International Business Machines Corporation

Alexander Pikovsky

Donna Karen Byron

Timothy P Winkler

Scott N Gerard

Matthew Sanchez

John P Bufe, Iii

Krishna Kummamuru

David Joel Pennell, Sr

David George Radley

Paul R Bastide

Robert E Loredo

Matthew E Broomhall

Suzanne L Estrada

Duke C Chang

Leanne N LeBlanc

Monty Ravindra Kamath

Eric Woods

Mary Swift

Amit P Bohra

Robert J McKeown

Swapnasarit Sahu

Jon Hylands

Stephen Pair

Colin Putney

Florian Pinel

Lakshminarayanan Krishnamurthy

Ashok Kumar

Jason D LaVoie

Daniel M Jamrog

Abhishek Shivkumar

Edward Eugene Seabolt

Kristin A Witherspoon

Leugim A Bustelo

Nicholas W Orrick

Travel itineraries are automatically prepared based upon user interests and sentiments inferred by deep semantic analysis of user-commented and user-preferred digital works of literature by receiving interests and associated sentiment levels for at least one user according to a deep semantic analysis of a plurality of works of literature, wherein the works of literature have been rated, commented, or both rated and commented by the user; searching repositories of travel items to find one or more matching travel items to the received more interests and associated sentiment levels; and preparing at least one travel itinerary including at least one found matching travel item. Travel itineraries may be prepared responsive to a user-initiated trip planning session, responsive to notification of a new travel item's availability, periodically, or a combination thereof.

Travel itinerary recommendation engine using inferred interests and sentiments

Mechanisms for training a Question and Answer (QA) system are provided. The QA system receives a training question for processing by the QA system and processes the training question to generate an answer to the training question, from a portion of content. The QA system identifies a repeatable pattern of content present in the portion of content in association with the answer to the training question. The QA system applies the repeatable pattern of content to other portions of content to generate at least one additional training question and at least one additional entry in a ground truth data structure to thereby expand a set of training questions and expand the ground truth data structure. The QA system is then trained using the expanded set of training questions and expanded ground truth data structure.

Expanding training questions through contextualizing feature search

A method, system, and computer program product for adapting tabular data for narration are provided in the illustrative embodiments. A set of categories used to organize data is identified in a first tabular portion of a document. A structure of the categories is analyzed. An inference is drawn about data in a first cell in the first tabular portion based on a position of the first cell in the structure. The first tabular portion of the document is transformed into a first narrative form using the inference.

Adapting tabular data for narration

Mechanisms are provided for performing an analysis of content based on a flow of property between entities. The mechanisms analyze a corpus of unstructured documents to identify one or more asset transfer flow relationships between entities. The mechanisms generate an asset transfer flow model defining a flow of an asset between entities based on the one or more asset transfer flow relationships between entities. The mechanisms input the asset transfer flow model into an analysis engine for use in analyzing content. The mechanisms analyze the content using the asset transfer flow model to generate results of the analysis and output the results of the analysis based on the asset transfer flow model.

Modeling asset transfer flow relationships discovered in unstructured data

Aspects include learning object/action pairs for recipe ingredients. A method includes extracting an ingredient list that includes a plurality of entries from a recipe stored in an electronic format. Each of the plurality of entries includes one or more words. For each multi-word entry in the ingredient list, an ingredient associated with the entry is identified by comparing words contained in the entry to terms in a lexicon of cooking domain terms. It is determined whether an action corresponds to the identified ingredient. The determining includes parsing the entry to locate a verb that is associated with the identified ingredient. Based on determining that an action corresponds to the identified ingredient, a food item action pair that includes the identified ingredient and the located verb is output.

Learning object/action pairs for recipe ingredients

A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

Rating difficulty of questions

Mechanisms for automatically generating a set of test questions for use in generating a test to be administered to human test takers are provided. The mechanisms ingest a document from a corpus of documents to select a potential test question answer from the document and generate a candidate test question based on the potential test question answer. The mechanisms evaluate the candidate test question using a Question and Answer (QA) system to generate a result indicative of whether the candidate test question should be maintained for test generation. The mechanisms store the candidate test question in the set of test questions in response to a result indicating that the candidate test question should be maintained for test generation.

Dynamic identification and validation of test questions from a corpus

An approach is provided for an information handling system to present knowledge-based information. In the approach, a semantic analysis is performed on the document with the analysis resulting in various sets of semantic content. Each of the sets of semantic content corresponds to an area in the document. The areas of the document are visually highlighted using visual indicators that show the availability of the sets of semantic content to a user via a user interface. In response to a user selection, such as a selection using the user interface or a user specified configuration setting, a selected set of semantic content is displayed to the user using the interface.

Active knowledge guidance based on deep document analysis

A mechanism is provided, in a question answering (QA) system, for performing persona-based question answering is provided. An identification of a requested persona is received from a user along with a natural language question input specifying an input question to be answered by the QA system. Responsive to receiving the requested persona, components of the QA system are customized to answer questions from a viewpoint of the requested persona. An answer to the input question is generated from the viewpoint of the requested persona based on the customization of the components of the QA system. The answer to the input question is output in a form representative of the requested persona.

Answering questions via a persona-based natural language processing (NLP) system

A method, system, and computer program product for creating or augmenting a business process model using a question and answer (Q and A) system are provided in the illustrative embodiments. A request to create the business process model of a business process is received. A knowledgebase is created using a business process modeling (BPM) data and user domain data. The user domain data comprises a combination of unstructured documents and structured documents related to an operation in a user domain. A natural language (NL) question is formed corresponding to the request. The NL question and the knowledgebase are submitted to the Q and A system. A set of answers is received from the Q and A system responsive to the NL question. An answer is presented as a part of the business process model.

Business process modeling using a question and answer system

Mechanisms are provided to implement a cognitive system collaboration identification and recommendation system (CSCIRS). The CSCIRS stores, in a cognitive system database, entries corresponding to a plurality of cognitive system instances. Each entry identifies operational characteristics of a corresponding cognitive system instance. The CSCIRS receives, from a requestor, a request to identify a potential collaborative cognitive system instance and identifies, based on the request and the cognitive system database, a candidate collaborative cognitive system instance. The CSCIRS generates a collaboration recommendation based on the identification and outputs an output corresponding to the collaboration recommendation.

Cognitive system comparison and recommendation engine

A method for adapting tabular data for narration is provided in the illustrative embodiments. A set of categories used to organize data is identified in a first tabular portion of a document. A structure of the categories is analyzed. An inference is drawn about data in a first cell in the first tabular portion based on a position of the first cell in the structure. The first tabular portion of the document is transformed into a first narrative form using the inference.

Growing community of inventors

Lexington, MA, United States of America

Alexander Pikovsky