Abstract: Records can be matched by a graph neural network model performing entity resolution on the records, and representing each record as a respective node in a graph. Record matching explanations can be generated, each record matching explanation indicating a first set of attributes, and a first set of corresponding values, used for the matching at least two of the records. Nodes can be clustered into a plurality of clusters by aggregating the record matching explanations and, based on the record matching explanations, determining which of the records have high importance values, in the first set of values, that match. At least one cluster explanation can be generated, the cluster explanation indicating a second set of attributes, and a second set of values corresponding to the second set of attributes, used for the clustering the nodes. The record matching explanation and the cluster explanation can be output.

Abstract: Mechanisms are provided for dynamic re-resolution of entities in a knowledge graph (KG) based on streaming updates. The KG and corresponding initial clusters associated with first entities are received along with a dynamic data stream having second documents referencing second entities. Clustering on the second documents based on the set of initial clusters, and document features of the second documents, is performed to provide a set of second document clusters. For second document clusters that should be modified based on entities associated with the second document cluster, a cluster modification operation is performed. Updated clusters are generated based on the clustering and modification of clusters. Entity re-resolution is dynamically performed on the entities in the KG based on the second entities associated with the updated clusters to generate an updated knowledge graph data structure.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Abstract: Aspects of the present disclosure relate to neural-based ontology generation and refinement. A set of input data can be received. A set of entities can be extracted from the set of input data using a named-entity recognition (NER) process, each entity having a corresponding label, the corresponding labels making up a label set. The label set can be compared to concepts in a set of reference ontologies. Labels that match to concepts in the set of reference ontologies can be selected as a candidate concept set. Relations associated with the candidate concepts within the set of reference ontologies can be identified as a candidate relation set. An ontology can then be generated using the candidate concept set and candidate relation set.

Abstract: Methods, systems, and computer program products for classifying data from de-identified content are provided herein. A computer-implemented method includes applying one or more rules to identify one or more structural elements of a document; determining, based at least in part on the one or more structural elements, one or more pairs of words within the document having a hypernym relationship; extracting de-identified content within the document based on one or more de-identification techniques applied to the document; and applying a set of causal rules to the de-identified content and the one or more pairs of words to annotate at a least a portion of the de-identified content as belonging to a class of protected content.

Abstract: One embodiment provides a computer implemented method, including: obtaining an information document corresponding to an entity, wherein the information document includes redacted information spans; identifying an entity type for each of the redacted information spans, wherein the entity type identifies a relationship between a redacted information span and at least one other entity within the information document; replacing the redacted information spans with replacement entities corresponding to the entity type of a given redacted information span, wherein the replacing is performed in view of a frequency distribution of actual information and wherein the replacing includes maintaining relationships of the redacted information spans; and controlling bias within the replacement entities, wherein the controlling includes detecting bias within the replacement entities.

Abstract: A method, a computer program product, and a system for non-obvious relationship detection. The method includes receiving a knowledge and inputting a first node and a second node from the knowledge graph into a twin neural network. The method also includes embedding the first node and the second node, aggregating neighborhood information and position information into the node embeddings. The method further includes concatenating the neighborhood information and the position information of the first node and the second node to produce a first output vector and a second output vector. The method also includes generating a final score by comparing the first output vector with the second output vector. The final score indicates a probability of a non-obvious relationship between the first node and the second node.

Abstract: Aspects of the present disclosure relate to neural-based ontology generation and refinement. A set of input data can be received. A set of entities can be extracted from the set of input data using a named-entity recognition (NER) process, each entity having a corresponding label, the corresponding labels making up a label set. The label set can be compared to concepts in a set of reference ontologies. Labels that match to concepts in the set of reference ontologies can be selected as a candidate concept set. Relations associated with the candidate concepts within the set of reference ontologies can be identified as a candidate relation set. An ontology can then be generated using the candidate concept set and candidate relation set.

Abstract: A computer-implemented process is disclosed for dynamically routing data objects to storage locations. The process includes assigning a unique identifier value and a timestamp value to an incoming data object. The process further includes determining a bucket for the data object based on the unique identifier value. The process further includes determining a storage location for the data object from among a plurality of storage locations, wherein the determination is based on the bucket and the timestamp value. The process further includes routing the data object to the storage location for storage therein.

Abstract: A computer-implemented process is disclosed for dynamically routing data objects to storage locations. The process includes assigning a unique identifier value and a timestamp value to an incoming data object. The process further includes determining a bucket for the data object based on the unique identifier value. The process further includes determining a storage location for the data object from among a plurality of storage locations, wherein the determination is based on the bucket and the timestamp value. The process further includes routing the data object to the storage location for storage therein.

Abstract: A flexible architecture component for providing data integration and exchange between a plurality of client applications is disclosed. The client applications are coupled to a network and access respective data sources, wherein the data sources of each of the client applications may be stored in different formats and are not directly accessible by the other client applications. Aspects of the present invention include providing an adapter API that provides a first set of methods for the client applications to use to translate data into XML. Each of the client applications is then modified to invoke the methods in the adapter API to convert data in their respective data sources into XML format and to have the XML formatted data imported into a database on a server, thereby standardizing the data from the data sources.

Abstract: A method and apparatus are provided for generating and using timing constraints templates for IP cores that can be instantiated in an integrated circuit design. The templates include a plurality of timing constraint statements for inputs and outputs of the respective IP core. At least one of the statements includes a configurable variable, wherein the timing constraints template is configurable through the variable for each of a plurality of instances of the IP core in the integrated circuit design.

Abstract: A method and apparatus are provided for generating and using timing constraints templates for IP cores that can be instantiated in an integrated circuit design. The templates include a plurality of timing constraint statements for inputs and outputs of the respective IP core. At least one of the statements includes a configurable variable, wherein the timing constraints template is configurable through the variable for each of a plurality of instances of the IP core in the integrated circuit design.

Abstract: A method and system for dynamically generating database queries is disclosed. The method and system include storing web interface data, including query attributes for a database, in one more tables. The attributes are then retrieved from the tables and displayed in a graphical user interface web page for user selection. Based on the attributes selected by the user, a SQL query is dynamically generated. The method and system further include displaying results of the SQL query to the user in graphical format, thereby enabling dynamic generation of custom queries.

Abstract: A flexible architecture component for providing data integration and exchange between a plurality of client applications is disclosed. The client applications are coupled to a network and access respective data sources, wherein the data sources of each of the client applications may be stored in different formats and are not directly accessible by the other client applications. Aspects of the present invention include providing an adapter API that provides a first set of methods for the client applications to use to translate data into XML. Each of the client applications is then modified to invoke the methods in the adapter API to convert data in their respective data sources into XML format and to have the XML formatted data imported into a database on a server, thereby standardizing the data from the data sources.