Abstract: A method for creating a database for a domain specific application includes providing a centralized data repository comprising data from different sources, identifying a set of data units of the repository that represent a specific domain, determining a pivotal entity type for an application, determining a mapping between different identifiers of the pivotal entity type, creating a reference set by selecting a first subset of the set of data units using the mapping, wherein the first subset represents the pivotal entity type, selecting, based at least in part on the mapping, a second subset of the set of data units, wherein the second subset represents non-pivotal entity types which are related to instances of the pivotal entity type in the reference set; and creating a database from data units and associated attributes selected from the reference set of data units and the second subset of data units.

Abstract: A computing system, program products and computer-implemented method of exploring data comprises accessing data structures and executing an interaction loop. Data structure captures connected graph nodes associated with attributes containing human-readable data. Interaction loop receives user-selected attributes associated with focal nodes and executes subroutines to determine layouts of an arborescence extending from focal nodes. The subroutines use starting nodes and pivot attributes as arguments. Identifying nodes of subroutines are connected to starting nodes by walking the graph and comparing pivot attributes with attributes associated with connected nodes, obtaining distance-dependent quantities. Subroutines compute layout data, including coordinates of the connected nodes. Coordinates are determined in accordance with the distance-dependent quantities. Interaction loop displays arborescence according to the layout data.

Abstract: A method for creating a database for a domain specific application includes providing a centralized data repository comprising data from different sources, identifying a set of data units of the repository that represent a specific domain, determining a pivotal entity type for an application, determining a mapping between different identifiers of the pivotal entity type, creating a reference set by selecting a first subset of the set of data units using the mapping, wherein the first subset represents the pivotal entity type, selecting, based at least in part on the mapping, a second subset of the set of data units, wherein the second subset represents non-pivotal entity types which are related to instances of the pivotal entity type in the reference set; and creating a database from data units and associated attributes selected from the reference set of data units and the second subset of data units.

Abstract: A tool for managing text-item recognition systems such as NER (Named Entity Recognition) systems. The tool applies the system to a text corpus containing instances of text items, such as named entities, to be recognized by the system, and selecting from the text corpus a set of instances of text items which the system recognized. The tool tokenizes the text corpus such that each instance in the aforementioned set is encoded as a single token and processing the tokenized text via a word embedding scheme to generate a word embedding matrix. The tool, responsive to selecting a seed token corresponding to an instance in the aforementioned set, performs a nearest-neighbor search of the embedding space to identify a set of neighboring tokens for the seed token, and identifies the text corresponding to each neighboring token as a potential instance of a text item to be annotated.

Abstract: A tool for managing text-item recognition systems such as NER (Named Entity Recognition) systems. The tool applies the system to a text corpus containing instances of text items, such as named entities, to be recognized by the system, and selecting from the text corpus a set of instances of text items which the system recognized. The tool tokenizes the text corpus such that each instance in the aforementioned set is encoded as a single token and processing the tokenized text via a word embedding scheme to generate a word embedding matrix. The tool, responsive to selecting a seed token corresponding to an instance in the aforementioned set, performs a nearest-neighbor search of the embedding space to identify a set of neighboring tokens for the seed token, and identifies the text corresponding to each neighboring token as a potential instance of a text item to be annotated.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system. The exemplary embodiments may include receiving a data analysis request, using a knowledge graph for determining a source dataset based on the received data analysis request, wherein the knowledge graph represents an extract, transform and load (ETL) based ontology, wherein the knowledge graph comprises nodes representing entities and edges representing relationships between the entities, and wherein the entities are instances of concepts of the ETL based ontology, building an ETL workflow for processing the source dataset in accordance with a data lineage associated with the source dataset in the knowledge graph, and executing the ETL workflow.

Abstract: Computer-implemented methods and systems are provided for generating training datasets for bootstrapping text classifiers. Such a method includes providing a word embedding matrix. This matrix is generated from a text corpus by encoding words in the text as respective tokens such that selected compound keywords in the text are encoded as single tokens. The method includes receiving, via a user interface, a user-selected set of the keywords a nearest neighbor search of the embedding space is performed for each keyword in the set to identify neighboring keywords, and a plurality of the neighboring keywords are added to the keyword-set. The method further comprises, for a corpus of documents, string-matching keywords in the keyword-sets to text in each document to identify, based on results of the string-matching, documents associated with each text class. The documents identified for each text class are stored as the training dataset for the classifier.

