Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.