Abstract: A computer-implemented method for generating a group of representative model cases for a trained machine learning model may be provided. The method comprising determining an input space, determining an initial plurality of model cases, and expanding the initial plurality of model cases by stepwise modifying field values of the records representing the initial plurality of model cases resulting in an exploration set of model cases. Additionally, the method comprises obtaining a model score value for each record of the exploration set of model cases, continuing the expansion of the exploration set of model cases thereby generating a refined model case set, and selecting the records in the refined model case set based on relative record distance values and related model score values between pairs of records, thereby generating the group of representative model cases.

Abstract: The disclosure relates to a method for screening a subject for the risk of chronic kidney disease (CKD), comprising: receiving marker data indicative for a plurality of marker parameters for a subject, such plurality of marker parameters indicating, for the subject for a measurement period, an age value, a sample level of creatinine, and a sample level of albumin; and determining a risk factor indicative of the risk of suffering CKD for the subject from the plurality of marker parameters, wherein the determining comprises: weighting the age value higher than the sample level of albumin, and weighting the sample level of creatinine higher than the sample level of albumin. Further, a computer-implemented method for screening a subject and a method for screening a subject for the risk of chronic kidney disease (CKD) are provided.

Abstract: A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.

Abstract: A method, computer system, and computer program product for eliminating a redundant node from a knowledge graph is provided. A structural analysis of a knowledge graph is performed by determining that two nodes have a similar structure. An empirical analysis is performed by determining a search result correlation of potentially redundant nodes, said search result correlation comprising a correlation of search result nodes generated from different search queries to said knowledge graph or a correlation of search results due to selected search result nodes in subtrees of said potentially redundant nodes. Results of said structural analysis and said empirical analysis are combined to generate a redundancy confidence level value for two said nodes. One of said two nodes is determined as redundant. One of said two redundant nodes is removed from the knowledge graph.

Abstract: A system for enhancing a classifier prediction in respect to underrepresented classes may be provided. A classifier system trained with training data to build a model is used for classifying unknown input data, and an evaluator engine adapted for a determination of an underrepresented class. Additionally, the system comprises an extractor engine adapted for an extraction of relating data from an additional source, and a similarity engine adapted for a selection of data sets out of the relating data wherein the similarity engine is also adapted for comparing features of the relating data and a representative data set for the underrepresented class. Finally, the system comprises a recursion unit adapted for triggering the evaluator engine, the extractor engine and the similarity engine treating selected data set as input data until the evaluator engine classifies the selected data set with a confidence level which is above a confidence threshold level.

Abstract: A computer-implemented method for improving fairness in a supervised machine-learning model may be provided. The method comprises linking the supervised machine-learning model to a reinforcement learning meta model, selecting a list of hyper-parameters and parameters of the supervised machine-learning model, and controlling at least one aspect of the supervised machine-learning model by adjusting hyper-parameters values and parameter values of the list of hyper-parameters and parameters of the supervised machine-learning model by a reinforcement learning engine relating to the reinforcement learning meta model by calculating a reward function based on multiple conflicting objective functions. The method further comprises repeating iteratively the steps of selecting and controlling for improving a fairness value of the supervised machine-learning model.

Abstract: A method for linking a first knowledge graph (KG) and a second KG in the presence of a third KG is provided. Content of nodes of the first KG is compared to nodes of the second KG. If a first KG node has a content relationship to a related second KG node, an edge identified by a tuple identifying the first KG and the first KG node and a tuple identifying the second KG and the second KG node is stored in a meta-layer KG. The method comprises comparing content of the nodes from the third KG with the content of nodes from the first and second KG, and in case relationships are identified, more complex tuples establishing this relationship in the meta-layer are stored. Finally, the method also comprises storing at least all nodes and edges of the meta-layer knowledge graph.

Abstract: A computer program product, system, and method for building a knowledge graph may include receiving a plurality of new nodes, receiving a base knowledge graph having existing nodes selectively connected by existing edges, and superimposing the new nodes onto selected ones of the existing nodes of the base knowledge graph. The method may further include connecting the new nodes by creating a new edge with a new weight between at least two of the new nodes if corresponding existing nodes in the underlying base knowledge graph have a connection via zero or a predetermined maximum number of existing edges, wherein the new weight is determined based on the existing weights of the existing edges of connections between the corresponding existing nodes, and detaching the new nodes with the new edges from the base knowledge graph.

Abstract: A method and system for deleting a node of a knowledge graph may be provided. The method comprises receiving knowledge graph data comprising nodes and edges, determining a first relevance degree value for a node and a second relevance degree value for the node, combining the first and the second relevance degree value and marking the node as deletable if the final relevance degree value is above a predefined relevance threshold value. The method comprises further applying queries against the knowledge graph, wherein at least a certain number of such queries invokes accessing of a node, marked as deletable, revising at least one of the first and second relevance degree value based on the accessing of the node marked as deletable, and deleting the node, marked as deletable, if over a predetermined period of time queries against the knowledge graph invoke no access of the node marked as deletable.

Abstract: A knowledge graph is divided into a plurality of sub-graphs, each sub-graph comprising a plurality of vertices and a plurality of edges. The knowledge graph is represented as a summary graph comprising for each of the sub-graphs a summary-graph vertex. A local sub-graph is generated as a copy of one of the sub-graphs together with a copy of a surrounding graph to the one of the sub-graphs. The content of the local sub-graph is modified. The local sub-graph is reintegrated, upon a reintegration trigger event, back into the knowledge graph, wherein a structure of the surrounding graph is used as a reintegration aid, by overlaying the structure and the knowledge graph, thereby identifying identical vertices of the surrounding structure and the knowledge graph as anchor points from where changes in the local sub-graph are reintegrated into the knowledge graph.

