Abstract: The present disclosure relates to a method for a weighting graph comprising nodes representing entities and edges representing relationships between entities in accordance with one or more domains. The method comprises: pre-processing the graph comprising assigning weights to the nodes and/or the edges of the graph in accordance with a specific domain of the domains, wherein the weight indicates a domain specific data quality problem of attribute values representing an edge of the edges and/or an entity involved in that edge. The weighted graph may be provided for enabling a processing of the graph in accordance with the specific domain.

Abstract: Duplicating identification in a graph having a second version. The graph includes nodes representing entities and edges representing relationships between the entities. The method includes: providing a previous first version of the graph. At least two target nodes of the graph may be identified. The first version and the second version of the graph may be compared for determining a comparison score indicative of a similarity between the two target nodes. The comparison score may be used for determining whether the two target nodes are duplicates with respect to each other.

Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a first graph comprising first nodes representing first entities and first edges representing relationships between first entities, the first nodes being associated with first entity attributes descriptive of the first entities represented by the first nodes, the first edges being associated with first edge attributes descriptive of the relationships represented by the first edges; determining a first subgraph for a certain node of the first nodes of the first graph, the first subgraph including the certain node and at least one neighboring node of the certain node; and determining a data quality issue regarding the certain node based, at least in part, on applying one or more applicable rules of a set of data quality rules to first entity attribute values and first edge attribute values of the first subgraph.

Abstract: In an approach, a processor receives a first record from a first data source, where the first record comprises attributes, a second record from a second data source, where the second record comprises said attributes, a first individual quality rating for the attributes of the first record, and a second individual quality rating for the attributes of the second record. A processor, in response to inputting the first record and the second record into a probabilistic matching engine, receives a matching score for each of the respective attributes. A processor calculates a weighted matching score for each of the respective attributes by weighting the matching score for each of the respective attributes with the first individual quality rating and the second individual quality rating. A processor assesses whether the first record and the second record represent the same entity based on the weighted matching score.

Abstract: Embodiments of the present invention provide for a method for duplicate determination in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. Embodiments of the present invention identify two target nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. Embodiments of the present invention can then determine whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs and based on a comparison of the two target nodes of the subgraphs.

Abstract: Embodiments of the present invention provide for a method for duplicate determination in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. Embodiments of the present invention identify two target nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. Embodiments of the present invention can then determine whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs and based on a comparison of the two target nodes of the subgraphs.

Abstract: Embodiments of the present invention determines duplicates in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. The method comprises: identifying at least two nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. The method further comprises determining whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs.

Abstract: A method, computer system, and computer program product for training a machine learning model for use by a task management system are provided. The embodiment may include presenting a task to be resolved to a user via a user interface. The embodiment may also include presenting a further task to be resolved to the user via the user interface. The embodiment may further include predicting time to be spent on the further task presented to the user. The embodiment may also include determining actual time the user spent completing the further task. The embodiment may further include training a machine learning model for a subsequent similar task based on the predicted time and the determined actual time.

Abstract: The present disclosure relates to a method, computer program product and system. The method may comprise providing a first graph being an instance of a first ontology. Sample values of a plurality of concept attributes may be collected from the first graph. The sample values may be clustered into one or more clusters based on content and/or format of the sample values. A cluster of the clusters that contains sample values representing different concept attributes may be identified. An additional concept and associated set of relations representing the concept attribute values of the cluster may be determined and the first ontology may be updated using the additional concept and associated set of relations.

Abstract: Methods, computer program products and/or systems are provided that perform the following operations: obtaining a first graph comprising first nodes representing first entities and first edges representing relationships between first entities, the first nodes being associated with first entity attributes descriptive of the first entities represented by the first nodes, the first edges being associated with first edge attributes descriptive of the relationships represented by the first edges; determining a first subgraph for a certain node of the first nodes of the first graph, the first subgraph including the certain node and at least one neighboring node of the certain node; and determining a data quality issue regarding the certain node based, at least in part, on applying one or more applicable rules of a set of data quality rules to first entity attribute values and first edge attribute values of the first subgraph.

Abstract: In an approach to improving machine learning model training for data matching from manual decisions, one or more computer processors detect a correction made to two data records. One or more computer processors determine a common attribute between the two data records. One or more computer processors identify a first machine learning model associated with the common attribute. One or more computer processors add comparison data of the two data records to training data for the machine learning model, where the comparison data includes the correction.

Abstract: Selecting entity matching systems sample record pairs by selecting at least one first record pair from entity matching system data records, scoring attribute compare methods of the at least one first record pair according to the entity matching system, adding the at least one first record pair to a no-match set according to the attribute matching score, selecting at least one second record pair from an entity matching system data record bucket, scoring attribute compare methods of the at least one second record pair according to the entity matching system, adding the at least one second record pair to record pair set, according to the second record pair attribute compare method score, and providing the record pair set to a user.

Abstract: The present disclosure relates to a method for a weighting graph comprising nodes representing entities and edges representing relationships between entities in accordance with one or more domains. The method comprises: pre-processing the graph comprising assigning weights to the nodes and/or the edges of the graph in accordance with a specific domain of the domains, wherein the weight indicates a domain specific data quality problem of attribute values representing an edge of the edges and/or an entity involved in that edge. The weighted graph may be provided for enabling a processing of the graph in accordance with the specific domain.

Abstract: Embodiments of the present invention determines duplicates in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. The method comprises: identifying at least two nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. The method further comprises determining whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs.