International Business Machines Corporation

Renee F Decker

Stanley John Vernier

Elinna Shek

Chengmin Ding

Octavian Florin Filoti

Daniel M Jamrog

Donna Karen Byron

Suzanne L Estrada

John A Morganti

Aditya S Gaitonde

Samir J Patel

Joseph F Zaffarano

Michael Drzewucki

James M Dixius

David Kenneth Sagal, Ii

Charles Evan Beller

Christopher Munjal Nolan

Suzanne Miller

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining data of a first user interactive user interfacing application for interactive engagement; and examining data of a second user interactive user interfacing application for interactive engagement.

Adaptive user interfacing

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining programming data defining a first user interactive user interfacing application for interactive engagement with an end user; examining programming data defining a second user interactive user interfacing application for interactive engagement with a user; identifying one or more difference between the second user interactive user interfacing application and the first user interactive user interfacing application using result data resulting from the examining programming data defining a first user interactive user interfacing application and resulting data resulting from the examining programming data defining a second user interactive user interfacing application; and generating update data for updating the second user interactive user interfacing application in dependence on the one or more difference between the second user interactive user interfacing application and the first user interactive user interfacing application.

A question answering (QA) system comprising memory for storing instructions, and a processor configured to execute the instructions to ingest source documents that include structured data and unstructured data to create a knowledge base, wherein the unstructured data includes table data; create table annotations to represent the table data; store the ingested structured data, unstructured data, and the table annotations in the knowledge base; and determine answers to questions using the knowledge base.

Framework for analyzing table data by question answering systems

In an approach to generating a digital sentiment signature to characterize an end to a communication, one or more computer processors detect a start of a communication between at least two participants. A computer starts a digital timer of the communication. A computer identifies one or more digital marks of the communication, where the one or more digital marks are a reflection of a sentiment of at least one of the at least two participants in the communication. A computer generates a digital sentiment signature based on the digital timer and on the one or more digital marks, where the digital sentiment signature is a digital signal that can be communicated across a plurality of types of communication channels. A computer detects an end of the communication. A computer determines a reason for the end of the communication. A computer stores the reason.

Digital sentiment signature generation

A system for question answering (QA) documents data ingestion decides to ingest the documents data through a first plurality of sub-pipelines including a first sub-pipeline having a first set of engines and a second sub-pipeline having a second set of engines being independent from the first set of engines. The system determines a subset of the documents data and decides to ingest the subset through a second plurality of sub-pipelines including a third sub-pipeline having a third set of engines and a fourth sub-pipeline having a fourth set of engines being independent from the third set of engines. A set of engines of the second plurality of sub-pipelines and a set of engines of the first plurality of sub-pipelines are in a common class. The system selects output data from the second plurality of sub-pipelines over corresponding output data from the first plurality of sub-pipelines and generates a knowledge base.

Plug-and-ingest framework for question answering systems

Embodiments for managing chatbots are provided. A set of documents is received. A plurality of entities are identified within the set of documents. At least one of the plurality of entities is selected based on a rareness criteria. Contextual data associated with each of the selected at least one of the plurality of entities is identified within the set of documents. At least one question-answer (QA) pair associated with each of the selected at least one of the plurality of entities is generated based on the identified contextual data.

Methods and systems for managing chatbots with respect to rare entities

An approach is provided in which the approach provides a first question to a requestor requesting access to a resource. The first question corresponds to a first cognitive level and includes at least one image selected from a set of images. The approach computes a confidence value of a first answer received from the requestor responding to the first question. In response to determining that the confidence value is below a confidence threshold, the approach provides a second question to the requestor corresponding to a second cognitive level that is increased from the first cognitive level. The approach grants access to the requestor in response to determining that a second answer received from the requestor responding to the second question is a correct answer.

Detector for online user verification

A method for supplementing context in dialog flows for chatbot systems includes responsive to receiving identification information associated with a user and a topic for a chatbot conversation, initializing a chatbot conversation between a chatbot and the user. The method identifies a set of entities and a set of one or more relationships, wherein each relationship from the set one or more relationships is between two entities from the set of entities. The method determines an initial set of relevancy scores for each entity in the set of entities and the set of one or more relationships, wherein the initial set of relevancy scores are based at least on a domain for the topic of the chatbot conversation. The method generates a first response to the user based on the initial set of relevancy scores, wherein the first response includes at least one entity and at least one relationship.

Context retrieval for chatbots

Aspects of the present invention disclose a method for extracting information of an unlabeled image within a document and aligning the information to text of the document. The method includes one or more processors identifying an image that is not associated with a corresponding label in a document that includes text. The method further includes determining a feature of an object of the image. The method further includes identifying an alignment candidate of the text of the document based at least in part on the feature of the object, wherein the alignment candidate is a segment of the text of the document identified as corresponding to the feature of the object. The method further includes aligning the feature with the alignment candidate of the text of the document.

Aligning unlabeled images to surrounding text

A system includes a memory having instructions stored therein. The system also includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to receive an input question in a source language, generate a first similarity score based at least in part on a source-language operand, generate a target-language operand based at least in part on the source-language operand, generate a second similarity score based at least in part on the target-language operand, generate a semantic concept score based at least in part on the first similarity score and the second similarity score, generate a set of ranked answers to the input question ('ranked answer set') based at least in part on the semantic concept score, and output the ranked answer set.

Semantic concept scorer based on an ensemble of language translation models for question answer system

A system for handling a graphical representation of data associated with a question answering (QA) input document includes a memory having instructions therein and includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to derive, at least from a portion of the QA input document, first metadata regarding a context of the graphical representation of data. The at least one processor is also configured to execute the instructions to derive, at least from a portion of the graphical representation of data, tabular data. The at least one processor is also configured to execute the instructions to determine, at least in part by comparing at least a portion of the first metadata to existing table annotations from a QA knowledge base, how to incorporate the tabular data into the QA knowledge base.

Framework for analyzing graphical data by question answering systems

A method trains an artificial intelligence system to respond to a complex query. A query/answer (Q/A) system sends a first query to an information server, where the first query is a query about a first topic with regard to a first entity, and where the information server provides access to unstructured information sources. In response to receiving a negative response to the first query for the first entity, the Q/A system sends variations of the first query and a second query about a second topic to the information server. The Q/A system identifies a pattern in the table of information in order to create the answer to the first query for the first entity. The Q/A system then trains an artificial intelligence system to respond to a complex query about the first entity with information in the table of information.

Training an artificial intelligence to generate an answer to a query based on an answer table pattern

A method of augmenting a knowledge graph includes obtaining the knowledge graph, which includes entities and relationships between the entities defining respective edges, clustering the entities into knowledge domains using semantic distances determined between the entities and a threshold on the semantic distances, identifying strengths of the relationships between adjacent entities in the knowledge graph, creating knowledge chains from node pairs in the knowledge graph, including generating a minimum spanning tree using the strengths of the relationships, pruning edges from the knowledge chain using a threshold on weights corresponding to the edges, defining a first knowledge index for each of the knowledge chains, defining a second knowledge index for each of the knowledge domains, and defining a third knowledge index for the knowledge graph as a harmonic mean of a sum of the first knowledge indexes and a sum of the second knowledge indexes.

Multi-dimensional knowledge index and application thereof

Aspects include creating a profile for a user from user account data and a user activity log, searching an indexed repository for keywords matching elements from the user account data and the user activity log, and retrieving documents from the indexed repository that are identified based on the searching. Aspects further include creating a custom portal for the user that includes the documents.

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Brunswick, MD, United States of America

Renee F Decker