The method and system for merging information aimed at merging the instances of individuals, a data-processing system performs the following steps: generating the instances of individuals using an ontology which defines, for each property of each instance of an individual, an evolution model to be applied to the property, evolution model representing the evolution of reliability of the property over time in relation to variability of the property over time; preforming the merging of information by comparing, two-by-two, the generated instances of individuals with instances of individuals stored in a knowledge base, performing, for each shared property, a calculation of similarity distance by applying at least evolution model defined for the property, so as to define a coefficient of confidence for each property in order to decide whether or not to merge the instances of individuals; and updating the knowledge base with the instances of individuals resulting from information merging.

A system and method for efficient management of a search database for retrieving context-based information, system including a database and processor, wherein the database includes columnar database for storing a plurality of documents, an ontological database configured to represent a plurality of concepts as nodes in a network and relationships between the concepts as edges between nodes and the search database configured to store an inverted index of the plurality of documents in the columnar database. Herein, the processor is configured to identify, using the ontological database, a set of concepts in each of the plurality of documents and store, in the search database, corresponding to a given document the set of concepts identified in the given document, and secondary concepts relating to the given document, wherein a secondary concept has a direct relationship in the network with at least one of the concepts in the set.

An information retrieval system (IPS). The system comprises an input interface (IN) for receiving a query related to an object of interest. A concept mapper (CM) is configured to map the query to one or more associated concept entries of a hierarchic graph data structure (ONTO). The entries in said structure encode linguistic descriptors of components of a model (GM) for said object (OB). A metric-mapper (MM) is configured to map the query to one or more metric relationship descriptors. A geo-mapper (GEO) is configured to map said concept entries against the geometric model linked to the hierarchic graph data structure to obtain spatio-numerical data associated with said linguistic descriptors. A metric component (MTC) is configured to compute one or more metric or spatial relationships between said object components based on the spatio-numerical data and the one or more metric relationship descriptors.

Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.

A similarity calculation apparatus according to the present invention includes: a name acquisition unit configured to acquire a first group name to which each word belonging to a first synonym group belongs and a second group name to which each word belonging to a second synonym group belongs; a name set generation unit configured to generate a first group name set and a second group name set; and a similarity calculation unit configured to calculate similarity between the first group name set and the second group name set. Therefore, even when a plurality of synonym groups are created, terms can be effectively unified.

A computer system for processing unstructured data, the computer system comprising a computer processor, a computer memory operatively coupled to the computer processor and the computer memory having disposed within it computer program instructions that, when executed by the processor, cause the computer system to carry out the steps of receiving unstructured data input from a client device, analyzing the unstructured data for features that satisfy logical segment criteria by using natural language processing (NLP), and partitioning the unstructured data into logical segments based on satisfaction of the logical segment criteria.

A computer system for processing unstructured data, the computer system comprising a computer processor, a computer memory operatively coupled to the computer processor and the computer memory having disposed within it computer program instructions that, when executed by the processor, cause the computer system to carry out the steps of receiving unstructured data input from a client device, analyzing the unstructured data for features that satisfy logical segment criteria by using natural language processing (NLP), and partitioning the unstructured data into logical segments based on satisfaction of the logical segment criteria.

A selection of content from a content presentation is received. At least one topic from the selected content is extracted using natural language processing (NLP). The at least one topic is representative of a subject conveyed within the selected content. At least one perspective associated with the at least one topic is extracted using NLP. The at least one perspective is representative of a point of view conveyed within the selected content regarding the at least one topic. A topic rating of the extracted topics and associated perspectives is determined based upon the extracted topics and associated perspectives. The topic rating is representative of a topic diversity among the extracted topics and associated perspectives. The topic rating is presented within a graphical user interface (GUI).

A method for matching semantic text data with tags. The method includes: pre-processing multiple semantic text data to obtain original corpus data comprising multiple semantic independent members; determining the degree of association between any two of the multiple semantic independent members according to a reproduction relationship of the multiple semantic independent members in a natural text, determining a theme corresponding to the association according to the degree of association between any two, and thus determining a mapping probability relationship between the multiple semantic text data and the theme; selecting one of the multiple semantic independent members corresponding to the association as a tag of the theme, and mapping the multiple semantic text data to the tag according to the determined mapping probability relationship between the multiple semantic text data and the theme; and taking the determined mapping relationship between the multiple semantic text data and the tag as a supervision material, and matching the unmapped semantic text data with the tag according to the supervision material.

A method for matching semantic text data with tags. The method includes: pre-processing multiple semantic text data to obtain original corpus data comprising multiple semantic independent members; determining the degree of association between any two of the multiple semantic independent members according to a reproduction relationship of the multiple semantic independent members in a natural text, determining a theme corresponding to the association according to the degree of association between any two, and thus determining a mapping probability relationship between the multiple semantic text data and the theme; selecting one of the multiple semantic independent members corresponding to the association as a tag of the theme, and mapping the multiple semantic text data to the tag according to the determined mapping probability relationship between the multiple semantic text data and the theme; and taking the determined mapping relationship between the multiple semantic text data and the tag as a supervision material, and matching the unmapped semantic text data with the tag according to the supervision material.