International Business Machines Corporation

Victoria O Odeyemi

Craig M Trim

Tushar Agrawal

Robert A Balfe

Vivek Verma

Jason A Smith

Albert T Y Wong

Serge Baduk

Sridhar Satuloori

Ahmed M A Nassar

Eman Omar

For confidence weighting relationships between complex entities in unstructured data an expressed relationship between a subset of a set of tokens is extracted from a knowledge graph corresponding to the unstructured data. The knowledge graph includes the set of tokens. The tokens in the subset of tokens are related in the expressed relationship by a set of predicates. A number of occurrences of the set of predicates in the unstructured data is determined. A number of occurrences of the expressed relationship in the unstructured data is determined. Using the number of occurrences of the set of predicates and the number of occurrences of the expressed relationship, a confidence value is computed and assigned to the expressed relationship.

Confidence weighting of complex relationships in unstructured data

A method for providing web transaction feedback includes a client device receiving a response indicating a success or a failure of a user operation pertaining to a potential web transaction. The client device then provides a web transaction feedback to the user using a human interface element capable of causing the client device to perform a set of motions and/or emit a range of temperatures. The set of motions is comprised of at least a first sub-set of the set of motions indicating the success of the user operation and at least a second sub-set of the set of motions indicating the failure of the user operation. The range of temperatures is comprised of at least a first temperature of the range of temperatures indicating the success of the user operation and at least a second temperature of the range of temperatures indicating the failure of the user operation.

Sensory feedback indicators for transactional processes

To extract relationships between complex entities from unstructured data, a parser parses, using an existing language model, the unstructured data to generate a parse tree. From the parse tree, a set of tokens is created. A token in the set of tokens includes a set of words found in the unstructured data. The set of tokens is inserted in the existing language model to form an enhanced language model. The unstructured data is re-parsed using the enhanced language model to create a knowledge graph. From the knowledge graph, a relationship between a subset of the set of tokens is extracted.

Extracting complex entities and relationships from unstructured data

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Toronto, Canada

Victoria O Odeyemi