Abstract: A computer-implemented method, system, and computer program product for dynamic access control to a node in a knowledge graph includes: structuring nodes of a knowledge graph into a plurality of hierarchically organized graph layers; assigning, to one or more users, an access right to a first node of the knowledge graph, the access right to the node selected from a plurality of access rights, where different types of users have different access rights; and assigning, to at least one user from the one or more users, an additional access right to a second node of the knowledge graph.

Abstract: A knowledge graph is divided into a plurality of sub-graphs, each sub-graph comprising a plurality of vertices and a plurality of edges. The knowledge graph is represented as a summary graph comprising for each of the sub-graphs a summary-graph vertex. A local sub-graph is generated as a copy of one of the sub-graphs together with a copy of a surrounding graph to the one of the sub-graphs. The content of the local sub-graph is modified. The local sub-graph is reintegrated, upon a reintegration trigger event, back into the knowledge graph, wherein a structure of the surrounding graph is used as a reintegration aid, by overlaying the structure and the knowledge graph, thereby identifying identical vertices of the surrounding structure and the knowledge graph as anchor points from where changes in the local sub-graph are reintegrated into the knowledge graph.

Abstract: A method for partitioning a knowledge graph is provided. The method analyzes past searches and determines an access frequency of a plurality of edges. The method marks, as intermediate cluster cores, edges having the highest access frequencies, sorts the marked intermediate cluster cores according to their access frequencies, and selects a first cluster core having the highest access frequency. The method assigns first edges in a first radius around the first cluster core to build the first cluster. The method selects a second cluster core having the highest access frequency apart from edges of the first cluster, and assigns second edges in a second radius around second cluster core to build the second cluster. The method partitions the knowledge graph into a first sub-knowledge-graph comprising the first cluster and a second sub-knowledge-graph comprising the second cluster.

Abstract: A computer-implemented method, system, and computer program product for dynamic access control to a node in a knowledge graph includes: structuring nodes of a knowledge graph into a plurality of hierarchically organized graph layers; assigning, to a user, an access right to a node of the knowledge graph, the access right to the node selected from a plurality of access rights; and changing the access right to the node dynamically, the changing based on at least one of a structure of the knowledge graph, an access history of the user to the node, and a parameter of the user indicative of a condition outside the knowledge graph.

Abstract: A computer-implemented method includes associating, using a processor, one or more words in an electronic agenda template to at least one agenda item indicative of a point for discussion. The processor captures a real-time interaction comprising speech from one or more participants of a plurality of discussion participants into a digital representation. The processor isolates a portion of the real-time interaction from the digital representation. The portion is associated with a single speaker of the plurality of discussion participants. The processor makes at least one match between an isolated portion of the real-time interaction and the at least one agenda item. The processor determines an intent of the single speaker from the isolated portion and matching the determined intent of the single speaker to the at least one agenda item on the electronic agenda template, and generates discussion minutes output based on the matched intent and agenda item.

Abstract: A knowledge graph is divided into a plurality of sub-graphs, each sub-graph comprising a plurality of vertices and a plurality of edges. The knowledge graph is represented as a summary graph comprising for each of the sub-graphs a summary-graph vertex. A local sub-graph is generated as a copy of one of the sub-graphs together with a copy of a surrounding graph to the one of the sub-graphs. The content of the local sub-graph is modified. The local sub-graph is reintegrated, upon a reintegration trigger event, back into the knowledge graph, wherein a structure of the surrounding graph is used as a reintegration aid, by overlaying the structure and the knowledge graph, thereby identifying identical vertices of the surrounding structure and the knowledge graph as anchor points from where changes in the local sub-graph are reintegrated into the knowledge graph.

Abstract: A method and a related system for controlling user access to a target node in a knowledge graph may be provided. The method comprises defining a knowledge graph structure limitation for a user, defining a node type depending on the number of edges connecting to the node, determining a condition for an access to the target node, based on the knowledge graph structure limitation relative to the start node and the node type of the target node, upon the user attempting, coming from a start node, to access the target node in the knowledge graph, and granting access to the target node based on the determination.

Abstract: A computer-implemented method, system, and computer program product for dynamic access control to a node in a knowledge graph includes: structuring nodes of a knowledge graph into a plurality of hierarchically organized graph layers; assigning, to a user, an access right to a node of the knowledge graph, the access right to the node selected from a plurality of access rights; and changing the access right to the node dynamically, the changing based on at least one of a structure of the knowledge graph, an access history of the user to the node, and a parameter of the user indicative of a condition outside the knowledge graph.

Abstract: A method, computer system, and computer program product for eliminating a redundant node from a knowledge graph is provided. A structural analysis of a knowledge graph is performed by determining that two nodes have a similar structure. An empirical analysis is performed by determining a search result correlation of potentially redundant nodes, said search result correlation comprising a correlation of search result nodes generated from different search queries to said knowledge graph or a correlation of search results due to selected search result nodes in subtrees of said potentially redundant nodes. Results of said structural analysis and said empirical analysis are combined to generate a redundancy confidence level value for two said nodes. One of said two nodes is determined as redundant. One of said two redundant nodes is removed from the knowledge graph.

Abstract: A system and machine-implemented method for sorting Next-Generation Sequencing (NGS) reads in O(n) time and space complexity that makes use low sparsity and nearly uniform distribution of the input array. The genome position field in the input array is used to determine the target position of the output array. Duplicate target positions due to n-fold coverage are handled by assigning either overflow buckets to each position or anterior assigning multiple target slots in the output array for each genome position depending on the distribution of reads over the genome and the resulting probability of hitting an already occupied slot. Once every tuple in the input array has been written to the output array, the output array in read through ascending order and each tuple is appended to the end of a final result array.