Abstract: Various videolization solutions are provided. A method for generating video is provided according to an example. The method includes obtaining one or more data portions and generating a customizable video using the one or more data portions and using one or more video generation templates; determining one or more selectable objects in the customizable video using one or more object definitions; receiving an indication indicating a user selection of at least one selectable object corresponding to an object definition; obtaining additional data portions related to the at least one selectable object corresponding to the indication; and regenerating the customizable video using the additional data portions and the one or more data portions corresponding to the indication.

Abstract: A semantic tagging engine automatically generates semantic tags for the given documents and enables semantic search, based on meanings of search terms and content tags. A Semantic Knowledge Management Tool (SKMT) forms a semantic search and knowledge management platform to search, analyze and manage enterprise content. SKMT scans different content sources and generates indexes of semantic keywords. Its interface allows users to manage various data sources, search, explore and visualize search results at semantic level. SKMT provides high precision of semantic search and semantic data visualization.

Abstract: Various videolization solutions are provided. A method for generating video is provided according to an example. The method includes obtaining one or more data portions and generating a customizable video using the one or more data portions and using one or more video generation templates; determining one or more selectable objects in the customizable video using one or more object definitions; receiving an indication indicating a user selection of at least one selectable object corresponding to an object definition; obtaining additional data portions related to the at least one selectable object corresponding to the indication; and regenerating the customizable video using the additional data portions and the one or more data portions corresponding to the indication.

Abstract: An apparatus comprises a data acquisition module configured to extract raw data, and aggregate the raw data to form aggregated data, a data curation module coupled to the data acquisition module and configured to perform curation of the aggregated data to form curated data, and transform the curated data into structured format data, and a transmitter coupled to the data curation module and configured to transmit the structured format data. A method comprises receiving an instruction to perform at least a portion of videolization for a topic, extracting raw data in response to the instruction, aggregating the raw data to form aggregated data, performing curation of the aggregated data to form curated data, transforming the curated data into structured format data, and transmitting the structured format data.

Abstract: A method for building and managing a user-customizable knowledge base, the method comprising acquiring data related to a plurality of entities from a plurality of heterogeneous data sources based on a customized acquisition configuration, wherein the customized acquisition configuration specifies a distinct data wrapper for each of the data sources, extracting entity-related information from the data to form a number of graph databases, and integrating the graph databases by mapping relationships between the entities to create an entity-centric knowledge base.

Abstract: Systems and methods are disclosed that perform automated semantic tagging. Automated semantic tagging produces semantically linked tags for a given text content. Embodiments provide ontology mapping algorithms and concept weighting algorithms that create accurate semantic tags that can be used to improve enterprise content management, and search for better knowledge management and collaboration.

Abstract: A semantic tagging engine automatically generates semantic tags for the given documents and enables semantic search, based on meanings of search terms and content tags. A Semantic Knowledge Management Tool (SKMT) forms a semantic search and knowledge management platform to search, analyze and manage enterprise content. SKMT scans different content sources and generates indexes of semantic keywords. Its interface allows users to manage various data sources, search, explore and visualize search results at semantic level. SKMT provides high precision of semantic search and semantic data visualization.

Abstract: Systems and methods are disclosed that perform automated semantic tagging. Automated semantic tagging produces semantically linked tags for a given text content. Embodiments provide ontology mapping algorithms and concept weighting algorithms that create accurate semantic tags that can be used to improve enterprise content management, and search for better knowledge management and collaboration.