Abstract: A portable system for converting wind energy into electrical energy is disclosed. The disclosed system may include a frame hosting one or more conversion modules, arranged as desired. A given conversion module may include one or more wind energy conversion devices (WECDs), arranged as desired. The conversion modules may be electrically connected, directly or indirectly, with one or more downstream electrical energy storage elements (e.g., such as a battery or other capacitive element, optionally native to a host platform). In this manner, the disclosed system may be configured for use in storing and/or supplying electric power for downstream consumption by a host platform or otherwise. In a more general sense, the disclosed system may be utilized, for example, for micro-generation of renewable electrical energy from wind.

Abstract: A question answering (QA) system comprising memory for storing instructions, and a processor configured to execute the instructions to ingest source documents that include structured data and unstructured data to create a knowledge base, wherein the unstructured data includes table data; create table annotations to represent the table data; store the ingested structured data, unstructured data, and the table annotations in the knowledge base; and determine answers to questions using the knowledge base.

Abstract: A portable system for converting wind energy into electrical energy is disclosed. The disclosed system may include a frame hosting one or more conversion modules, arranged as desired. A given conversion module may include one or more wind energy conversion devices (WECDs), arranged as desired. The conversion modules may be electrically connected, directly or indirectly, with one or more downstream electrical energy storage elements (e.g., such as a battery or other capacitive element, optionally native to a host platform). In this manner, the disclosed system may be configured for use in storing and/or supplying electric power for downstream consumption by a host platform or otherwise. In a more general sense, the disclosed system may be utilized, for example, for micro-generation of renewable electrical energy from wind.

Abstract: A portable system for converting wind energy into electrical energy is disclosed. The disclosed system may include a frame hosting one or more conversion modules, arranged as desired. A given conversion module may include one or more wind energy conversion devices (WECDs), arranged as desired. The conversion modules may be electrically connected, directly or indirectly, with one or more downstream electrical energy storage elements (e.g., such as a battery or other capacitive element, optionally native to a host platform). In this manner, the disclosed system may be configured for use in storing and/or supplying electric power for downstream consumption by a host platform or otherwise. In a more general sense, the disclosed system may be utilized, for example, for micro-generation of renewable electrical energy from wind.

Abstract: A system for question answering (QA) documents data ingestion decides to ingest the documents data through a first plurality of sub-pipelines including a first sub-pipeline having a first set of engines and a second sub-pipeline having a second set of engines being independent from the first set of engines. The system determines a subset of the documents data and decides to ingest the subset through a second plurality of sub-pipelines including a third sub-pipeline having a third set of engines and a fourth sub-pipeline having a fourth set of engines being independent from the third set of engines. A set of engines of the second plurality of sub-pipelines and a set of engines of the first plurality of sub-pipelines are in a common class. The system selects output data from the second plurality of sub-pipelines over corresponding output data from the first plurality of sub-pipelines and generates a knowledge base.

Abstract: A portable system for converting wind energy into electrical energy is disclosed. The disclosed system may include a frame hosting one or more conversion modules, arranged as desired. A given conversion module may include one or more wind energy conversion devices (WECDs), arranged as desired. The conversion modules may be electrically connected, directly or indirectly, with one or more downstream electrical energy storage elements (e.g., such as a battery or other capacitive element, optionally native to a host platform). In this manner, the disclosed system may be configured for use in storing and/or supplying electric power for downstream consumption by a host platform or otherwise. In a more general sense, the disclosed system may be utilized, for example, for micro-generation of renewable electrical energy from wind.

Abstract: A system includes a memory having instructions stored therein. The system also includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to receive an input question in a source language, generate a first similarity score based at least in part on a source-language operand, generate a target-language operand based at least in part on the source-language operand, generate a second similarity score based at least in part on the target-language operand, generate a semantic concept score based at least in part on the first similarity score and the second similarity score, generate a set of ranked answers to the input question (“ranked answer set”) based at least in part on the semantic concept score, and output the ranked answer set.

Abstract: A system for handling a graphical representation of data associated with a question answering (QA) input document includes a memory having instructions therein and includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to derive, at least from a portion of the QA input document, first metadata regarding a context of the graphical representation of data. The at least one processor is also configured to execute the instructions to derive, at least from a portion of the graphical representation of data, tabular data. The at least one processor is also configured to execute the instructions to determine, at least in part by comparing at least a portion of the first metadata to existing table annotations from a QA knowledge base, how to incorporate the tabular data into the QA knowledge base.

Abstract: A method trains an artificial intelligence system to respond to a complex query. A query/answer (Q/A) system sends a first query to an information server, where the first query is a query about a first topic with regard to a first entity, and where the information server provides access to unstructured information sources. In response to receiving a negative response to the first query for the first entity, the Q/A system sends variations of the first query and a second query about a second topic to the information server. The Q/A system identifies a pattern in the table of information in order to create the answer to the first query for the first entity. The Q/A system then trains an artificial intelligence system to respond to a complex query about the first entity with information in the table of information.

