Abstract: A system processes unstructured data to identify a plurality of subsets of text in a set of text in the unstructured data and determines, for a subset from the plurality of subsets, probabilities based on a position of the subset in the set of text, a part of speech (POS) of each word in the subset, and POSs of one or more words on left and right hand sides of the subset, a number of the one or more words being selected based on a length of the set of text. The system generates a feature vector for the subset, the feature vector including the probabilities and additional features of the subset; and classifies, using a classifier, the subset into one of a plurality of classes based on the feature vector for the subset, the plurality of classes representing an ontology of a domain of knowledge.

Abstract: A system processes unstructured data to identify a plurality of subsets of text in a set of text in the unstructured data and determines, for a subset from the plurality of subsets, probabilities based on a position of the subset in the set of text, a part of speech (POS) of each word in the subset, and POSs of one or more words on left and right hand sides of the subset, a number of the one or more words being selected based on a length of the set of text. The system generates a feature vector for the subset, the feature vector including the probabilities and additional features of the subset; and classifies, using a classifier, the subset into one of a plurality of classes based on the feature vector for the subset, the plurality of classes representing an ontology of a domain of knowledge.

Abstract: A system and method of performing fault diagnosis and analysis for one or more vehicles. The method includes: obtaining design failure mode and effect analysis (DFMEA) data that specifies a plurality of failure modes; receiving diagnostic association data; receiving vehicle operation signals association data; generating augmented DFMEA data that indicates a causal relationship between the diagnostic data and the first set of failure modes, and that indicates a causal relationship between the vehicle operation signals data and the second set of failure modes, wherein the augmented DFMEA data is generated based on the DFMEA data, the diagnostic association data, and the vehicle operation signals association data; and performing fault diagnosis and analysis for the one or more vehicles using the augmented DFMEA data.

Abstract: A system and method of performing fault diagnosis and analysis for one or more vehicles. The method includes: obtaining design failure mode and effect analysis (DFMEA) data that specifies a plurality of failure modes; receiving diagnostic association data; receiving vehicle operation signals association data; generating augmented DFMEA data that indicates a causal relationship between the diagnostic data and the first set of failure modes, and that indicates a causal relationship between the vehicle operation signals data and the second set of failure modes, wherein the augmented DFMEA data is generated based on the DFMEA data, the diagnostic association data, and the vehicle operation signals association data; and performing fault diagnosis and analysis for the one or more vehicles using the augmented DFMEA data.

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 method and system to determine relatedness select a first customer observable from a first source document, the first customer observable being made up of two terms, the two terms being a first term of a first type and a first term of a second type, and select a second customer observable from a second source document, the second customer observable being made up of a second term of the first type and a second term of the second type. The method includes creating a first corpus of all documents that include the first terms, creating a second corpus of all documents that include the second terms, obtaining other first terms in the first corpus and other second in the second corpus, and performing semantic similarity analysis to determine a similarity score between the first customer observable and the second customer observable.

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 method and system to determine relatedness select a first customer observable from a first source document, the first customer observable being made up of two terms, the two terms being a first term of a first type and a first term of a second type, and select a second customer observable from a second source document, the second customer observable being made up of a second term of the first type and a second term of the second type. The method includes creating a first corpus of all documents that include the first terms, creating a second corpus of all documents that include the second terms, obtaining other first terms in the first corpus and other second in the second corpus, and performing semantic similarity analysis to determine a similarity score between the first customer observable and the second customer observable.

Abstract: A system and method of analyzing content of multilingual vehicle diagnostic records includes: determining a word window within a vehicle diagnostic record; identifying a pair or a tuple comprising parts, symptoms, or actions; generating a plurality of pairs or tuples comprising parts, symptoms, or actions; determining a frequency value for each pair or tuple; and comparing the determined frequency value with a predetermined threshold.

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 and method of identifying relevant service terms within service records includes: receiving service terms included in one or more service records at computer processing equipment; classifying the service terms into a group of likely relevant service terms and a group of likely irrelevant service terms using the computer processing equipment; and identifying the relevant service terms from the group of likely relevant service terms and ignoring the likely irrelevant service terms using the computer processing equipment.

Abstract: A system and method of analyzing content of multilingual vehicle diagnostic records includes: determining a word window within a vehicle diagnostic record; identifying a pair or a tuple comprising parts, symptoms, or actions; generating a plurality of pairs or tuples comprising parts, symptoms, or actions; determining a frequency value for each pair or tuple; and comparing the determined frequency value with a predetermined threshold.

Abstract: A system and method of analyzing vehicle diagnostic records using a trained database includes: receiving one or more vehicle diagnostic records; determining the number of words in each vehicle diagnostic record; accessing a probable or predominant classification from the trained database for one or more word positions; and classifying the word positions based on the probably or predominant classification.