Abstract: The exemplary embodiments disclose a system and method, a computer program product, and a computer system. The exemplary embodiments may include receiving a data analysis request, using a knowledge graph for determining a source dataset based on the received data analysis request, wherein the knowledge graph represents an extract, transform and load (ETL) based ontology, wherein the knowledge graph comprises nodes representing entities and edges representing relationships between the entities, and wherein the entities are instances of concepts of the ETL based ontology, building an ETL workflow for processing the source dataset in accordance with a data lineage associated with the source dataset in the knowledge graph, and executing the ETL workflow.

Abstract: Real-time rendering representations of objects in a view. Objects include clusters of nodes of a graph structure and links between said clusters. The graph structure comprises edges defined as pairs of the nodes. User inputs in respect to a current view and/or at least one of the objects is received. A data structure is updated, which associates multiple identifiers. Multiple identifiers may include cluster identifiers, node identifiers, and link identifiers, which respectively identify clusters of nodes, nodes of said graph, and links between pairs of clusters.

Abstract: Real-time rendering representations of objects in a view. Objects include clusters of nodes of a graph structure and links between said clusters. The graph structure comprises edges defined as pairs of the nodes. User inputs in respect to a current view and/or at least one of the objects is received. A data structure is updated, which associates multiple identifiers. Multiple identifiers may include cluster identifiers, node identifiers, and link identifiers, which respectively identify clusters of nodes, nodes of said graph, and links between pairs of clusters.

Abstract: A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

Abstract: A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

Abstract: A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

Abstract: A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, eij, ekl(i)) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K×M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.

Abstract: A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, eij, ekl(i)) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K×M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.

Abstract: A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, eij, ekl(i)) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K×M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.

Abstract: A method for guided keyword-based exploration of data stored in a database includes providing, with a processing device, valid query templates for the data, wherein the provided valid query templates include static parts and dynamic parts; selecting those of the provided valid query templates that match a user-provided keyword; generating valid queries from the selected valid query templates using the data; and querying the data using a user-selected valid query selected from the generated valid queries.

Abstract: Exemplary embodiments include a method for re-ordering and visualizing a matrix in the presence of a data hierarchy stored on a computer system, the method including receiving a matrix, in the computer system, the matrix having rows and columns, reordering the matrix so that groups of related rows and columns are brought in adjacent matrix positions, wherein reordering the matrix A obeys constraints imposed by the data hierarchy on at least one of the rows and the columns and reordering the hierarchy on at least one of the rows and columns, wherein affinities in the data hierarchy are extracted by the reordering of the data hierarchy.

Abstract: A computer-implemented method for identifying relationships between entities includes accessing a first data structure being a two-dimensional array of scalar elements (e, eij, ekl(i)) representable as a matrix, each of the scalar elements capturing a relationship between two entities; reorganizing the first data structure by clustering the scalar elements separately on each dimension of the two-dimensional array, to obtain a second data structure, representable as a K×M block matrix, wherein each block is a reordered sequence of rows and/or columns of the first data structure; compacting the second data structure by: determining two parallel block sequences, which are the most similar according to a given distance measure, the parallel block sequences being either distinct rows or distinct columns of blocks of the second data structure; and reorganizing the second data structure by merging the two determined sequences into a single block sequence.

Abstract: A method, system and computer product for visualizing affinities between objects. The method includes the steps of: forming a representation of a minimum spanning tree where the minimum spanning tree connects the plurality of objects based on a pairwise distance between the plurality of objects; forming a hierarchical cluster of the plurality of objects where the hierarchical cluster includes a level; agglomerating the plurality of objects based on the pairwise distance; displaying a view of the representation of the minimum spanning tree in a graphical user interface; receiving a user selection of a parameter containing a hierarchical level; and identifying, in the view, a target cluster that corresponds to the hierarchical level; where at least one of the steps is carried out using a computer device so that affinities between the plurality of objects are visualized.