Method for learning a latent interest taxonomy from multimedia metadata

Abstract

Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool evaluates metadata associated with each digital multimedia object against a knowledge graph, where the knowledge graph is built from data including information external to each of the digital multimedia objects and where the knowledge graph provides a plurality of attributes. The analysis tool associates one or more of the plurality of attributes with each of the digital multimedia objects. Each of the attributes correlates with a time and a location described in the metadata of that object. The analysis tool identifies one or more concepts from attributes associated to each of the objects and maps each of the plurality of attributes to the one or more concepts.

What is claimed is: 
     
         1 . A method for identifying latent relationships between interests based on metadata of a plurality of digital multimedia objects, the method comprising:
 evaluating metadata associated with each digital multimedia object against a knowledge graph, wherein the knowledge graph is built from data comprising information external to each of the digital multimedia objects and wherein the knowledge graph provides a plurality of attributes;   associating, based on the evaluation, one or more of the plurality of attributes with each of the digital multimedia objects, the one or more attributes correlating with a time and a location described in the metadata of that object;   identifying one or more concepts from attributes associated to each of the objects; and   mapping each of the plurality of attributes to the one or more concepts.   
     
     
         2 . The method of  claim 1 , wherein mapping each of the plurality of attributes to the one or more concepts comprises:
 determining a membership score of each of the one or more attributes to each of the one or more concepts, wherein the membership score is a measure indicating a strength of correlation of a given attribute to a given concept.   
     
     
         3 . The method of  claim 2 , wherein each of the attributes is associated with at least one of the one or more concepts based on the membership score. 
     
     
         4 . The method of  claim 1 , further comprising:
 determining, from the one or more concepts, a hierarchical relationship between a first concept and at least a second concept.   
     
     
         5 . The method of  claim 1 , wherein each of the one or more concepts includes at least a first attribute that co-occurs with a second attribute. 
     
     
         6 . The method of  claim 1 , wherein the attributes are imputed to each of the one or more digital multimedia objects from the knowledge graph, wherein the attributes further describe a plurality of events scheduled to occur at one or more of the plurality of locations. 
     
     
         7 . The method of  claim 1 , wherein each of the digital multimedia objects is one of an image or a video. 
     
     
         8 . A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, perform an operation for identifying latent relationships between interests based on metadata of a plurality of digital multimedia objects, the operation comprising:
 evaluating metadata associated with each digital multimedia object against a knowledge graph, wherein the knowledge graph is built from data comprising information external to each of the digital multimedia objects and wherein the knowledge graph provides a plurality of attributes;   associating, based on the evaluation, one or more of the plurality of attributes with each of the digital multimedia objects, the one or more attributes correlating with a time and a location described in the metadata of that object;   identifying one or more concepts from attributes associated to each of the objects; and   mapping each of the plurality of attributes to the one or more concepts.   
     
     
         9 . The computer-readable storage medium of  claim 8 , wherein mapping each of the plurality of attributes to the one or more concepts comprises:
 determining a membership score of each of the one or more attributes to each of the one or more concepts, wherein the membership score is a measure indicating a strength of correlation of a given attribute to a given concept.   
     
     
         10 . The computer-readable storage medium of  claim 9 , wherein each of the attributes is associated with at least one of the one or more concepts based on the membership score. 
     
     
         11 . The computer-readable storage medium of  claim 8 , wherein the operation further comprises:
 determining, from the one or more concepts, a hierarchical relationship between a first concept and at least a second concept.   
     
     
         12 . The computer-readable storage medium of  claim 8 , wherein each of the one or more concepts includes at least a first attribute that co-occurs with a second attribute. 
     
     
         13 . The computer-readable storage medium of  claim 8 , wherein the attributes are imputed to each of the one or more digital multimedia objects from the knowledge graph, wherein the attributes further describe a plurality of events scheduled to occur at one or more of the plurality of locations. 
     
     
         14 . The computer-readable storage medium of  claim 8 , wherein each of the digital multimedia objects is one of an image or a video. 
     
     
         15 . A system, comprising:
 a processor; and   a memory storing one or more application programs configured to perform an operation for identifying latent relationships between interests based on metadata of a plurality of digital multimedia objects, the operation comprising:
 evaluating metadata associated with each digital multimedia object against a knowledge graph, wherein the knowledge graph is built from data comprising information external to each of the digital multimedia objects and wherein the knowledge graph provides a plurality of attributes; 
 associating, based on the evaluation, one or more of the plurality of attributes with each of the digital multimedia objects, the one or more attributes correlating with a time and a location described in the metadata of that object; 
 identifying one or more concepts from attributes associated to each of the objects; and 
 mapping each of the plurality of attributes to the one or more concepts. 
   
     
     
         16 . The system of  claim 15 , wherein mapping each of the plurality of attributes to the one or more concepts comprises:
 determining a membership score of each of the one or more attributes to each of the one or more concepts, wherein the membership score is a measure indicating a strength of correlation of a given attribute to a given concept.   
     
     
         17 . The system of  claim 16 , wherein each of the attributes is associated with at least one of the one or more concepts based on the membership score. 
     
     
         18 . The system of  claim 15 , wherein the operation further comprises:
 determining, from the one or more concepts, a hierarchical relationship between a first concept and at least a second concept.   
     
     
         19 . The system of  claim 15 , wherein each of the one or more concepts includes at least a first attribute that co-occurs with a second attribute. 
     
     
         20 . The system of  claim 15 , wherein the attributes are imputed to each of the one or more digital multimedia objects from the knowledge graph, wherein the attributes further describe a plurality of events scheduled to occur at one or more of the plurality of locations.