Abstract: An approach is provided in which a system transforms a set of embedding approximation values corresponding to a set of knowledge graph nodes into a set of binary valued embedding vectors. The system evaluates the set of binary valued embedding vectors against a query and a selects one of the binary valued embedding vectors based on the evaluation. The system then identifies one of the knowledge graph nodes that corresponds to the selected binary valued embedding vector and in turn, provides a result to the query based on the identified knowledge graph node.

Abstract: An approach is provided in which a system transforms a set of embedding approximation values corresponding to a set of knowledge graph nodes into a set of binary valued embedding vectors. The system evaluates the set of binary valued embedding vectors against a query and a selects one of the binary valued embedding vectors based on the evaluation. The system then identifies one of the knowledge graph nodes that corresponds to the selected binary valued embedding vector and in turn, provides a result to the query based on the identified knowledge graph node.

Abstract: Various embodiments set forth systems and techniques for training a sentence preprocessing model. The techniques include determining, using a machine translation system, a back translation associated with a ground truth translation of a source sentence in a source language to a target language, wherein the back translation comprises a translation of the ground truth translation from one or more target languages to the source language; determining, using the sentence preprocessing model, a simplified sentence associated with the source sentence; and updating one or more parameters of the sentence preprocessing model based on the simplified sentence and the back translation.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

Abstract: Mechanisms are provided for performing a cognitive operation. The mechanisms receive an original graph data structure comprising nodes and edges between nodes and activity log information for nodes of the original graph data structure. The mechanisms identify a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes. The mechanisms calculate an importance weight for each edge in the set of edges and modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure. The mechanisms then perform a cognitive operation based on the modified graph data structure. The set of edges may comprise actual edges between the nodes and/or potential edges between the nodes.

Abstract: An approach is provided in which an information handling system trains multiple classifiers using a set of training samples. The information handling system selects a leader classifier from the multiple classifiers that generates the most amount of correct decisions corresponding to the set of training samples. Next, the information handling system identifies an endorser classifier from the multiple classifiers that generates the highest proportion of correct decisions among the endorser classifier's decisions matching the leader classifier's decisions, and combines the leader classifier and the endorser classifier into a combined classifier stage. In turn, the information handling system utilizes the combined classifier stage to process inquiries and generate results.

Abstract: An approach is provided in which a system transforms a set of embedding approximation values corresponding to a set of knowledge graph nodes into a set of binary valued embedding vectors. The system evaluates the set of binary valued embedding vectors against a query and a selects one of the binary valued embedding vectors based on the evaluation. The system then identifies one of the knowledge graph nodes that corresponds to the selected binary valued embedding vector and in turn, provides a result to the query based on the identified knowledge graph node.

Abstract: An approach is provided in which a system transforms a set of embedding approximation values corresponding to a set of knowledge graph nodes into a set of binary valued embedding vectors. The system evaluates the set of binary valued embedding vectors against a query and a selects one of the binary valued embedding vectors based on the evaluation. The system then identifies one of the knowledge graph nodes that corresponds to the selected binary valued embedding vector and in turn, provides a result to the query based on the identified knowledge graph node.

Abstract: An approach is provided in which an information handling system trains multiple classifiers using a set of training samples. The information handling system selects a leader classifier from the multiple classifiers that generates the most amount of correct decisions corresponding to the set of training samples. Next, the information handling system identifies an endorser classifier from the multiple classifiers that generates the highest proportion of correct decisions among the endorser classifier's decisions matching the leader classifier's decisions, and combines the leader classifier and the endorser classifier into a combined classifier stage. In turn, the information handling system utilizes the combined classifier stage to process inquiries and generate results.

Abstract: Mechanisms are provided for performing a cognitive operation. The mechanisms receive an original graph data structure comprising nodes and edges between nodes and activity log information for nodes of the original graph data structure. The mechanisms identify a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes. The mechanisms calculate an importance weight for each edge in the set of edges and modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure. The mechanisms then perform a cognitive operation based on the modified graph data structure. The set of edges may comprise actual edges between the nodes and/or potential edges between the nodes.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.

Abstract: A mechanism is provided for search engine domain transfer. The mechanism receives an input query to search a specialized domain from a user and performs a general-domain search based on the input query to generate a set of general-domain results. The mechanism generates a feature vector based on the general-domain results and generates a score for each document within the specialized domain based on the feature vector. The mechanism generates a ranked result set of documents based on the scores of the documents in the specialized domain and presents the ranked result set to the user.