4 results about "Name disambiguation" patented technology

This invention provides a method, application, and electronic device for constructing an author name disambiguation model. First, a name block and an initial training set consisting of entity pair training samples are constructed. An author name disambiguation baseline model is trained based on the initial training set. The baseline model is then used to calculate the global feature importance vector for each entity pair training sample through feature attribution calculation. The score for each entity pair training sample is calculated based on the global feature importance vector and a non-empty indicator vector, and a training set is selected. The baseline model is then retrained using the selected training set to obtain the author name disambiguation model. The objective loss function of the author name disambiguation model includes classification loss and attribution prior regularization, improving model interpretability, reducing information loss due to metadata sparsity, suppressing model attention drift, and being compatible with various model structures. It has low engineering modification costs and can be widely applied to name disambiguation scenarios in various academic data processing systems.

The invention discloses a paper data processing method and system based on semantic similarity, the paper data processing method is suitable for duplication name disambiguation of paper authors, the paper data processing method comprises: obtaining comparison information of a target author, the comparison information comprising name information of the target author; obtaining a paper set of the name information, and extracting an author set and a unit set of each paper in the paper set; comparing whether the unit sets of any two papers are consistent or not, if the unit sets of the two papers are consistent, comparing the similarity of the papers, if the similarity of the papers is smaller than a first threshold value, comparing the coincidence rate of author sets, and if the coincidence rate of the author sets is smaller than a third threshold value, extracting keywords in the papers, and carrying out keyword semantic similarity comparative analysis; and if the semantic similarity of the keywords is smaller than a second threshold value, marking different labels on the name information of the two papers. According to the method, the duplication name disambiguation of the paper author can be realized.

The invention relates to the technical field of name disambiguation, and discloses a name disambiguation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-disambiguated name data set, and forming a pairwise-combined name combination according to names; acquiring industry information of each name in the name combination; setting the weight of each preset dimension according to the industry information; calculating a dimension value of the name combination in a preset dimension, and performing weighted summation according to the dimension value of the preset dimension and the weight to obtain a weighted score value; judging whether the weighted score value is greater than a preset weighted score value or not; and affirming the names in the name combinations with the weighted score values greater than a preset weighted score value as the same name. The method has the beneficial effects that the name disambiguation efficiency and accuracy are remarkably improved, the problem of identity confusion of natural persons among different enterprises is effectively solved, and the storage overhead and the calculation complexity are greatly reduced.

The invention discloses a name disambiguation method based on deep semantic representation and a dynamic matching strategy, and belongs to the technical field of name disambiguation, and the method comprises the steps: obtaining and processing to-be-disambiguated name reference item data, and generating to-be-disambiguated name reference item data of a unified view; constructing a name disambiguation depth semantic capture model based on artificial intelligence, analyzing the to-be-disambiguated name reference item data, and determining to-be-disambiguated name depth semantic representation data; and dynamically matching the candidate cluster according to the deep semantic representation data of the name to be disambiguated, calculating a dynamic matching score between the deep semantic representation data of the name to be disambiguated and the candidate cluster, and realizing name disambiguation according to the dynamic matching score. According to the invention, the problem that the accuracy and efficiency of name disambiguation are reduced because name disambiguation cannot be realized based on deep semantic representation and dynamic matching in the prior art is solved. According to the method, name disambiguation can be realized based on deep semantic representation and dynamic matching, and the accuracy and efficiency of name disambiguation are effectively improved.