Accenture Global Services Gmbh

Nuance Communications, Inc.

Peter Zei-Chan Yeh

Adwait Ratnaparkhi

William Jarrold

Benjamin Douglas

Alex M Kass

Colin Anil Puri

Scott Kurth

Mary Elizabeth Hamilton

Sanjay Mathur

David L Martin

Deepak Ramachandran

Peter Frederick Patel-Schneider

John Mills Akred

John Y Miller

Jordan K Buller

Shaw-Yi Chaw

Erin Maneri

Jan Noessner

According to some aspects, a method for aligning a first data source and a second data source during a plurality of iterations comprising a current iteration and a previous iteration is provided. The method comprises generating at least one property alignment hypothesis between at least one first property of the first data source and at least one second property of the second data source; generating a plurality of instance alignment hypotheses between a respective first plurality of instances of the first data source and a respective second plurality of instances of the second data source; and verifying at least one property alignment hypothesis and/or at least one of the plurality of instance alignment hypotheses. Generating the at least one property alignment hypothesis and/or generating the plurality of instance alignment hypotheses is based, at least in part, on at least one property alignment hypothesis and/or at least one instance alignment hypothesis verified during the previous iteration.

Methods and system for iteratively aligning data sources

Aspects discussed herein present a solution for utilizing large-scale knowledge graphs for inference at scale and generating explanations for the conclusions. In some embodiments, aspects discussed herein learn inference paths from a knowledge graph and determine a confidence score for each inference path. Aspects discussed herein may apply the inference paths to the knowledge graph to improve database lookup, keyword searches, inferences, etc. Aspects discussed herein may generate a natural language explanation for each conclusion or result from one or more inference paths that led to that conclusion or result. Aspects discussed herein may present the best conclusions or results to the user based on selection strategies. The presented results or conclusions may include generated natural language explanations rather than links to documents with word occurrences highlighted.

Utilizing large-scale knowledge graphs to support inference at scale and explanation generation

A method is provided for representing and updating the state of a dialog involving a series of queries and commands to an artificial intelligence system. Each statement within the dialogue may be modeled as a relational tree spanning nodes corresponding to named entities within the statement. A data structure may be used to store each of these trees and to modify them as the dialog progresses. A subsequent statement in the dialog may be parsed and its contents used to update an ongoing search initiated within that dialog. Statements may be used for the update process despite being fragmentary or not corresponding to any predetermined grammar. An algorithm is disclosed for updating the trees within the data structure after a new statement is parsed.

State tracking over machine-learned relational trees in a dialog system

An extended collaboration event monitoring system monitors users' interactions with computer software applications and detects and extracts events. The system intelligently determines whether the extracted events trigger undetected events or other action items. The system provides the extracted events to a social networking client that translates the extracted events and returns the translated data to the system. The system publishes the translated data to a social networking/collaboration interface embedded into the interface of the computer software application being utilized by one of the users. The system not only publishes the translated data corresponding to a user's interactions with the computer software application to that user's interface, but also publishes the user's interactions with the computer software application to interfaces corresponding to other project team members as well.

Extended collaboration event monitoring system

Embodiments of the present invention solve the technical problem of identifying, collecting, and managing rules that improve poor quality data on enterprise initiatives ranging from data governance to business intelligence. In a specific embodiment of the present invention, a method is provided for producing data quality rules for a data set. A set of candidate conditional functional dependencies are generated comprised of candidate seeds of attributes that are within a certain degree of relatedness in the ontology of the data set. The candidate conditional functional dependencies are then applied to the data refined until they reach a quiescent state where they have not been refined even though the data they have been applied to has been stable. The resulting refined candidate conditional functional dependencies are the data enhancement rules for the data set and other related data sets. In another specific embodiment of the present invention, a computer system for the development of data quality rules is provided having a rule repository, a data quality rules discovery engine, and a user interface.

Method and system for accelerated data quality enhancement

A method, in one embodiment, can include encoding knowledge about a topic domain into a data modeling technique. Additionally, a set of candidate conditional functional dependencies can be generated based on a data set of the topic domain. Moreover, the set of candidate conditional functional dependencies and the data modeling technique encoded with the topic domain knowledge can be applied to the data set to obtain a plurality of data quality rules for the data set.

Data quality enhancement for smart grid applications

Growing community of inventors

San Jose, CA, United States of America

Peter Zei-Chan Yeh