Abstract: According to an embodiment of the present invention, a natural language query including an ambiguous entity is received from a user. A meaning of the ambiguous entity is determined based on an extracted language context of the natural language query. The determined meaning, extracted language context, and contextual information of the user is applied to a machine learning model to determine a plurality of computer applications from amongst multiple computer applications to process the natural language query. The determined applications are executed to produce results for the natural language query tailored to an interest of the user in accordance with the contextual information.

Abstract: The present invention relates to a method and system for associating gene expression data with a disease name. A first data set associated with a plurality of genetic probes for a plurality of biological samples may be received. The first data set may be sorted based on a normalized gene expression values for the plurality of genetic probes. A largest value gap of the normalized gene expression values may be identified. A set of expressed genes within the first data set may be identified. An indexable document may be generated for a biological sample of the plurality of biological samples comprising data associated with the set of expressed genes. A second data set associated with an expressed gene of the set of expressed genes may be searched. A disease name may be associated with an expressed gene based on a threshold correlation between the disease name and the expressed gene.

Abstract: According to embodiments of the present invention, methods, systems and computer readable media are provided, in a cognitive data processing system, for implementing a predictive analytics system that utilizes entity and non-entity information. A collection of content is processed to extract defined entities pertaining to one or more domains. Semantic relationships are determined between objects within the collection of content, wherein the objects include undefined entities. The defined entities and objects are resolved based on entity definitions and the semantic relationships to determine defined entities and undefined entities for a resulting data set. The resulting data set is processed to identify one or more relationships between a defined entity and an undefined entity.

Abstract: Methods, systems and computer readable media are provided for accessing faceted information using ontologies. Information for an initial entity, including different ontologies to which the initial entity belongs, is retrieved. Entities within different ontologies are determined. The determined entities and different ontologies are displayed on a user interface to enable traversal of the different ontologies for viewing of the determined entities. Accordingly, a user may search for an initial entity. Different ontologies, linked to the initial entity may be returned. A user may select an ontology, and ontologies in which the initial entity belongs are displayed. The user may select any of the displayed ontologies to access other entities in the selected ontology.

Abstract: Methods, systems and computer readable media are provided for accessing faceted information using ontologies. Information for an initial entity, including different ontologies to which the initial entity belongs, is retrieved. Entities within different ontologies are determined. The determined entities and different ontologies are displayed on a user interface to enable traversal of the different ontologies for viewing of the determined entities. Accordingly, a user may search for an initial entity. Different ontologies, linked to the initial entity may be returned. A user may select an ontology, and ontologies in which the initial entity belongs are displayed. The user may select any of the displayed ontologies to access other entities in the selected ontology.

Abstract: Methods, systems, and computer program products are provided for processing a request regarding relationships among instances of entities. A graphical representation of instances of entities is generated and includes one or more source nodes, each representing an instance of an input entity of a request, and one or more related nodes, each representing an instance of a second entity related to one or more corresponding instances of the input entity and associated with a corresponding confidence score for the relationship. Each of the one or more related nodes associated with a confidence score satisfying a threshold is identified. One or more supplemental nodes are added to the graphical representation, each of which represents a corresponding instance of a third entity with a relationship to a corresponding instance of the second entity. The graphical representation is traversed to identify relationships between instances of entities and produce results for the request.

Abstract: Methods, systems, and computer program products are provided for processing a request regarding relationships among instances of entities. A graphical representation of instances of entities is generated and includes one or more source nodes, each representing an instance of an input entity of a request, and one or more related nodes, each representing an instance of a second entity related to one or more corresponding instances of the input entity and associated with a corresponding confidence score for the relationship. Each of the one or more related nodes associated with a confidence score satisfying a threshold is identified. One or more supplemental nodes are added to the graphical representation, each of which represents a corresponding instance of a third entity with a relationship to a corresponding instance of the second entity. The graphical representation is traversed to identify relationships between instances of entities and produce results for the request.

Abstract: According to embodiments of the present invention, an entity may be represented by an unstructured feature vector comprising a plurality of features extracted from unstructured data using semantic analysis and a structural feature vector comprising a plurality of features from chemical structure data. A similarity matrix may be used to compare entities and generate a similarity score, based on both the unstructured feature vector and the structural feature vector for each entity. In some aspects, a user may enter a query (from which a chemical structural feature vector is dynamically generated) to compare against entities having unstructured and/or structural feature vectors, stored in a database.

Abstract: According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Abstract: According to embodiments of the present invention, an entity may be represented by an unstructured feature vector comprising a plurality of features extracted from unstructured data using semantic analysis and a structural feature vector comprising a plurality of features from chemical structure data. A similarity matrix may be used to compare entities and generate a similarity score, based on both the unstructured feature vector and the structural feature vector for each entity. In some aspects, a user may enter a query (from which a chemical structural feature vector is dynamically generated) to compare against entities having unstructured and/or structural feature vectors, stored in a database.

Abstract: According to embodiments of the present invention, similarity metrics or measures of similarity may be combined using an adaptive weighting scheme. A subset of entities from a first set of entities that have a known relationship is randomly selected. The subset is combined with a second set of entities that have an unknown relationship to each other and/or to the first set of entities. At least two different measures of similarity (similarity metrics) between the first set and the combined second set (including the subset) is determined for each entity in the second set. For each entity in the second set, the at least two different measures of similarity are compared, and a weight is assigned adaptively to each measure of similarity based on the magnitude of the measure of similarity. The weighted measures of similarity are combined to determine an aggregate adaptively weighted similarity score for each entity.

