Abstract: A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.

Abstract: A semantic processor and method for automatically recognizing Whole-Part relations in at least one natural language electronic or digital document recognizes one or more expanded Subject-Action-Object (eSAO) sets in text, wherein each eSAO set has one or more eSAO components; matches the one or more eSAO sets against Whole-Part relationship patterns, and generates one or more eSAO Whole-Part relations based on the matching, wherein the eSAO Whole-Part relation comprises a Whole eSAO and an Part eSAO.

Abstract: A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.

Abstract: Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.

Abstract: Answering System enables a user to input a query and responds with answers. Server stores an indexed database of a plurality of S-A-O's (subject-action-object). User generates the query that includes query elements (A-O), (S-A), or (S-X-O), or element (S). The server identifies the database element S, O, or A, or elements (A-O) associated with a respective knowledge base S-A-O that includes the query element(s) in response to the server receiving the query and generates a natural language answer S-A-O that includes the identified and the query elements(s) and transmits the answer to the user apparatus that provides an audio or visual response. The server can conduct WWW searches for documents with S-A-O elements that match query element(s) and adds them to the database, thus treating the WWW as an available knowledge base.

Abstract: A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.

Abstract: Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.

Abstract: A system and method for cross-language knowledge searching. The system has a Semantic Analyzer, a natural language user request/document search pattern/semantic index Generator, a user request search pattern Translator and a Knowledge Base Searcher. The system also provides automatic semantic analysis and semantic indexing of natural language user requests/documents on knowledge recognition and cross-language relevant to user request knowledge extraction/searching. System functionality is ensured by Linguistic Knowledge Base as well as by a number of unique bilingual dictionaries of concepts/objects and actions.

Abstract: A system and method for summarizing the contents of a natural language document provided in electronic or digital form includes preformatting the document, performing linguistic analysis, weighting each sentence in the document as a function of quantitative importance, and generating one or more document summaries, from a plurality of selectable document summary types, as a function of the sentence weights.

Abstract: A semantic processor and method for automatically recognizing Whole-Part relations in at least one natural language electronic or digital document recognizes one or more expanded Subject-Action-Object (eSAO) sets in text, wherein each eSAO set has one or more eSAO components; matches the one or more eSAO sets against Whole-Part relationship patterns, and generates one or more eSAO Whole-Part relations based on the matching, wherein the eSAO Whole-Part relation comprises a Whole eSAO and an Part eSAO.

Abstract: A system and method for cross-language knowledge searching. The system has a Semantic Analyzer, a natural language user request/document search pattern/semantic index Generator, a user request search pattern Translator and a Knowledge Base Searcher. The system also provides automatic semantic analysis and semantic indexing of natural language user requests/documents on knowledge recognition and cross-language relevant to user request knowledge extraction/searching. System functionality is ensured by Linguistic Knowledge Base as well as by a number of unique bilingual dictionaries of concepts/objects and actions.

Abstract: A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.

Abstract: A system and method for summarizing the contents of a natural language document provided in electronic or digital form includes preformatting the document, performing linguistic analysis, weighting each sentence in the document as a function of quantitative importance, and generating one or more document summaries, from a plurality of selectable document summary types, as a function of the sentence weights.

Abstract: Answering System enables a user to input a query and responds with answers. Server stores an indexed database of a plurality of S-A-O's (subject-action-object). User generates the query that includes query elements (A-O), (S-A), or (S-X-O), or element (S). The server identifies the database element S, O, or A, or elements (A-O) associated with a respective knowledge base S-A-O that includes the query element(s) in response to the server receiving the query and generates a natural language answer S-A-O that includes the identified and the query elements(s) and transmits the answer to the user apparatus that provides an audio or visual response. The server can conduct WWW searches for documents with S-A-O elements that match query element(s) and adds them to the database, thus treating the WWW as an available knowledge base.

Abstract: A customized industry-oriented knowledge base (CIO KB) with information which is relevant to a user's interests includes information about different relevant natural/technical items or processes relating to given industry or discipline. This involves forming a customized industry-oriented knowledge base (CIO KB) on the basis of tree of the CIO KB comprising names of items, processes, parameters which relevant to given industry. The CIO KB is formed from an SAO KB (subject-action-object knowledge base) by selection of all the SAOs comprising the mentioned names of relevant items, processes, or parameters in their subjects or objects.

Abstract: In a digital computer, the method of processing a natural language expression entered or downloaded to the computer that includes (1) identifying in the expression expanded subject, action, object components that includes at least four components, subject, action, object (SAO) components and at least one additional component from the class of preposition, indirect object, adjective, and adverbial eSAO components (2) extracting each of the at least four components for designation into a respective subject, action, object field and at least a preposition field, indirect object field, adjective field, and adverbial field, and (3) using the components in at least certain ones of said fields for at least one of (i) displaying components to the user, (ii) forming a search pattern of a user request for information search of local or on-line databases, and (iii) forming an eSAO knowledge base. A constraint field can also be provided to accept non-classified components.

Abstract: In a digital computing system, the method of and apparatus for storing data representing a natural language sentence segment that includes at least one action (A) and one object (O) of the action, extracting the AO to form an AO extraction, processing the AO extraction pursuant to paraphrasing rules to form a plurality of para-AOs, and using the plurality of para-AOs during subsequent processing steps to be implemented in the digital computing system. One embodiment includes processing a natural language user request processing the AO extraction pursuant to positive and negative paraphrasing rules to form a plurality of para-AOs, and using the plurality of para-AOs as an expanded user request to search for information. An optional feature includes combining the S with each para-AO to form a plurality of para-SAOs as an expanded user request to search for information.