International Business Machines Corporation

Aleksandra Mojsilovic

Kush Raj Varshney

Ioana Monica Baldini Soares

Evan J Patterson

Jianying Hu

Jun Li Wang

Ajay Mohindra

Jennifer Ceil Lai

Francisco Phelan Curbera

Shubir Kapoor

Shilpa N Mahatma

Shahram Ebadollahi

Vikas Sindhwani

Ock Kee Baek

Bernice E Rogowitz

Jose Gomes

Gigi Yuen-Reed

Houtan Aghili

Kevin Singley

Maja Vukovic

Sameep Mehta

Biplav Srivastava

Ramya Raghavendra

Kalapriya Kannan

Mark Steven Squillante

John Thomas Richards

Darrell Christopher Reimer

Rachel K E Bellamy

Amit Dhurandhar

Manish Kesarwani

Bonnie Kathryn Ray

Matthew R Arnold

Kaoutar El Maghraoui

Michael James Hind

Jinfeng Yi

Karthikeyan Natesan Ramamurthy

Moninder Singh

Wanli Min

Stephanie Houde

David John Piorkowski

Ana Radovanovic

Jason Tsay

Francesca Rossi

Abhishek Kumar

Dongping Fang

Craig F Schreiber

Valerie C Wiggins

Kien T Pham

John H Bauer

Techniques facilitating using flow graphs to represent a data analysis program in a cloud based system for open science collaboration and discovery are provided. In an example, a system can represent a data analysis execution as a flow graph where vertices of the flow graph represent function calls made during the data analysis program and edges between the vertices represent objects passed between the functions. In another example, the flow graph can then be annotated using an annotation database to label the recognized function calls and objects. In another example, the system can then semantically label the annotated flow graph by aligning the annotated graph with a knowledge base of data analysis concepts to provide context for the operations being performed by the data analysis program.

Representation of a data analysis using a flow graph

Techniques for generating and searching semantic flow graphs are provided that include creating, by a system operatively coupled to a processor employing a semantic flow graph creation process, a semantic flow graph based on an ontology associated with a set of subjects and a raw flow graph determined from an analysis of a data set relating to the set of subjects and searching, by the system, the semantic flow graph to determine a subset of information of the semantic flow graph that is responsive to a query based on the query and information of the semantic flow graph.

Semantic queries based on semantic representation of programs and data source ontologies

A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Generation and management of an artificial intelligence (AI) model documentation throughout its life cycle

A bias detection method, system, and computer program product to evaluate bias in an artificial intelligence service include selecting a bias context, the bias context having a bias specification associated with the bias context, generating test data for determining the bias in the artificial intelligence service based on the bias specification and the bias context, and testing the artificial intelligence service for the bias with the generated test data.

Generating representative unstructured data to test artificial intelligence services for bias

A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.

Humanitarian crisis analysis using secondary information gathered by a focused web crawler

Methods and systems for generating prediction data are described. In an example, a processor may retrieve preferential data from a memory. The preferential data may include a set of preferences that corresponds to a first subset of objects among a set of objects, and may exclude preferences associated with a second subset of objects among the set of objects. Each preference may indicate a preferred object between a respective pair of objects among the first subset of objects. The processor may determine first predicted ratings corresponding to the first subset of objects based on the preferential data. The processor may determine second predicted ratings corresponding to the second subset of objects based on the preferential data. The processor may generate prediction data by populating entries of the prediction data with the first and second predicted ratings, where the prediction data may include predicted ratings of the set of objects.

Recommendation prediction based on preference elicitation

A method includes performing contextual association of entities using multi-source data. For each context the method performs co-clustering to identify distinct expert-skill associations; constructing single-entity unipartite graph representations and performing a random walk within each single-entity unipartite graph; for each single-entity unipartite graph, obtaining steady state distributions using the random walks to obtain clusters of experts and skills; performing a weighted two-way random walk across entity graphs (graph edges), giving preference to traversal within members of the same co-cluster; and performing link prediction for each context by dynamically adding edges, and obtaining overall skills predictions, analyses and inferences by merging the contexts and weighting the links of each context. The method can also use the context-specific weights obtained from the co-association information in a matrix completion procedure, and finally merge the context-specific outputs to obtain overall skills predictions, analyses and inferences. A computer program product and a system are also disclosed for performing the method.

Method, system and computer program product for automating expertise management using social and enterprise data

A computer-implemented method includes generating a raw flow graph based on execution of a program. A semantic flow graph is generated based on the raw flow graph and based on an ontology in a subject matter-domain associated with the program. The semantic flow graph illustrates one or more semantic concepts of the program.

Generating semantic flow graphs representing computer programs

An application as a service provided in a secure environment. A sandbox in a user's computing environment may be created. An application may be downloaded to the user's computing environment to run within the sandbox. Data sources associated with the user's computing environment may be searched and connectivity established with data registry of the data sources based on data description received with the application. The application may be run within the sandbox using the established connectivity. Metering may be performed to monitor usage of the application at the user's computing environment.

Healthcare as a service&#x2014;downloadable enterprise application

A method provides a random walk model with heterogeneous graphs to leverage multiple source data and accomplish prediction tasks. The system and method components include: 1) A heterogeneous graph formulation including heterogeneous instances of abstract objects as graph nodes and multiple relations as edges connecting those nodes. The different types of relations, such as client-vendor relation and client-product relation, are often quantified as the weights of edges connecting those entities; 2) To accomplish prediction tasks with such information, launching a multi-stage random walk model over the heterogeneous graph. The random walk within a subgraph with homogenous nodes usually produces the relevance between entities of the same type. The random walk across different type of nodes provides the prediction of decisions, such as a client purchasing a product.

Predictive and descriptive analysis on relations graphs with heterogeneous entities

A system, method and computer program product provides a random walk model with heterogeneous graphs to leverage multiple source data and accomplish prediction tasks. The system and method components include: 1) A heterogeneous graph formulation including heterogeneous instances of abstract objects as graph nodes and multiple relations as edges connecting those nodes. The different types of relations, such as client-vendor relation and client-product relation, are often quantified as the weights of edges connecting those entities; 2) To accomplish prediction tasks with such information, launching a multi-stage random walk model over the heterogeneous graph. The random walk within a subgraph with homogenous nodes usually produces the relevance between entities of the same type. The random walk across different type of nodes provides the prediction of decisions, such as a client purchasing a product.

The invention provides methods, systems and apparatus for assigning color names to individual image pixels, and generating verbal description of color composition in images, suitable for a wide variety of information processing applications. For an isolated image pixel (individual color sample or color value) an illustrative embodiment of this invention uses a predetermined vocabulary of color names, and then generates a distance measure describing the best color match and corresponding color name for the given pixel. For the input image, an illustrative embodiment of this invention computes the simplified representation of the scene, consistent with human perception, and uses the predetermined vocabulary, syntax rules and metric to assign color names to all perceptually significant objects and regions and obtain the verbal description of the overall color composition. The invention is also applicable to other types of information signals, such as sequences of video frames, web pages, etc.

Color naming, color categorization and describing color composition of images

Growing community of inventors

New York, NY, United States of America

Aleksandra Mojsilovic