International Business Machines Corporation

Adam D Dumey

Charles Evan Beller

Elinna Shek

Chengmin Ding

Allen Ginsberg

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a media artifact annotation system, the method comprising inputting one or more relationships; for each of the one or more relationships, extracting, through an entity argument extraction module, one or more entity arguments; constructing, through a media query construction module, a media query using the one or more entity arguments; submitting the media query to a media search corpus; receiving search results comprising one or more media artifacts from the media search corpus; passing, through an annotation module, the search results to an annotator; receiving, through a response input module, one or more responses regarding each of the one or more media artifacts from the annotator; if the response for the media artifact is confirmatory, applying an annotation to the media artifact; and if the response for the media artifact is rejection, applying no annotation to the media artifact.

System and method for rapid annotation of media artifacts with relationship-level semantic content

A technique for responding to user input includes assigning respective initial confidence scores to relationship n-tuples in a knowledge graph (KG). Each of the n-tuples designates at least a first entity, a second entity, and a relationship between the first and second entities or a single entity and a relationship between the single entity and one or more properties of the single entity. Respective feature vectors are associated with each of the n-tuples. A training set that includes at least a subset of the n-tuples labeled with respective ground truth labels is generated. Respective initial weights are learned for the feature vectors based on the training set. Respective subsequent confidence scores are generated for each of the n-tuples based on the initial weights for the feature vectors. A response to user input is generated based on the subsequent confidence scores for one or more of the n-tuples.

Responding to user input based on confidence scores assigned to relationship entries in a knowledge graph

A technique for assigning confidence scores to relationship entries in a knowledge graph includes assigning respective initial confidence scores to relationship n-tuples in a knowledge graph. Each of the relationship n-tuples designates at least a first entity, a second entity, and a relationship between the first and second entities or a single entity and a relationship between the single entity and one or more properties of the single entity. Respective feature vectors are associated with each of the relationship n-tuples. A training set that includes at least a subset of the relationship n-tuples labeled with respective ground truth labels is generated. Respective initial weights are learned for the feature vectors based on the training set. Respective subsequent confidence scores are generated for each of the relationship n-tuples based on the initial weights for the feature vectors.

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Falls Church, VA, United States of America

Adam D Dumey