Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A method and/or system for query expansion may include: providing a set of training data in a given domain in the form of training question texts and training answer texts, identifying disjoint answer words in the training answer text that do not occur in the associated training question text, generating a graph of question word nodes and answer word nodes generated from the set of training data for the given domain in the form of the training question texts and the training answer texts, wherein edges are provided between a disjoint pair of a question word node for a question word in a training question and an answer word node for a disjoint answer word in an associated training answer, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: A method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata are encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: A method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata are encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, and applying spreading activation through the graph to result in a top n most highly activated nodes that are used as candidate words for expansion of a user query input.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, including providing weightings for the nodes and edges based on frequency data; and receiving a user query input, activating input nodes in the graph for words in the user query input, and applying spreading activation through the graph using the weightings to result in a top n most highly activated nodes that are used as candidate words for expansion of the user query input.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A method and system are provided for query expansion. The method may include: providing a graph of question and answer word nodes generated from a set of training data for a given domain in the form of training question and answer texts, wherein the answer word nodes are disjoint words that do not occur in both a training question and an associated training answer and wherein edges are provided between a disjoint pair of a training question word and an associated training disjoint answer word, including providing weightings for the nodes and edges based on frequency data; and receiving a user query input, activating input nodes in the graph for words in the user query input, and applying spreading activation through the graph using the weightings to result in a top n most highly activated nodes that are used as candidate words for expansion of the user query input.

Abstract: A mechanism is provided for semantic graph augmentation for domain adaptation. A semantic graph is provided based on a knowledge base in which concepts are linked by semantic relationships. A domain corpus of content in a new target domain is analyzed for adaptation with reference to one or more resources that provide syntactic analysis of the domain content that links portions of the domain corpus to existing concepts and links in the semantic graph. Triples are extracted from the analysis of the domain corpus of any two concepts that share a linking syntactic relationship. The triples are evaluated as potential relationship assertions. A relationship assertion is selected for addition as a new link between existing concepts in the semantic graph.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: Method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata is encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Method and system are provided for adaptive evaluation of meta-relationships in semantic graphs. The method includes providing a semantic graph based on a knowledge base in which concepts in the form of graph nodes are linked by semantic relationships in the form of graph edges. Metadata is encoded in the edges and nodes of the semantic graph, of weightings for measuring a meta-relationship, wherein the meta-relationship applies to the concepts of the semantic graph and is independent of the semantic relationship defined by the edges of the semantic graph. A graph activation is carried out for an input context relating to one or more concepts of the semantic graph, wherein the weightings are applied to a spreading activation signal through the semantic graph to produce a measure of the meta-relationship for a sub-set of concepts of the semantic graph.

Abstract: Mechanisms are provided for latent ambiguity handling in natural language processing. The method may include: providing an annotated semantic graph based on a knowledge base in which nodes representing semantic concepts are linked by semantic relationships, wherein one or more nodes are annotated to indicate a latent ambiguity of the semantic concept; processing a natural language input by activation of the annotated semantic graph; during processing when encountering an annotated node indicating a latent ambiguity of the semantic concept, applying a pre-defined latent ambiguity process to the node; and replacing any annotated nodes indicating a latent ambiguity of the concept remaining in an output of the processing of the natural language input with an alternative node from the semantic graph.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.

Abstract: A system, method and computer program product provides improved performance in machine learning, decision making and similar processes. In one example method, a plurality of individual subsets of features of a dataset comprising multiple features are received. The subsets may be provided by applying one or more feature selection methods to the dataset. Each subset is represented as a graph based on a predefined graph template. The example method merges the graphs of the plurality of individual subsets by overlaying the graphs on each other to form a merged feature graph. The merged feature graph may be used for identifying a single subset of features for use in machine learning, decision making and similar processes.