Abstract: A method generates a complex response to a question in a question answering system. A core knowledge database stores essential annotations associated with term spans from an original text document. One or more auxiliary knowledge databases store non-essential annotations for the term spans from the original text document. One or more processors establish links among the core knowledge database and the one or more auxiliary knowledge databases by using parallel fields for corresponding term spans, where the parallel fields provide positional term span location information for correlating the essential annotations to the non-essential annotations. The processor(s) receive a question from a question answering system, and then, using the correlated essential annotations and non-essential annotations, fetch term spans that answer the question in a complex response, generate the complex response by using the fetched term spans, and then return the complex response to the question answering system.

Abstract: A system includes a memory having instructions stored therein. The system also includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to receive an input question in a source language, generate a first similarity score based at least in part on a source-language operand, generate a target-language operand based at least in part on the source-language operand, generate a second similarity score based at least in part on the target-language operand, generate a semantic concept score based at least in part on the first similarity score and the second similarity score, generate a set of ranked answers to the input question (“ranked answer set”) based at least in part on the semantic concept score, and output the ranked answer set.

Abstract: A method of augmenting a knowledge graph includes obtaining the knowledge graph, which includes entities and relationships between the entities defining respective edges, clustering the entities into knowledge domains using semantic distances determined between the entities and a threshold on the semantic distances, identifying strengths of the relationships between adjacent entities in the knowledge graph, creating knowledge chains from node pairs in the knowledge graph, including generating a minimum spanning tree using the strengths of the relationships, pruning edges from the knowledge chain using a threshold on weights corresponding to the edges, defining a first knowledge index for each of the knowledge chains, defining a second knowledge index for each of the knowledge domains, and defining a third knowledge index for the knowledge graph as a harmonic mean of a sum of the first knowledge indexes and a sum of the second knowledge indexes.

Abstract: A system for question answering (QA) documents data ingestion decides to ingest the documents data through a first plurality of sub-pipelines including a first sub-pipeline having a first set of engines and a second sub-pipeline having a second set of engines being independent from the first set of engines. The system determines a subset of the documents data and decides to ingest the subset through a second plurality of sub-pipelines including a third sub-pipeline having a third set of engines and a fourth sub-pipeline having a fourth set of engines being independent from the third set of engines. A set of engines of the second plurality of sub-pipelines and a set of engines of the first plurality of sub-pipelines are in a common class. The system selects output data from the second plurality of sub-pipelines over corresponding output data from the first plurality of sub-pipelines and generates a knowledge base.

Abstract: A computer-implemented method configured to train a system to be able to provide answers to questions that do not have a direct relationship established in a database.

Abstract: A system for handling a graphical representation of data associated with a question answering (QA) input document includes a memory having instructions therein and includes at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to derive, at least from a portion of the QA input document, first metadata regarding a context of the graphical representation of data. The at least one processor is also configured to execute the instructions to derive, at least from a portion of the graphical representation of data, tabular data. The at least one processor is also configured to execute the instructions to determine, at least in part by comparing at least a portion of the first metadata to existing table annotations from a QA knowledge base, how to incorporate the tabular data into the QA knowledge base.

Abstract: A method trains an artificial intelligence system to respond to a complex query. A query/answer (Q/A) system sends a first query to an information server, where the first query is a query about a first topic with regard to a first entity, and where the information server provides access to unstructured information sources. In response to receiving a negative response to the first query for the first entity, the Q/A system sends variations of the first query and a second query about a second topic to the information server. The Q/A system identifies a pattern in the table of information in order to create the answer to the first query for the first entity. The Q/A system then trains an artificial intelligence system to respond to a complex query about the first entity with information in the table of information.

Abstract: A question answering (QA) system comprising memory for storing instructions, and a processor configured to execute the instructions to ingest source documents that include structured data and unstructured data to create a knowledge base, wherein the unstructured data includes table data; create table annotations to represent the table data; store the ingested structured data, unstructured data, and the table annotations in the knowledge base; and determine answers to questions using the knowledge base.

Abstract: A method of augmenting a knowledge graph includes obtaining the knowledge graph, which includes entities and relationships between the entities defining respective edges, clustering the entities into knowledge domains using semantic distances determined between the entities and a threshold on the semantic distances, identifying strengths of the relationships between adjacent entities in the knowledge graph, creating knowledge chains from node pairs in the knowledge graph, including generating a minimum spanning tree using the strengths of the relationships, pruning edges from the knowledge chain using a threshold on weights corresponding to the edges, defining a first knowledge index for each of the knowledge chains, defining a second knowledge index for each of the knowledge domains, and defining a third knowledge index for the knowledge graph as a harmonic mean of a sum of the first knowledge indexes and a sum of the second knowledge indexes.

Abstract: An approach is provided in which a knowledge manager generates a knowledge structure that includes security annotation tokens and term tokens. Each of the security annotation tokens are stored in a parallel field and align to at least one of the term tokens. The knowledge manager matches security policies corresponding to a search request to one or more of the security annotation tokens and, in turn, generates search results based upon obfuscation of one or more of the term tokens aligned to the matched security annotation tokens.

Abstract: A system and computer program product configured to train a system to be able to provide answers to questions that do not have a direct relationship established in a database.