International Business Machines Corporation

Lakshminarayanan Krishnamurthy

Donna Karen Byron

Niyati Parameswaran

Swaminathan Chandrasekaran

Priscilla Santos Moraes

Michael C Smith

Devendra Goyal

Sridhar Sudarsan

Joseph N Kozhaya

Joseph M Kaufmann

Craig M Trim

Anita Govindjee

Bharath Dandala

Reinaldo T Katahira

Laura J Bennett

Shanker Parameswaran

Anne Elizabeth Gattiker

Branimir Konstantinov Boguraev

Ming Tan

Saloni Potdar

Edward Eugene Seabolt

Devin A Conley

Timothy P Winkler

Krishna Kummamuru

William Graham O'Keeffe

David Deidou Taieb

Benjamin P Segal

Benjamin L Johnson

Ryan R Anderson

Cale R Vardy

Oren Sar-Shalom

Sadanand Rajaram Bajekal

Joan Walsh Tomlinson

Kelly A Argyros

Priscilla S Moraes

Alvin C Richardson

Christopher L Walk

William A Beason

Corville O Allen

Richard Joseph Stevens

Alexander Pikovsky

Andrew Jason Lavery

Fernando Jose Suarez Saiz

Pablo N Mendes

Eric L Erpenbach

Ravi S Sinha

Vincent J Dowling

One or more portions of a text corpus indicative of operational risk from a set of risk assessment documents is identified. Contextual features from the one or more portions of the text corpus are determined by applying a natural language processing (NLP) algorithm on the one or more portions. Risk identifiers are extracted based on the determined contextual features. A risk assessment software is generated based on the extracted risk identifiers and an operational risk category.

Generating risk assessment software

An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.

Enriched message embedding for conversation deinterleaving

Clustering a set of natural language queries NLQs based on a set of significant events retrieved from a corpus stored in a computer system is described. A set of NLQs is used by a search engine for searching a selected corpus to retrieve respective sets of significant events. The set of NLQs is clustered into a plurality of NLQ clusters according to a number of common significant events being returned by the search engine for respective members of an NLQ cluster.

Clustering a set of natural language queries based on significant events

In response to running at least one testing phrase on a previously trained text classifier and identifying a separate predicted classification label based on a score calculated for each respective at least one testing phrase, a text classifier decomposes extracted features summed in the score into word-level scores for each word in the at least one testing phrase. The text classifier assigns a separate heatmap value to each of the word-level scores, each respective separate heatmap value reflecting a weight of each word-level score. The text classifier outputs the separate predicted classification label and each separate heatmap value reflecting the weight of each word-level score for defining a heatmap identifying the contribution of each word in the at least one testing phrase to the separate predicted classification label for facilitating client evaluation of text classification anomalies.

Evaluating text classification anomalies predicted by a text classification model

A knowledgebase of an expert system is populated with rules inferred from a set of business processes that govern the manner in which the business interacts with users. Each business process contains an input, an output, an action, and a set of dependency relationships that relate pairs of the input, the output, and the action. Each process's input, output, action, and dependency relationships are translated, respectively, into a subject, an object, a predicate, and a set of dependency relationships among the subject, object, and predicate, of a natural-language rule. Each rule is stored in the expert system's knowledgebase as a directed graph, and nodes representing each stored subject, object, and predicate are assigned domain classifications as a function of characteristics of the business rule. These domain classifications are represented within the knowledgebase as a set of domain classifications determined as a further function of characteristics of the business rule.

Populating an expert-system knowledgebase based on correspondences between knowledgebase axioms and business processes

Embodiments provide a computer implemented method for generating a domain-specific thesaurus on a cognitive system, comprising: receiving data of the domain-specific corpus and a plurality of terms of interest from a user; splitting the data of the domain-specific corpus into a plurality of sentences using natural language processing techniques; for each term in the plurality of terms of interest, retrieving a plurality of candidate sentences containing a corresponding term, from the plurality of sentences; for each candidate sentence, providing a list of synonyms of the corresponding term, wherein the synonyms are contextual alternatives in the corresponding candidate sentence; for each term in the plurality of terms of interest, tracking a frequency of each synonym, and forming a frequency map including all the synonyms of a corresponding term and the frequency of each synonym; and generating a domain-specific thesaurus based on a combination of all the synonyms in the frequency map.

Generation of domain thesaurus

A method for providing discovery and realization of business measurement concepts may include providing at least one interface configured to receive an input from an operator associated with an organization, determining a selected set of glossary terms from a repository including a plurality of glossaries based at least in part on the input where the glossaries relate to different performance indicator components that are combinable to define a measurable performance indicator, and generating at least one performance indicator of the organization based on the selected set of glossary terms.

Discovery and generation of organizational key performance indicators utilizing  glossary repositories

A search space can be reduced using a pruned ontology that comprises entities from an initial ontology. A document corpus having a plurality of documents is received for processing. An ontology pruner determines a set of entities in the plurality of documents. For each entity in the set of entities, the ontology pruner determines a link for the entity in the initial ontology. The ontology pruner determines a score for the entity based on information obtained through the link for the entity in the initial ontology. The ontology pruner omits the entity from the pruned ontology if the score is below a predetermined or configurable threshold value.

Search space reduction for knowledge graph querying and interactions

According to embodiments of the present invention, medical treatment outcomes are simulated. A system receives a request to determine a medical treatment pertaining to a medical condition of a patient. The request is applied to one or more models, via a processor, to simulate outcomes for a plurality of different medical treatments for the medical condition, wherein the one or more models account for impacts of the plurality of different medical treatments on use of medical treatments awaiting future approval. The medical treatment is determined from the simulated outcomes, via a processor, with a desired level of impact on the medical treatments awaiting future approval based on characteristics of the patient. Methods and computer readable media are also provided herein for simulating medical treatment outcomes.

Cognitive systems for generating prospective medical treatment guidance

A test controller submits testing phrases to a text classifier and receives, from the text classifier, classification labels each comprising one or more respective heatmap values each associated with a separate word. The test controller aligns each of the classification labels corresponding with a respective testing phrase. The test controller identifies one or more anomalies of a selection of one or more classification labels that are different from an expected classification label for the respective testing phrase. The test controller outputs a graphical representation in a user interface of the selection of one or more classification labels and one or more respective testing phrases with visual indicators based on one or more respective heatmap values.

Displaying text classification anomalies predicted by a text classification model

A knowledge graph is built based on a corpus stored in the computer system. The corpus includes a set of searchable events and each event includes a respective set of entities. A set of entities is identified in a first set of significant events returned by natural language query (NLQ). The knowledge graph determines which ones of the set of entities are related to the entities in the NLQ to produce a filtered set of entities. The filtered set of entities is used to identify a second set of significant events in the selected corpus. Members of the first and second set of significant events are presented to a user as a search result.

Determining the significance of an event in the context of a natural language query

Electronic natural language processing in a natural language processing (NLP) system, such as a Question-Answering (QA) system. A receives electronic text input, in question form, and determines a readability level indicator in the question. The readability level indicator includes at least a grammatical error, a slang term, and a misspelling type. The computer determines a readability level for the electronic text input based on the readability level indicator, and retrieves candidate answers based on the readability level.

Readability awareness in natural language processing systems

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Round Rock, TX, United States of America

Lakshminarayanan Krishnamurthy