus, comprising:

one or more network interfaces to communicate with a network;

a processor coupled to the network interfaces and configured to execute one or more processes; and

a memory configured to store a process executable by the processor, the process when executed configured to;

perform machine learning-based clustering of sensor data from a plurality of sensors in the network, to form sensor data clusters;

mapping, by the service, the data clusters to symbolic clusters using a geometric conceptual space;

inferring, by the service, a domain specific language from the symbolic clusters and from a domain specific ontology;

performing, by the service and based on a query structured using the domain specific language, a lookup using the domain specific ontology to form a query response; and

sending, by the service, the query response that comprises a result of the performed lookup via the network.

10. The apparatus as in claim 9, wherein the machine learning-based clustering of the sensor data comprises applying an unsupervised multimodal autoencoder to the sensor data.

11. The apparatus as in claim 9, wherein the domain corresponds to a particular location.

12. The apparatus as in claim 9, wherein the process when executed is further configured to:

form the domain specific ontology by combining a base ontology with opinion or intention metadata mined from the domain.

13. The apparatus as in claim 12, wherein the process when executed is further configured to:

mine the opinion or intention metadata from aggregated user messages associated with the domain.

14. The apparatus as in claim 9, wherein the apparatus infers the domain specific language from the semantic clusters and from the domain specific ontology by:

extracting concepts from the semantic clusters;

treating the extracted concepts as symbolic information; and

applying the domain specific ontology to the symbolic information, to infer the domain specific language.

15. The apparatus as in claim 9, wherein the process when executed is further configured to:

advertise a class of sensor data available from one or more of the plurality of sensors, based on the domain specific language.

16. The apparatus as in claim 9, wherein the geometric conceptual space comprises a plurality of quality dimensions, each point in the conceptual space comprising a set of quality values.

17. A tangible, non-transitory, computer-readable medium storing program instructions that cause a service in a network to execute a process comprising:

performing, by the service in the network, machine learning-based clustering of sensor data from a plurality of sensors in the network, to form sensor data clusters;

mapping, by the service, the data clusters to symbolic clusters using a geometric conceptual space;

inferring, by the service, a domain specific language from the symbolic clusters and from a domain specific ontology;

performing, by the service and based on a query structured using the domain specific language, a lookup using the domain specific ontology to form a query response; and

sending, by the service, the query response that comprises a result of the performed lookup.

18. The computer-readable medium as in claim 17, wherein the domain corresponds to a particular industry.

19. The computer-readable medium as in claim 17, wherein the process further comprises:

forming, by the service, the domain specific ontology by combining a base ontology with opinion or intention metadata mined from the domain.

20. The computer-readable medium as in claim 19, wherein the process further comprises:

mining the opinion or intention metadata from aggregated user messages associated with the domain.