International Business Machines Corporation

Hima Prasad Karanam

L Venkata Subramaniam

Mukesh Kumar Mohania

Tanveer Afzal Faruquie

Marvin Mendelssohn

Snigdha Chaturvedi

Girish Venkatachaliah

Shajith I Mohamed

Sumit Neelam

Udit Sharma

Sachindra Joshi

Santosh Srivastava

Vijay Ekambaram

Sameep Mehta

Sriram Kolinjivadi Padmanabhan

Albert Maier

Shivakumar Vaithyanathan

Manish Kesarwani

Lucian Popa

Rajasekar Krishnamurthy

Vikas V Joshi

Mauricio Antonio Hernandez-Sherrington

Sumit Bhatia

Douglas Ronald Burdick

Dan Dan Zheng

Salil Joshi

Mohan Nagraj Dani

Tanveer A Faruquie

Angel Marie Smith

Bogdan Alexe

Heather Stimpson

Systems and techniques that facilitate question answering via tailored knowledge induction are provided. In various embodiments, a system can access a natural language question. In various aspects, the system can identify an answer for the natural language question, based on a tailored knowledge graph iteratively generated from a set of structured sub-queries associated with the natural language question.

Question answering via tailored knowledge induction

Methods, systems, and computer program products for extracting query-related temporal information from unstructured text documents are provided herein. A computer-implemented method includes obtaining at least one user query comprising one or more temporal components; converting at least a portion of the at least one user query into one or more logic form representations; mapping at least a portion of the one or more logic form representations to one or more portions of at least one source of unstructured text data; extracting temporal information, specific to the at least one user query, from one or more portions of the at least one source of unstructured text data based on the mapping; generating at least one response to the at least one user query based on the extracted temporal information; and performing one or more automated actions based on the at least one generated response.

Extracting query-related temporal information from unstructured text documents

Methods, systems, and computer program products for partitioning and parallel loading of property graphs with constraints are provided herein. A computer-implemented method includes obtaining graph-related input data and corresponding constraint data, wherein the graph-related input data and corresponding constraint data are at least one of user-defined and input data model-based; generating at least one in-memory graph based at least in part on processing at least a portion of the obtained graph-related input data; partitioning the at least one in-memory graph into two or more sub-graphs by processing the at least one in-memory graph using one or more polynomial time partition algorithms; and generating at least one property graph by allocating, at least a portion of the two or more partitioned sub-graphs which satisfy the obtained constraint data, to two or more threads that run in parallel.

Partitioning and parallel loading of property graphs with constraints

Methods, systems and computer program products for query processing are provided herein. A computer-implemented method includes receiving a first query from a user, determining whether the first query is capable of being answered using symbolic reasoning performed on data of a symbolic knowledge base, and executing the symbolic reasoning to generate a first query answer in response to a determination that the first query is capable of being answered using the symbolic reasoning. Axioms are extracted from a plurality of documents when it is determined that a second query is not capable of being answered using the symbolic reasoning. The method further includes determining whether the axioms are consistent with the symbolic knowledge base, and generating a second query answer based on the axioms in response to a determination that the one or more axioms are consistent with the symbolic knowledge base.

System and method for approximate reasoning using ontologies and unstructured data

Methods, systems, and computer program products for tracking missing data using provenance traces and data simulation are provided herein. A computer-implemented method includes generating, for each of multiple stages in a data curation sequence, a machine learning model of the data curation sequence, wherein the model is based on historical input records within the data curation sequence, historical output records within the data curation sequence, and provenance data within the data curation sequence; creating a simulated output record based on a detected anomaly corresponding to the data curation sequence; predicting the content of absent input records that precede the simulated output record in the data curation sequence and provenance data corresponding to the simulated output record; and outputting, to a user, in response to a query pertaining to the detected anomaly, the predicted input records and information relating the predicted input records to the detected anomaly.

Tracking missing data using provenance traces and data simulation

One embodiment provides a method, including utilizing at least one processor to execute computer code that performs the steps of: receiving, from a user, one or more keywords, wherein the one or more keywords identify, within a social media data set, a subset of social media data; determining, based on social media statistical information, an estimated volume of the subset of the social media data; determining, based on the social media statistical information, an estimated relevance of the subset of social media data to the user; and updating the one or more keywords, to improve the estimated volume and estimated relevance. Other aspects are described and claimed.

