International Business Machines Corporation

Shilpi Ahuja

Rashmi Gangadharaiah

Sheng Hua Bao

Mary Ann Roth

Rafael J Z Bastidas

Charles Daniel Wolfson

Thomas Kemp

A system and method are provided for inferring location attributes from data entries. The method comprises for data entries in a structured data set format, a computer system selecting a sample of rows. The computer system then identifies columns containing geospatial and temporal information based on the column headings. The computer system next identifies location information within the structured data set. The computer system determines implied location information based on the identified location information. The computer system derives location values based on the identified and implied location information using consolidation rules, resulting in a final set of location attributes for the data entries. The computer system then associates the final set of location attributes with the data entries.

Inferring location attributes from data entries

A method of identifying location data in a data set comprises generating a data sample from the data set, training a plurality of models with the data sample to identify the location data in the data set, and applying the data set to the trained models to determine the location data within the data set. The plurality of models includes one or more first models to identify primary attributes of the location data indicating a geographical area and one or more second models to identify secondary attributes of the location data used to determine corresponding primary attributes.

Method to determine columns that contain location data in a data set

Merging synonymous entities from multiple structured sources into a dataset includes receiving a first set of paired terms from a first authoritative source for a domain and a second set of paired terms from a second authoritative source for the domain. The first set of paired terms is compared to the second set of paired terms with a similarity assessment based on a clustering statistical algorithm to identify paired terms from the first set of paired terms that share a synonymous term with one or more paired terms from the second set of paired terms. The paired terms associated with the synonymous term are merged and a dataset is generated that associates a normalized version of the synonymous term with any terms included in the merged paired terms.

Merging synonymous entities from multiple structured sources into a dataset

Relationship extraction between descriptors in one or more lists of weather condition descriptors, and adverse event descriptors within unstructured data sources using natural language processing. Medical condition descriptor may be a descriptor that may be used to further extract relationships between weather condition descriptors and adverse event descriptors. A data object is generated, according to a data model, based on the extracted relationships between the descriptors. A set of candidate unstructured documents containing the extracted relationship between the descriptors is retrieved and filtered by selecting unstructured documents that include a precautionary measure descriptor. The filtered precautionary measure descriptors are presented to a user in a summarized message to a user device.

Weather-based natural language text processing

A mechanism is provided for automatically detecting and cleansing erroneous concepts in an aggregated knowledge base. A graph data structure representing the concept present in a portion of the natural language content is generated. The graph data structure is analyzed to determine whether or not the graph data structure comprises one or more concept conflicts in association with a set of nodes in the graph data structure, the one or more concept conflicts are associated with the set of nodes if two or more nodes represent separate and distinct concepts. Responsive to determining that there are one or more concept conflicts due to there being two or more nodes representing separate and distinct concepts, the two or more nodes are split into separate distinct concepts within the knowledge base.

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San Jose, CA, United States of America

Shilpi Ahuja