Abstract: 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.

Abstract: 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.

Abstract: 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 semantic concepts and relations in a text corpus. The system may receive domain rules to guide search. A semantic parser may interpret the search results. The system may aggregate and synthesize information from interpreted results. The system may rank the aggregated results data and present data on the user interface. The user interface may include filters to refine query results and highlight evidence snippets.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A natural language programming system may configure a machine learning (ML) model to translate natural language descriptions into constrained language statements. The constrained language statements may express the natural language descriptions using a constrained subset of natural language. The constrained subset of natural language includes words with unambiguous semantics and with meaning that has a clear and singular interpretation. The constrained language statements with unambiguous semantics enable construction of valid statements in high-level “English-like” executable programming language. With the present system, a user does not need to learn a new programming language but rather learn to constrain their natural language statements to a subset of the natural language (“constrained language”) and to generate executable programs.

Abstract: 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.

Abstract: A meta model may be provided as a global structure providing flexible or customizable options for a specific ontology designed by a system operator. A meta model may include generic structures, such as attributes, attribute categories, and attribute properties. A system operator may configure a set of specific attributes, attribute categories, and synthesis rules within the meta model to define a desired ontology, customizing the system to a specific purpose. A system can receive assertions about points of interest known to the system, and store information about attributes of points of interest based on the specified ontology.

Abstract: 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.

Abstract: 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.

Abstract: A meta model may be provided as a global structure providing flexible or customizable options for a specific ontology designed by a system operator. A meta model may include generic structures, such as attributes, attribute categories, and attribute properties. A system operator may configure a set of specific attributes, attribute categories, and synthesis rules within the meta model to define a desired ontology, customizing the system to a specific purpose. A system can receive assertions about points of interest known to the system, and store information about attributes of points of interest based on the specified ontology.

Abstract: 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.

Abstract: A collaborative user support system may include support components and a user portal to interact with a user and generate solutions to user problems. The support components may include a natural language understanding (NLU) engine, a reasoning engine, a semantic search engine, a multimodal dialog engine, and an active learning engine. The user portal may receive input dialog and the NLU engine may translate the dialog into machine language. The reasoning engine may determine whether the dialog includes a problem and call the semantic search engine to identify a potential solution. The multimodal dialog engine may determine a visual representation of a problem and/or solution. The active learning engine may receive user feedback on generated solutions for continuous improvements to the system.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving a query, conducting a search through one or more data sources to identify candidate answers to the query, and providing each of the candidate answers with a preliminary score. The method further comprises filtering out any of the candidate answers with a preliminary score that does not satisfy a defined condition. The candidate answers having preliminary scores that satisfy this condition form a subset of the candidate answers. Each of the candidate answers in this subset is processed to produce further scores. A ranking function is applied to these further scores to determine a ranking for each of the candidate answers in the subset; and after this ranking function is applied, one or more of the candidate answers are selected as one or more final answers to the query.

Abstract: 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.

Abstract: A method for generating inference graphs over content to answer input inquiries. First, independent factors are produced from the inquiry, and these factors are converted to questions. The questions are then input to a probabilistic question answering system (PQA) that discovers relations which are used to iteratively expand an inference graph starting from the factors and ending with possible answers. A probabilistic reasoning system is used to infer the confidence in each answer by, for example, propagating confidences across relations and nodes in the inference graph as it is expanded. The inference graph generator system can be used to simultaneously bi-directionally generate forward and backward inference graphs that uses a depth controller component to limit the generation of both paths if they do not meet. Otherwise, a joiner process forces the discovery of relations that join the answers to factors in the inquiry.

Abstract: A method, computer system, and computer program product for decision support is provided. The present invention may include receiving a problem case information and generating a query based on the problem case information. The present invention may also include generating a plurality of answers for the query using the question-answering module. The present invention may also include calculating numerical values for multiple evidence dimensions from evidence sources for each of the answers using the question-answering module and may further include calculating a corresponding confidence value for each of the answers based on the numerical value of each evidence dimension using the question-answering module. The present invention may also include outputting the generated answers, the corresponding confidence values, and the numerical values of each evidence dimension for one or more selected answers using the input/output module.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, identifying a plurality of candidate answers to the query; and for at least one of these candidate answers, identifying at least one proof of the answer. This proof includes a series of premises, and a multitude of documents are identified that include references to the premises. A set of these documents is selected that include references to all of the premises. This set of documents is used to generate one or more scores for the one of the candidate answers. A defined procedure is applied to the candidate answers to determine a ranking for the answers, and this includes using the one or more scores for the at least one of the candidate answers in the defined procedure to determine the ranking for this one candidate answer.

Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query, decomposing the input query into a plurality of different subqueries, and conducting a search in one or more data sources to identify at least one candidate answer to each of the subqueries. A ranking function is applied to each of the candidate answers to determine a ranking for each of these candidate answers; and for each of the subqueries, one of the candidate answers to the subquery is selected based on this ranking. A logical synthesis component is applied to synthesize a candidate answer for the input query from the selected the candidate answers to the subqueries. In one embodiment, the procedure applied by the logical synthesis component to synthesize the candidate answer for the input query is determined from the input query.