Abstract: Techniques for using digital entity correlation to generate a composite knowledge graph from constituent graphs. In an aspect, digital attribute values associated with primary entities may be encoded into primitives, e.g., using a multi-resolution encoding scheme. A pairs graph may be constructed, based on seed pairs calculated from correlating encoded primitives, and further expanded to include subjects and objects of the seed pairs, as well as pairs connected to relationship entities. A similarity metric is computed for each candidate pair to determine whether a match exists. The similarity metric may be based on summing a weighted landing probability over all primitives associated directly or indirectly with each candidate pair. By incorporating primitive matches from not only the candidate pair but also from pairs surrounding the candidate pair, entity matching may be efficiently implemented on a holistic basis.

Abstract: Techniques for using digital entity correlation to generate a composite knowledge graph from constituent graphs. In an aspect, digital attribute values associated with primary entities may be encoded into primitives, e.g., using a multi-resolution encoding scheme. A pairs graph may be constructed, based on seed pairs calculated from correlating encoded primitives, and further expanded to include subjects and objects of the seed pairs, as well as pairs connected to relationship entities. A similarity metric is computed for each candidate pair to determine whether a match exists. The similarity metric may be based on summing a weighted landing probability over all primitives associated directly or indirectly with each candidate pair. By incorporating primitive matches from not only the candidate pair but also from pairs surrounding the candidate pair, entity matching may be efficiently implemented on a holistic basis.

Abstract: A computer implemented method for generating a set of updates for a database comprising multiple records including erroneous, missing and inconsistent values, the method comprising using a set of partitioning functions for subdividing the records of the database into multiple subsets of records, allocating respective ones of the records to at least one subset according to a predetermined criteria for mapping records to subsets, applying multiple machine learning models to each of the subsets to determine respective candidate replacement values representing a tuple repair for a record including a probability of candidate and current values for the record, computing probabilities to select replacement values for the record from among the candidate replacement values which maximise the probability for values of the record for an updated database.

Abstract: A computer implemented method for matching data records from multiple entities comprising providing respective transaction logs for the entities representing actions performed by or in respect of the entities, determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

Abstract: The subject disclosure is directed towards providing data for augmenting an entity-attribute-related task. Pre-processing is preformed on entity-attribute tables extracted from the web, e.g., to provide indexes that are accessible to find data that completes augmentation tasks. The indexes are based on both direct mappings and indirect mappings between tables. Example augmentation tasks include queries for augmented data based on an attribute name or examples, or finding synonyms for augmentation. An online query is efficiently processed by accessing the indexes to return augmented data related to the task.

Abstract: A computer implemented method for repairing records of a database, comprises determining a first set of records of the database which violate a functional dependency of the database, determining a second set of records of the database comprising duplicate records, computing a cost metric representing a measure for the cost of mutually dependently modifying records in the first and second sets, modifying records in the first and second sets on the basis of the cost metric to provide a modified database instance.

Abstract: A computer-implemented method for detecting a set of inconsistent data records in a database including multiple records, comprises selecting a data quality rule representing a functional dependency for the database, transforming the data quality rule into at least one rule vector with hashed components, selecting a set of attributes of the database, transforming at least one record of the database selected on the basis of the selected attributes into a record vector with hashed components, computing a dot product of the rule and record vectors to generate a measure representing violation of the data quality rule by the record.

Abstract: A computer implemented method for repairing records of a database, comprises determining a first set of records of the database which violate a functional dependency of the database, modifying records in the first set to make them consistent with the functional dependency to provide an output consistent database instance, determining a second set of records of the output consistent database instance comprising duplicate records, merging duplicate records in the second set in dependence on the functional dependencies of the records to provide a modified database instance.

Abstract: A computer implemented method for correcting records in a database comprising generating, using a processor, respective candidate replacement entries for multiple inconsistent records of the database, grouping the candidate replacement entries to provide multiple groups of related candidate updates for the database, ranking the groups according to a loss function to quantify database quality, receiving input for a selected group, sorting candidate replacement entries in the selected group, and applying updates from the selected group to the database to correct entries of the inconsistent records.

Abstract: The subject disclosure is directed towards providing data for augmenting an entity-attribute-related task. Pre-processing is preformed on entity-attribute tables extracted from the web, e.g., to provide indexes that are accessible to find data that completes augmentation tasks. The indexes are based on both direct mappings and indirect mappings between tables. Example augmentation tasks include queries for augmented data based on an attribute name or examples, or finding synonyms for augmentation. An online query is efficiently processed by accessing the indexes to return augmented data related to the task.

Abstract: A computer-implemented method for detecting a set of inconsistent data records in a database including multiple records, comprises selecting a data quality rule representing a functional dependency for the database, transforming the data quality rule into at least one rule vector with hashed components, selecting a set of attributes of the database, transforming at least one record of the database selected on the basis of the selected attributes into a record vector with hashed components, computing a dot product of the rule and record vectors to generate a measure representing violation of the data quality rule by the record.

Abstract: A computer implemented method for repairing records of a database, comprises determining a first set of records of the database which violate a functional dependency of the database, determining a second set of records of the database comprising duplicate records, computing a cost metric representing a measure for the cost of mutually dependently modifying records in the first and second sets, modifying records in the first and second sets on the basis of the cost metric to provide a modified database instance.

Abstract: A computer implemented method for repairing records of a database, comprises determining a first set of records of the database which violate a functional dependency of the database, modifying records in the first set to make them consistent with the functional dependency to provide an output consistent database instance, determining a second set of records of the output consistent database instance comprising duplicate records, merging duplicate records in the second set in dependence on the functional dependencies of the records to provide a modified database instance.

Abstract: A computer implemented method for matching data records from multiple entities comprising providing respective transaction logs for the entities representing actions performed by or in respect of the entities, determining a matching score using the transaction logs for respective pairs of the entities and for predetermined combinations of merged entities by generating a measure representing a gain in behavior recognition for the entities before and after merging, and using the gain as a matching score.

Abstract: A computer implemented method for generating a set of updates for a database comprising multiple records including erroneous, missing and inconsistent values, the method comprising using a set of partitioning functions for subdividing the records of the database into multiple subsets of records, allocating respective ones of the records to at least one subset according to a predetermined criteria for mapping records to subsets, applying multiple machine learning models to each of the subsets to determine respective candidate replacement values representing a tuple repair for a record including a probability of candidate and current values for the record, computing probabilities to select replacement values for the record from among the candidate replacement values which maximise the probability for values of the record for an updated database.

Abstract: A computer implemented method for correcting records in a database comprising generating, using a processor, respective candidate replacement entries for multiple inconsistent records of the database, grouping the candidate replacement entries to provide multiple groups of related candidate updates for the database, ranking the groups according to a loss function to quantify database quality, receiving input for a selected group, sorting candidate replacement entries in the selected group, and applying updates from the selected group to the database to correct entries of the inconsistent records.