Abstract: Rationalization of network predictions using similarity to known connections is provided. In various embodiments, a graph is read. The graph comprises a plurality of nodes. Each of the plurality of nodes corresponds to an entity or property. The plurality of nodes is interconnected by a plurality of edges. Each edge corresponds to a relationship between connected nodes. A new edge in the graph is predicted. The new edge corresponds to a relationship between a first node and a second node. The first node corresponds to an entity and the second node corresponds to an entity or property. One or more additional nodes connected to the second node is located. The one or more additional nodes is scored according to its connections in common with the first node. One or more sources is provided to a user describing the connection between the one or more additional node and the second node.

Abstract: According to an embodiment of the present invention, a system designates each document in a collection of documents as a member of a first group containing known subjects for a concept of interest or as a member of a second group containing candidate subjects for the concept of interest and determines a subset of documents for at least one subject. The system generates a classifier based on the documents in the first and second groups and applies the classifier to a set of documents for the at least one subject to determine whether each document belong to the first and/or second group. The system generates a score for the at least one subject based on a quantity of documents for that subject assigned to the first group of documents relative to a total quantity of documents for that subject and ranks that subject based on the determined score for each subject.

Abstract: The present invention relates to a method and system for associating gene expression data with a disease name. A first data set associated with a plurality of genetic probes for a plurality of biological samples may be received. The first data set may be sorted based on a normalized gene expression values for the plurality of genetic probes. A largest value gap of the normalized gene expression values may be identified. A set of expressed genes within the first data set may be identified. An indexable document may be generated for a biological sample of the plurality of biological samples comprising data associated with the set of expressed genes. A second data set associated with an expressed gene of the set of expressed genes may be searched. A disease name may be associated with an expressed gene based on a threshold correlation between the disease name and the expressed gene.

Abstract: According to one embodiment, a method is provided for approximate named-entity extraction from a dictionary that includes entries, where each of the entries includes one or more words. Words are read from the entries of the dictionary, and network resources are searched to determine a frequency of occurrence of the words on the network resources. In view of the frequency of occurrence of the words located on the network resources, domain relevancy of the words in the entries of the dictionary is determined. A domain repository is created using top-ranked words as determined by the domain relevancy of the words. In view of the domain repository, signatures for both the entries of the dictionary and strings of an input document are computed. The strings of the input document are filtered by comparing the signatures of the strings against the signatures of the entries to identify approximate-match entity names.

Abstract: According to one embodiment, approximate named-entity extraction from a dictionary that includes entries is provided, where each of the entries includes one or more words. Words are read from the entries of the dictionary, and network resources are searched to determine a frequency of occurrence of the words on the network resources. In view of the frequency of occurrence of the words located on the network resources, domain relevancy of the words in the entries of the dictionary is determined. A domain repository is created using top-ranked words as determined by the domain relevancy of the words. In view of the domain repository, signatures for both the entries of the dictionary and strings of an input document are computed. The strings of the input document are filtered by comparing the signatures of the strings against the signatures of the entries to identify approximate-match entity names.

Abstract: An approach for composing an analytic solution is provided. After associating descriptive schemas with web services and web-based applets, a set of input data sources is enumerated for selection. A desired output type is received. Based on the descriptive schemas that specify required inputs and outputs of the web services and web-based applets, combinations of web services and web-based applets are generated. The generated combinations achieve a result of the desired output type from one of the enumerated input data sources. Each combination is derived from available web services and web-based applets. The combinations include one or more workflows that provide an analytic solution. A workflow whose result satisfies the business objective may be saved. Steps in a workflow may be iteratively refined to generate a workflow whose result satisfies the business objective.

Abstract: An approach for composing an analytic solution is provided. After associating descriptive schemas with web services and web-based applets, a set of input data sources is enumerated for selection. A desired output type is received. Based on the descriptive schemas that specify required inputs and outputs of the web services and web-based applets, combinations of web services and web-based applets are generated. The generated combinations achieve a result of the desired output type from one of the enumerated input data sources. Each combination is derived from available web services and web-based applets. The combinations include one or more workflows that provide an analytic solution. A workflow whose result satisfies the business objective may be saved. Steps in a workflow may be iteratively refined to generate a workflow whose result satisfies the business objective.

Abstract: Discovering a keyword query corresponding to an input collection of documents taken from a candidate pool includes selecting a document from a working set as the input set, and extracting a list of snippets in the selected document. For each snippet, executing a set of proximity queries based on selected terms in that snippet, and finding all possible proximity queries that return less than N query results from the candidate pool. A query is selected from said proximity queries, based on the selected query returning the greatest number of working set documents, and returning the smallest number of documents not in the working set. Documents returned by the selected query are removed from the working set, and the above steps are repeated until no documents remain in the working set. The disjunction of selected queries is returned as the discovered query.

Abstract: Embodiments of the invention provide a system, method and computer program products for information retrieval from multiple documents by proximity searching for search queries. A method includes generating an index for the multiple documents, wherein the index includes words in snippets in the documents. An input search query is processed against the index by searching query terms over the snippets to introduce term proximity information implicitly in the information retrieval. Results of multiple sentence level search operations are combined as output.