International Business Machines Corporation

Elemental Cognition LLC

Afrl/rij

Elmental Cognition LLC

Aditya Anand Kalyanpur

David Angelo Ferrucci

Jennifer Chu-Carroll

James W Murdock, Iv

David W Buchanan

James J Fan

Adam P Lally

Chang Wang

Mike Barborak

Greg Burnham

Adam Lally

Stefano Pacifico

Kavitha Srinivas

David C Gondek

Christopher A Welty

Wlodek Wlodzimierz Zadrozny

Achille Belly Fokoue-Nkoutche

Siddharth A Patwardhan

Branimir Konstantinov Boguraev

Sugato Bagchi

Aaron K Baughman

Alfio Massimiliano Gliozzo

Mihaela Ancuta Bornea

James William Murdock, Iv

Apoorv Agarwal

Shirin Saleem

Robert G Farrell

Min Wang

Benjamin P Segal

Michael Robert Glass

Edith Gail Schonberg

Lorrie Ann Tomek

Michael A Barborak

Achille Fokoue

David J McClosky

Jose Barrera

Julian Timothy Dolby

Anastasios Kementsietsidis

Li Li Ma

Brian Patrick Byrne

Kenneth James Barker

Michael James Ward

Songyun Duan

Richard L Darden

Kirill M Osipov

Aaron Stephen Jay Kershenbaum

David Melville

Clifton James McFate

Gregory H Burnham

Andrea Bradshaw

Thomas Breloff

Or Biran

David A Ferucci

Kailash Karthik Saravanakumar

David Gondek

Siddharth Patwardham

David A Ferrucci

A research assistant system may include a research tool and components and a user interface to discover and evidence answers to complex research questions. The research tools may include components to iteratively perform steps in a research process, including searching, analyzing, connecting, aggregating, synthesizing, and chaining together evidence from a diverse set of knowledge sources. The system may receive an input query and perform a semantic search for key concepts in a text corpus. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank and score the aggregated results data and present data on the user interface. The user interface may include prompts to iteratively guide user input to explore evidentiary chains and connect research concepts to produce research results annotated by evidence passages.

Interactive research assistant—multilink

A dynamic reasoning system may include a symbolic reasoning engine that iteratively calls a dynamic rule generator to answer an input query. The symbolic reasoning engine may determine a primary goal and/or secondary goals to generate proofs for the answer. The symbolic reasoning engine may call a rules component to provide rules to prove a current input goal. The rules component may use a static rule knowledge base and/or the dynamic rule generator to retrieve and rank rules relevant to the current input goal. The dynamic rule generator may generate new rules that lead to the current input goal. The dynamic rule generator may include a statistical model that generates unstructured or structured probabilistic rules based on context related to the input query. The symbolic reasoning engine may return a list of rules with confidence for explaining the answer to the input goal.

Knowledge representation and reasoning system and method using dynamic rule generator

Interactive research assistant&#x2014;life science

Interactive research assistant&#x2014;user interface/user experience (UI/UX)

A natural language interfacing system may use a knowledge acquisition tool to obtain structured representations from user input text. The system may initiate interaction with a request for input and a partial statement with blank text slots labeled by field types. The system may receive input text to fill in a slot of the partial statement and perform semantic parsing on the input text to identify a trigger concept. The system may generate a list of templates defining different semantic frames for the trigger concept. A generated template may include additional generated slots and/or suggested slot-fillers to guide user input. In response to a template selection, the partial statement includes the trigger concept annotated with a semantic frame. This process is repeated by iteratively updating the list of templates until the statement is completed. The statement is mapped to a structured representation including semantic frames.

Knowledge acquisition tool

Generating textual entailment pair by a natural language processing (NLP) system. The NLP system receives two input texts, such as a question and a candidate answer. The NLP system queries a database and retrieves passages likely to include text that support the candidate answer. The NLP system generates parse trees and performs term matching on the passages and scores them according to the matching. The NLP system detects anchor pairs in the question and in the passage and aligns subgraphs (within the parse trees) of one to the other based on matching. The NLP system identifies aligned terms in the question and the passage that are not in the aligned subgraphs. The NLP system identifies text fragments, for the question and the passage, within the non-aligned segments of their respective parse trees, that connect the aligned term to the aligned portion of the subgraph.

Entailment knowledge base in natural language processing systems

According to an aspect, concept identification in a question answering system includes receiving, at a computer processor, a text span as a subject of a search query. An aspect also includes searching a title-oriented document (TOD) corpus for the text span, and matching, by the computer processor, concepts in title-oriented documents TODs of the TOD corpus to the span of text. The matching of the concepts includes decomposing the title-oriented documents into a series of passages, scoring the passages identified as possible matches, and merging and ranking results of the scoring to produce final scores for each concept associated with the title-oriented documents.

Concept identification in a question answering system

An architecture and processes enable computer learning and developing an understanding of arbitrary natural language text through collaboration with humans in the context of joint problem solving. The architecture ingests the text and then syntactically and semantically processes the text to infer an initial understanding of the text. The initial understanding is captured in a story model of semantic and frame structures. The story model is then tested through computer generated questions that are posed to humans through interactive dialog sessions. The knowledge gleaned from the humans is used to update the story model as well as the computing system's current world model of understanding. The process is repeated for multiple stories over time, enabling the computing system to grow in knowledge and thereby understand stories of increasingly higher reading comprehension levels.

Architecture and processes for computer learning and understanding

A method of generating secondary questions in a question-answer system. Missing information is identified from a corpus of data using a computerized device. The missing information comprises any information that improves confidence scores for candidate answers to a question. The computerized device automatically generates a plurality of hypotheses concerning the missing information. The computerized device automatically generates at least one secondary question based on each of the plurality of hypotheses. The hypotheses are ranked based on relative utility to determine an order in which the computerized device outputs the at least one secondary question to external sources to obtain responses.

Generating secondary questions in an introspective question answering system

Growing community of inventors

Westwood, NJ, United States of America

Aditya Anand Kalyanpur