Patents by Inventor Rakebul Muff Hasan

Rakebul Muff Hasan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11961010
    Abstract: Provided is a method for performing entity linking between a surface entity mention in a surface text and entities of a knowledge graph, including supplying the surface text to a contextual text representation model, pooling contextual representations of the tokens of a surface entity mention in the surface text with contextual representations of the other tokens within the surface text to provide a contextual entity representation vector representing the surface entity mention; supplying an identifier of a candidate knowledge graph entity to a knowledge graph embedding model, to provide an entity node embedding vector and combining the contextual entity representation vector with the entity node embedding vector to generate an input vector applied to a fully connected layer which provides an unnormalized output transformed by a softmax function into a normalized output processed to classify whether the surface entity mention corresponds to the candidate knowledge graph entity.
    Type: Grant
    Filed: June 21, 2021
    Date of Patent: April 16, 2024
    Assignee: SIEMENS AKTIENGESELLSCHAFT
    Inventors: Rakebul Muff Hasan, Ulugbek Peter Kodirov
  • Publication number: 20210406706
    Abstract: Provided is a method for performing entity linking between a surface entity mention in a surface text and entities of a knowledge graph, including supplying the surface text to a contextual text representation model, pooling contextual representations of the tokens of a surface entity mention in the surface text with contextual representations of the other tokens within the surface text to provide a contextual entity representation vector representing the surface entity mention; supplying an identifier of a candidate knowledge graph entity to a knowledge graph embedding model, to provide an entity node embedding vector and combining the contextual entity representation vector with the entity node embedding vector to generate an input vector applied to a fully connected layer which provides an unnormalized output transformed by a softmax function into a normalized output processed to classify whether the surface entity mention corresponds to the candidate knowledge graph entity.
    Type: Application
    Filed: June 21, 2021
    Publication date: December 30, 2021
    Inventors: Rakebul Muff Hasan, Ulugbek Peter Kodirov