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: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: A method of augmenting a knowledge graph includes obtaining the knowledge graph, which includes entities and relationships between the entities defining respective edges, clustering the entities into knowledge domains using semantic distances determined between the entities and a threshold on the semantic distances, identifying strengths of the relationships between adjacent entities in the knowledge graph, creating knowledge chains from node pairs in the knowledge graph, including generating a minimum spanning tree using the strengths of the relationships, pruning edges from the knowledge chain using a threshold on weights corresponding to the edges, defining a first knowledge index for each of the knowledge chains, defining a second knowledge index for each of the knowledge domains, and defining a third knowledge index for the knowledge graph as a harmonic mean of a sum of the first knowledge indexes and a sum of the second knowledge indexes.

Abstract: A system, computer program product, and method are provided to automate a natural language processing system to facilitate an artificial intelligence platform defining a relationship between dialogue and post dialogue activity. Dialogue is detected and analyzed, including identification of key words and phrases within the dialogue. Post dialogue actions, including physical actuation of a hardware device and an associated temporal proximity of the action and the dialogue, are monitored. The hardware device receives an instruction from a processing unit that relates to the analyzed dialogue and the hardware device changes states and/or actuates another hardware device. The system constructs a hypothesis, i.e., a relationship from the identified key phrase drawn from the analyzed dialogue and the monitored post action dialogue. A dialogue tree containing identified terms and associated post dialogue actions is dynamically modified with one or more new identified terms and the associated post dialogue actions.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: 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 r

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

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.