Oracle International Corporation

Silver Creek Systems, Inc.

Edward A Green

Kevin L Markey

Luis E Rivas

Mark L Kreider

Alec Sharp

Philip Ogren

Ramon Krosley

Alee Sharp

A search system associates contextual metadata with search terms and/or stored terms to facilitate identification of relevant information. In one implementation, a search term is identified () from a received search request. The search term is then rewritten () in standard form and the standard form term is then set () as the current search parameter. A source database is then searched () using the current search parameter. If any results are obtained () these results may be output () to the user. If no results are obtained, a parent classification of the search term is set () as the current search parameter and the process is repeated. The invention thereby provides the ease of use of term searching with the comprehensiveness of category searching.

Subject matter context search engine

Computer-based tools and methods for conversion of data from a first form to a second form without reference to the context of data to be converted. The conversion may be facilitated by matching source data with external information (e.g., public and/or private schema) that contain rules (e.g., context specific rules) for conversion of the data. The matching may be performed based on an optimized index string matching technique that may be operable to match source data to external information that is context dependent without specific identification of the context of either the source data or the external information identified. Accordingly, the conversion of data may be performed in an unsupervised machine learning environment.

Contextually blind data conversion using indexed string matching

A machine based tool and associated logic and methodology used in converting data from an input form to a target form using context dependent conversion rules. A frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment. A slot represents an included chunk of information. An exemplary conversion system () includes a parser () for use in parsing and converting an input stream () to provide an output stream () in a form for use by a target system (). The parser () uses information from a public schema (), a private schema () and a grammar (). The public schema (), private schema () and grammar () may include conversion rules applicable to less than the whole of a subject matter area including the input stream ().

Frame-slot architecture for data conversion

A string analysis tool for calculating a similarity metric between a source string and a plurality of target strings. The string analysis tool may include optimizations that may reduce the number of calculations to be carried out when calculating the similarity metric for large volumes of data. In this regard, the string analysis tool may represent strings as features. As such, analysis may be performed relative to features (e.g., of either the source string or plurality of target strings) such that features from the strings may be eliminated from consideration when identifying target strings for which a similarity metric is to be calculated. The elimination of features may be based on a minimum similarity metric threshold, wherein features that are incapable of contributing to a similarity metric above the minimum similarity metric threshold are eliminated from consideration.

Scalable string matching as a component for unsupervised learning in semantic meta-model development

A semantic conversion system () includes a self-learning tool (). The self-learning tool () receives input files from legacy data systems (). The self-learning tool () includes a conversion processor () that can calculate probabilities associated with candidate conversion terms so as to select an appropriate conversion term. The self-learning tool () provides a fully attributed and normalized data set ().

Self-learning data lenses for conversion of information from a first form to a second form

Self-learning data lenses for conversion of information from a source form to a target form

A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules, and in efficiency generating an index that may be utilized to access the converted data in a database. Once the data has been converted, an index data structure for each data object may be automatically generated that encodes one or more characteristics or attributes of the converted data so that an entity may access the data using the index structure. As an example, the one or more characteristics may include categories, subcategories, or other attributes of the data.

Self-indexing data structure

A machine based tool and associated logic and methodology are used in converting data from an input form to a target form using context dependent conversion rules. In particular, a frame-slot architecture is utilized where a frame represents an intersection between a contextual cue recognized by the machine tool, associated content and related constraint information to specific to that conversion environment, whereas a slot represents an included chunk of information. An exemplary conversion system () includes a parser () for use in parsing and converting an input stream () from a source () to provide an output stream () in a form for use by a target system (). To accomplish the desired conversion, the parser () uses information from a public schema (), a private schema () and a grammar (). The public schema (), private schema () and grammar () may include conversion rules applicable to less than the whole of a subject matter area including the input stream ().

In connection with a machine-based system for transforming data from a source form to a target form, a tool is provided for sharing information established in developing a transformation model. The shared information may relate to rules for mapping source collection terms to standardized terms, rules for ordering or syntax, rules for classifying terms or other transformation rules. Such information defines a knowledge base for the project. The knowledge base is divided into domains and subdomains that define a hierarchy that includes dependencies and references. These dependencies and references are used for domain management such that any conflicts and inconsistencies can be identified and arbitrated. In this manner, information sharing is accommodated, for example, for efficient knowledge base creation and multi-user functionality.

Multi-user functionality for converting data from a first form to a second form

A method and apparatus are disclosed for transforming information from one semantic environment to another. In one implementation, a SOLx system () includes a Normalization/Translation NorTran Workbench () and a SOLx server (). The NorTran Workbench () is used to develop a knowledge base based on information from a source system (), to normalize legacy content () according to various rules, and to develop a database () of translated content. During run time, the SOLx server () receives transmissions from the source system (), normalizes the transmitted content, accesses the database () of translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages.

Method and apparatus for normalizing and converting structured content

A method and apparatus are disclosed for transforming information from one semantic environment to another, for example, to facilitate electronic information searches. In one implementation, a SOLx systemincludes a Normalization/Translation NorTran Workbenchand a SOLx server. The NorTran Workbenchis used to develop a knowledge base based on information from a source system, to normalize legacy contentaccording to various rules, and to develop a databaseof translated content. During run time, the SOLx serverreceives transmissions from the source system, normalizes the transmitted content, accesses the databaseof translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages. Additionally, content can be classified relative to a taxonomy defining relationships between terms or items so as to facilitate electronic searching or other processing.

Normalizing and classifying locale-specific information

A method and apparatus are disclosed for transforming information from one semantic environment to another. In one implementation, a SOLx system includes a Normalization/Translation NorTran Workbench and a SOLx server. The NorTran Workbench is used to develop a knowledge base based on information from a source system, to normalize legacy content according to various rules, and to develop a database of translated content. During run time, the SOLx server receives transmissions from the source system, normalizes the transmitted content, accesses the database of translated content and otherwise translates the normalized content, and reconstructs the transmission to provide substantially real-time transformation of electronic messages.

Growing community of inventors

Englewood, CO, United States of America

Edward A Green