Abstract: Provided is a method including obtaining a set of ontologies mapping n-grams onto concepts to which the n-grams refer in different respective domains of knowledge. The method includes receiving an update associating a first n-gram with a first concept and receiving information by which the update is associated with a given domain of knowledge. The method includes selecting a subset of ontologies by determining that the update in the given domain of knowledge is applicable to respective domains of knowledge of the subset of ontologies and that the first concept has a specified type of relationship to a subset of concepts to which other n-grams are mapped in the subset of ontologies. The method also includes storing, in response to the determination, associations between the first n-gram and the subset of concepts in at least some of the subset of ontologies in memory of the computer system.

Abstract: Provided is a method including obtaining a set of ontologies mapping n-grams onto concepts to which the n-grams refer in different respective domains of knowledge. The method includes receiving an update associating a first n-gram with a first concept and receiving information by which the update is associated with a given domain of knowledge. The method includes selecting a subset of ontologies by determining that the update in the given domain of knowledge is applicable to respective domains of knowledge of the subset of ontologies and that the first concept has a specified type of relationship to a subset of concepts to which other n-grams are mapped in the subset of ontologies. The method also includes storing, in response to the determination, associations between the first n-gram and the subset of concepts in at least some of the subset of ontologies in memory of the computer system.

Abstract: Provided is a computer-implemented process including obtaining a corpus of natural-language text documents, automatically generating questions about information in corresponding portions of the documents, and associating the questions with the corresponding portions of the documents. The process further includes storing the questions and the associations with the corresponding portions of the documents in memory to form an index of automatically-generated questions to corresponding portions of documents that answer the questions.

Abstract: Provided is a process including obtaining a set of natural-language text documents that discuss a topic, the set of documents containing different states of knowledge about the topic at different times. The process includes selecting an ontology from among a plurality of ontologies that correspond to different domains of knowledge, the selection being based on the ontology corresponding to a domain of knowledge including the topic. The process includes identifying concepts discussed in the documents using the ontology and detecting changes in at least some of the concepts over time based on differences between discussion of the concepts in documents authored at different times. The process includes updating natural language instructions on the topic based on the detected changes in the concepts and storing the updated natural language instructions in memory.

Abstract: Provided is a method including obtaining a set of ontologies mapping n-grams onto concepts to which the n-grams refer in different respective domains of knowledge. The method includes receiving an update associating a first n-gram with a first concept and receiving information by which the update is associated with a given domain of knowledge. The method includes selecting a subset of ontologies by determining that the update in the given domain of knowledge is applicable to respective domains of knowledge of the subset of ontologies and that the first concept has a specified type of relationship to a subset of concepts to which other n-grams are mapped in the subset of ontologies. The method also includes storing, in response to the determination, associations between the first n-gram and the subset of concepts in at least some of the subset of ontologies in memory of the computer system.

Abstract: Provided is a computer-implemented process including obtaining a corpus of natural-language text documents, automatically generating questions about information in corresponding portions of the documents, and associating the questions with the corresponding portions of the documents. The process further includes storing the questions and the associations with the corresponding portions of the documents in memory to form an index of automatically-generated questions to corresponding portions of documents that answer the questions.

Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

Abstract: A process including obtaining a set of natural-language text documents that discuss a topic, the set of documents containing different states of knowledge about the topic at different times. The process includes selecting an ontology from among a plurality of ontologies that correspond to different domains of knowledge, the selection being based on the ontology corresponding to a domain of knowledge including the topic. The process includes identifying concepts discussed in the documents using the ontology and detecting changes in at least some of the concepts over time based on differences between discussion of the concepts in documents authored at different times. The process includes updating natural language instructions on the topic based on the detected changes in the concepts and storing the updated natural language instructions in memory.

Abstract: A method includes obtaining parameters and a document, determining a domain based on the parameters, where the domain maps to a first ontology, and where ontologies map n-grams onto a set of concepts. The method includes scoring a first set of n-grams of the document using a scoring model based on relations between members of the first set of n-grams, selecting sections of the text based on n-gram scores provided by the scoring model, and determining an initial n-gram set, where each respective n-gram of the initial n-gram set maps to a respective concept of the set of concepts, and where each respective n-gram is identified by an ontology other than the first ontology. The method includes determining related n-grams mapped to the set of concepts associated with the domain and generating a text summary for the document based on the sections and the related n-grams.

Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

Abstract: Provided is a computer-implemented process including obtaining a corpus of natural-language text documents, automatically generating questions about information in corresponding portions of the documents, and associating the questions with the corresponding portions of the documents. The process further includes storing the questions and the associations with the corresponding portions of the documents in memory to form an index of automatically-generated questions to corresponding portions of documents that answer the questions.

Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

Abstract: Provided is a method including obtaining parameters and a document, determining a domain based on the parameters, where the domain maps to a first ontology, and where ontologies map n-grams onto a set of concepts. The method includes scoring a first set of n-grams of the document using a scoring model based on relations between members of the first set of n-grams, selecting sections of the text based on n-gram scores provided by the scoring model, and determining an initial n-gram set, where each respective n-gram of the initial n-gram set maps to a respective concept of the set of concepts, and where each respective n-gram is identified by an ontology other than the first ontology. The method includes determining related n-grams mapped to the set of concepts associated with the domain and generating a text summary for the document based on the sections and the related n-grams.

Abstract: Provided is a method including obtaining a set of ontologies mapping n-grams onto concepts to which the n-grams refer in different respective domains of knowledge. The method includes receiving an update associating a first n-gram with a first concept and receiving information by which the update is associated with a given domain of knowledge. The method includes selecting a subset of ontologies by determining that the update in the given domain of knowledge is applicable to respective domains of knowledge of the subset of ontologies and that the first concept has a specified type of relationship to a subset of concepts to which other n-grams are mapped in the subset of ontologies. The method also includes storing, in response to the determination, associations between the first n-gram and the subset of concepts in at least some of the subset of ontologies in memory of the computer system.

Abstract: Provided is a process including obtaining a set of natural-language text documents that discuss a topic, the set of documents containing different states of knowledge about the topic at different times. The process includes selecting an ontology from among a plurality of ontologies that correspond to different domains of knowledge, the selection being based on the ontology corresponding to a domain of knowledge including the topic. The process includes identifying concepts discussed in the documents using the ontology and detecting changes in at least some of the concepts over time based on differences between discussion of the concepts in documents authored at different times. The process includes updating natural language instructions on the topic based on the detected changes in the concepts and storing the updated natural language instructions in memory.

Abstract: A method for the antimicrobial finishing of synthetic fibers, in which an aqueous solution of an organic primer component (P), such as, for example, gallic acid, and, as antimicrobial component (K), at least one organic quaternary ammonium compound, and a metal salt component (M) are applied at the same time or at a different time to the fibers, permits permanent antimicrobial finishing.

Abstract: An antimicrobial composition comprising an organic antimicrobial component (K) and at least one metal salt component (M) and also, where appropriate, a solvent (L) and further auxiliary components, which comprises as organic component (K) at least one compound of the general formula (I) where the radicals independently have for example the following meanings: R1 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R2 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R3 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R4 is a branched or unbranched alkyl radical having 1 to 18 carbon atoms, R5 is a branched or unbranched alkyl radical having 1 to 18 carbon atoms, R6 is a branched or unbranched alkyl radical having 8 to 18 carbon atoms; n is an integer from 1 to 6, and which comprises as metal salt component (M) at least one salt of a di- to pentavalent metal, is useful for durable finishing of textiles.

Abstract: The use is described of zinc pyrithione for finishing woven and non-woven textiles to render them resistant against colonization by house dust mites. Zinc pyrithione is used alone or in combination with other actives with or without other textile chemicals. The formulations comprising zinc pyrithione are applied to the textiles using the standard processes of textile finishing, such that there is a concentration of preferably 1000 to 6000 ppm of zinc pyrithione on the textile. This gives on uncoated sheetlike textiles a very distinct reduction in the mite population in testing; ideal finishes even eliminate the mite population completely.

Abstract: An antimicrobial composition comprising an organic antimicrobial component (K) and at least one metal salt component (M) and also, where appropriate, a solvent (L) and further auxiliary components, which comprises as organic component (K) at least one compound of the general formula (I) where the radicals independently have for example the following meanings: R1 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R2 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R3 is a branched or unbranched alkyl radical having 1 to 12 carbon atoms, R4 is a branched or unbranched alkyl radical having 1 to 18 carbon atoms, R5 is a branched or unbranched alkyl radical having 1 to 18 carbon atoms, R6 is a branched or unbranched alkyl radical having 8 to 18 carbon atoms; n is an integer from 1 to 6, and which comprises as metal salt component (M) at least one salt of a di- to pentavalent metal, is useful for durable finishing of textiles.