Abstract: Technology for using a computing device to interpret entity and relationship occurrences a natural language understanding system that includes the following operations (not necessarily in the following order): (i) receiving a corpus that includes unstructured data and/or structured data; (ii) parsing the corpus to obtain parsed corpus information; (iii) applying artificial intelligence to the parsed corpus information to determine a plurality of logical relationships manifested by the corpus; and (iv) expressing, by machine logic, the plurality of logical relationships as a respectively corresponding plurality of logical rule expressions, with each logical rule expression of the plurality of logical rule expressions expressing the respectively corresponding logical relationship as fact(s) with regard to the corpus.

Abstract: Embodiments relate to a computer system, computer program product, and method to prevent unauthorized file dissemination and replication. A file parameter is defined, with the defined file parameter including a file dissemination characteristic. The file is encoded with the defined file parameter as file metadata. Dissemination and replication of the file is managed responsive to the encoded file parameter. The defined parameter is assessed along with a physical replication destination. The file is selectively replicated or transmitted responsive to the file parameter and the destination assessment.

Abstract: Embodiments of the present invention relate to performing entity recognition on a stream while providing ongoing training or supplementation of an entity dictionary. In one embodiment, a method of and computer program product for stream based named entity recognition is provided. A first portion of a textual input is received. A plurality of patterns is applied to the first portion to determine that a predetermined type is present in the first portion. Approval is requested of the presence of the predetermined type. An indication of approval or disapproval of the predetermined type is received. A dictionary is supplemented according to the indication. A second portion of the textual input is received. The plurality of patterns is applied to the second portion.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.

Abstract: Embodiments relate to a system, program product, and method for knowledge resource management. A first document is subjected to a first semantic annotation and one or more entities, relations, and textual annotations of interest are identified. A neural model is built with the first document and trained with the first document and one or more of the first semantic annotations. An un-annotated document is applied to the neural model, and one or more second semantic annotations are produced. The un-annotated document is enriched with the produced second semantic annotation(s) and is subjected to adjudication. The neural model is selectively amended responsive to the adjudication.

Abstract: Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Abstract: Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Abstract: Embodiments relate to a system, program product, and method for iterative expansion and application of a domain-specific dictionary. One or more dictionary instances are applied against a text corpus. The dictionary is iteratively expanded and selectively populated with one or more additional dictionary instances, including semantically similar instances to the applied dictionary instances and extension instances contextually related to the applied dictionary instances. The iteratively expanded dictionary is applied to an unexplored corpus to identify matching corpus data to populated instances of the dictionary.

Abstract: A computer-implemented method according to one embodiment includes receiving a request to perform an action associated with a virtual monetary account, applying one or more rules associated with the virtual monetary account to one or more characteristics of the request, and conditionally implementing the request, based on results of the applying.

Abstract: One embodiment provides a method for on-demand relation extraction from unstructured text that includes obtaining a text corpus of domain related unstructured text. Representations of the unstructured text that capture entity-specific syntactic knowledge are created. Initial user seeds of informative examples containing relations are received. Extraction models in a neural network are trained using the initial user seeds. Performance information and a confidence score are provided for each prediction for each extraction model. A next batch of informative examples are identified for annotation from the text corpus based on training a neural network classifier on a pool of labeled informative examples. Stopping criteria is determined based on differences of the performance information and the confidence score in relation to parameters for each extraction model. Based on the stopping criteria, it is determined whether to retrain a particular extraction model after the informative examples have been labeled.

Abstract: One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.

Abstract: One embodiment provides a method for relevant language-independent terminology extraction from content, the method including extracting lexicon items from the content based on context extraction patterns using statistical processing. Feedback on the extracted lexicon items is received to automatically tune scores and thresholds for the context extraction patterns. Available Linked Data is leveraged for a bootstrap source. The relevant language-independent terminology extraction is bootstrapped using the bootstrap source.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: A method for updating automated annotations for a paper-based document is provided. The method may include receiving an image of the paper-based document, the image of the paper-based document including a plurality of highlighted annotations having at least one edited annotation. The method may also include extracting the at least one edited annotation from the received image of the paper-based document. The method may further include adding the extracted at least one edited annotation of the paper-based document to a data retention system.

Abstract: Embodiments of the present invention disclose a computer system for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: Embodiments of the present invention disclose a computer program product for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: Embodiments of the present invention disclose a computer system for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: Embodiments of the present invention disclose a computer program product for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: Embodiments of the present invention disclose a method for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for optimizing data analysis. A set of analytics are received and a plurality of subsets of the set of analytics is generated. An optimization goal(s) is defined. The plurality of subsets of analytics are applied to a set of data. The output of applied analytics are recorded on a data storage device by the computer. Performance values of the computer applying the plurality of subsets of analytics are recorded and displayed. An optimal subset of the plurality of subsets of analytics is calculated, using recorded performance values and outputs of the applied plurality of subsets of analytics, being based on the optimization goal. A set of applied analytics are displayed, where the set of applied analytics includes a number of subsets of analytics of the plurality of analytics that meet the criteria of the optimal subset.