Abstract: A system, for filtering and fusing multi-source ontologies. The system includes a tangible processing controller unit and non-transitory computer-readable storage device in communication with the tangible processing controller unit. The storage device includes a first receiving unit that, when executed by the tangible processing control unit, receives a plurality of ontologies, each ontology having a set of rules and a class structure with a plurality of data classes. The storage device also includes a second receiving unit that, when executed, receives data. The device also includes a comparison unit that compares the data classes from the plurality of ontologies, and a merging unit that merges the data classes that are identical or consistent into a new data class. The storage device also includes a discarding unit that discards the data classes that are inconsistent. The storage device also includes a new-set-generation unit that generates a new set of class structure.

Abstract: A system having an annotation module that annotates, using a master ontology, unstructured verbatim regarding a product and related issue, and a customer-observable (CO) construction module determining associations amongst terminology in the annotated output, yielding a group of CO pairs. A CO merging module merges at least one first CO pair into a second CO pair based on similarities. A pointwise mutual-information module determines which CO pairs of the group of merged CO pairs are relatively more-severe or more-relevant, yielding a group of critical CO pairs. An output module initiates activity to implement the results, such as by automated repair of the product or change to product design or manufacturing process. The system in some embodiments identifies, using a subject-matter-expert (SME) database, features of false-positive associations, and in machine-learning implements the features to improve CO formation going forward.

Abstract: A system, for filtering and fusing multi-source ontologies. The system includes a tangible processing controller unit and non-transitory computer-readable storage device in communication with the tangible processing controller unit. The storage device includes a first receiving unit that, when executed by the tangible processing control unit, receives a plurality of ontologies, each ontology having a set of rules and a class structure with a plurality of data classes. The storage device also includes a second receiving unit that, when executed, receives data. The device also includes a comparison unit that compares the data classes from the plurality of ontologies, and a merging unit that merges the data classes that are identical or consistent into a new data class. The storage device also includes a discarding unit that discards the data classes that are inconsistent. The storage device also includes a new-set-generation unit that generates a new set of class structure.

Abstract: Methods and systems are provided for automatically comparing, combining and fusing vehicle data. First data is obtained pertaining to a first plurality of vehicles. Second data is obtained pertaining to a second plurality of vehicles. One or both of the first data and the second data include abbreviated terms. The abbreviated terms are disambiguating at least in part by identifying, from a domain ontology stored in a memory, respective basewords that are associated with each of the abbreviated terms, filtering the basewords, performing a set intersection of the basewords, and calculating posterior probabilities for the basewords based at least in part on the filtering and the set intersection. The first data and the second data are combined, via a processor, based on semantic and syntactic similarity between respective data elements of the first data and the second data and the disambiguating of the abbreviated terms.

Abstract: A method of detecting textual and behavioral commonalities in warranty reported data. Extracting, by a processor, records of verbatim data from a memory storage unit. A first set of basewords is identified for comparison with the extracted records. A binary flag is set in response to an occurrence of a respective baseword in a respective record. An occurrence matrix is generated that includes entries identifying a number of times basewords are identified in each record. The occurrence matrix is formatted to a format as identified by the user.

Abstract: A method of automatically developing an ontology for product function and failure mode documentation for an apparatus. The apparatus is identified. A function-flow model is generated for the identified apparatus for identifying a composite structure of the apparatus. Functions and failure modes associated with the identified apparatus are enumerated. Failure data is obtained from a plurality of heterogeneous data sources. A semantic similarity module is applied to the enumerated failures by comparing a plurality of documents between the data sources. The semantic similarity module bridges a variety of terms used in the heterogeneous data to describe a respective failure. Failures associated with the enumerated apparatus functions are extracted from the plurality of documents between heterogeneous data sources. A composite of related terminology is generated for each identified failure mode.

Abstract: A method of automatically developing an ontology for product function and failure mode documentation for an apparatus. The apparatus is identified. A function-flow model is generated for the identified apparatus for identifying a composite structure of the apparatus. Functions and failure modes associated with the identified apparatus are enumerated. Failure data is obtained from a plurality of heterogeneous data sources. A semantic similarity module is applied to the enumerated failures by comparing a plurality of documents between the data sources. The semantic similarity module bridges a variety of terms used in the heterogeneous data to describe a respective failure. Failures associated with the enumerated apparatus functions are extracted from the plurality of documents between heterogeneous data sources. A composite of related terminology is generated for each identified failure mode.

Abstract: Methods and systems are provided for automatically comparing, combining and fusing vehicle data. First data is obtained pertaining to a first plurality of vehicles. Second data is obtained pertaining to a second plurality of vehicles. The first data and the second data are compared and combined based on syntactic similarity between respective data elements of the first data and the second data collected during different stages of vehicle life cycle development.