Abstract: Embodiments for automated feature engineering are provided. Data associated with a machine learning model is received. The received data is mapped to at least one description associated with the data. A feature for the machine learning model is generated based on a formula within a corpus. The formula is associated with the at least one description.

Abstract: Embodiments for learning semantic description of data based on physical knowledge in a computing environment by a processor. Physical knowledge data and semantic labels associated with data from one or more data sources may be learned. Source attributes of the one or more data sources may be associated with one or more classes and concepts of a plurality of ontologies based on the physical knowledge data and the semantic labels to generate textual descriptors of the data.

Abstract: A method, system, and computer program product are disclosed. The method includes extracting at least one identifier from a formula in a document and extracting text passages in the document that contain the identifier(s). The method also includes selecting an identifier and extracted text passages containing the identifier, as well as generating identifier-passage pairs for the selected text passages and the identifier. Further, the method includes submitting the identifier-passage pairs to a question answering (QA) model, which generates candidate answers from the selected text passages. A definition of the identifier is then selected from the candidate answers.

Abstract: Embodiments for using learned physical knowledge to guide feature engineering in a computing environment by a processor. Physical knowledge data associated with a dataset may be learned. The physical knowledge data may be translated into a plurality of features for one or more automated feature engineering models to execute for one or more prediction and monitoring operations, wherein the plurality of features represent relationships between the physical knowledge data.

Abstract: A method, system, and computer program product are disclosed. The method includes extracting at least one identifier from a formula in a document and extracting text passages in the document that contain the identifier(s). The method also includes selecting an identifier and extracted text passages containing the identifier, as well as generating identifier-passage pairs for the selected text passages and the identifier. Further, the method includes submitting the identifier-passage pairs to a question answering (QA) model, which generates candidate answers from the selected text passages. A definition of the identifier is then selected from the candidate answers.

Abstract: A computer-implemented method of extracting properties of one or more variables of a mathematical transformation in a document identified by automatic feature engineering (AFE) is provided. The document is parsed to extract at least one of a mathematical formula including identifying data, a textual description of the one or more variables of the mathematical transformation, or a category identifier to which the document belongs. Constraints are determined to apply to the one or more variables extracted from the mathematical transformations in the document. At least one candidate feature is automatically generated from a portion of an input data that conforms to the determined constraints.

Abstract: Embodiments for automated feature engineering are provided. Data associated with a machine learning model is received. The received data is mapped to at least one description associated with the data. A feature for the machine learning model is generated based on a formula within a corpus. The formula is associated with the at least one description.

Abstract: A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.

Abstract: A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.

Abstract: A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.

Abstract: A method of updating a plurality of databases storing place names includes collecting first, second and third coordinates of addresses including a first, a second and a third place name, respectively, clustering the first, second and third coordinates in a first, a second and a third cluster, respectively, obtaining a base address including the first place name and having base coordinates, assessing the best fit of the base coordinates to the first, second, and third clusters, and based on the two best fit clusters, identifying the second or third place name as an alias place name for the first place name. The method further includes obtaining alias addresses including the alias place name, fuzzy matching the base address to an alias address, and updating first and second databases by linking the alias address in the second database to the base address in the first database.