Keyword based data crawling

Methods, systems, and computer program products for deriving a multi-pass matching algorithm for data de-duplication are provided herein. A method includes identifying multiple passes across multiple databases using a set of one or more blocking columns derived from a set of trained input data; identifying, in each of the multiple passes, one or more columns across the multiple databases that match one or more of the blocking columns; selecting a given pass from the multiple passes, wherein said given pass comprises a maximum number of matching columns within the multiple passes; determining, for the given pass, data that conform to the given pass comprising (i) a set of matching columns, (ii) one or more matching types and (iii) one or more weights; and determining one or more subsequent passes across the multiple databases iteratively by removing the data that conform to the given pass.

Deriving a multi-pass matching algorithm for data de-duplication

Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Automatically mining patterns for rule based data standardization systems

Described herein are methods, systems and computer program products for entity resolution. Entity resolution, also known as entity matching or record linkage, seeks to identify equivalent data objects between or among datasets. An example method includes creating a deterministic model by defining an entity to be resolved, selecting two datasets for comparison, defining matching predicates for attributes of the datasets to select a set of candidate matches, and defining a precedence rule for the candidate matches to select a subset of the candidate matches. The method includes running the deterministic model on the two datasets. Running the deterministic model includes applying the matching predicates and the precedence rule to data in the datasets that correspond to the attributes. The method also includes applying a cardinality rule to results of the running, and outputting the matching candidates for which the cardinality rule is satisfied.

Entity resolution between datasets

Systems and associated methods for address standardization and applications related thereto are described. Embodiments exploit a common context in a taxonomy and a given address to detect and correct deviations in the address. Embodiments establish a possible path from a root of the taxonomy to a leaf in the taxonomy that can possibly generate a given address. Given a new address, embodiments use complete addresses, and/or segments or elements thereof, to compute the representations of the elements and find a closest matching leaf in the taxonomy. Embodiments then traverse the path to a root node to detect the agreement and disagreement between the path and the address entry. Taxonomical structured is thus used to detect, segregate and standardize the expected fields.

Systems and methods for standardization and de-duplication of addresses using taxonomy

A method, system and a computer program product for determining resources allocation in a distributed computing environment. An embodiment may include identifying resources in a distributed computing environment, computing provisioning parameters, computing configuration parameters and quantifying service parameters in response to a set of service level agreements (SLA). The embodiment may further include iteratively computing a completion time required for completion of the assigned task and a cost. Embodiments may further include computing an optimal resources configuration and computing at least one of an optimal completion time and an optimal cost corresponding to the optimal resources configuration. Embodiments may further include dynamically modifying the optimal resources configuration in response to at least one change in at least one of provisioning parameters, computing parameters and quantifying service parameters.

Resources management in distributed computing environment

According to one embodiment of the present invention, a system controls cleansing of data within a database system, and comprises a computer system including at least one processor. The system receives a data set from the database system, and one or more features of the data set are selected for determining values for one or more characteristics of the selected features. The determined values are applied to a data quality estimation model to determine data quality estimates for the data set. Problematic data within the data set are identified based on the data quality estimates, where the cleansing is adjusted to accommodate the identified problematic data. Embodiments of the present invention further include a method and computer program product for controlling cleansing of data within a database system in substantially the same manner described above.

Cleansing a database system to improve data quality

Methods, computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Systems, methods, and computer products for optimally managing large rule sets are disclosed. Rule dependencies of rules within a set of rules may be determined as a function of rules execution frequency data generated from applying the rules over a data set. The rules within the set of rules may be clustered into rules clusters based on the determined rule dependencies, in which the rules clusters comprise disjoint subsets of the rules within the set of rules. Cluster frequency data for the rules clusters may be used to arrive at an optimal ordering. Each rule within the set of rules may be assigned a unique identification that may capture an execution order of the rules within the set of rules.

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New Delhi, India

Hima Prasad Karanam