Construction of trainable semantic vectors and clustering, classification, and searching using trainable semantic vectors
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Abstract
An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.
47 Citations
75 Claims
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1-52. -52. (canceled)
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53. A method of searching for datasets within a collection of datasets, the method comprising the steps:
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constructing a trainable semantic vector for each dataset;
receiving a query containing information indicative of desired datasets;
constructing a trainable semantic vector for the query;
comparing the trainable semantic vector for the query to the trainable semantic vector of each dataset; and
selecting datasets whose trainable semantic vectors are closest to the trainable semantic vector for the query. - View Dependent Claims (54, 55, 56, 72)
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57. A method of expanding a dataset, the method comprising the steps:
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constructing a trainable semantic vector for the dataset;
comparing the trainable semantic vector for the dataset to the trainable semantic vectors of each of the data points in a semantic lexicon;
selecting data points whose trainable semantic vectors are closest to the trainable semantic vector for the dataset;
adding said selected data points to said dataset. - View Dependent Claims (58, 59, 73)
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60-64. -64. (canceled)
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65. A system for searching datasets within a collection of datasets, the system comprising:
a computer configured to;
construct a trainable semantic vector for each dataset;
receive a query containing information indicative of desired datasets;
construct a trainable semantic vector for the query;
compare the trainable semantic vector for the query to the trainable semantic vector of each dataset; and
select datasets whose trainable semantic vectors are closest to the trainable semantic vector for the query. - View Dependent Claims (74)
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66-70. -70. (canceled)
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71. A computer-readable medium carrying one or more sequences of instructions for searching for datasets within a collection of datasets, wherein execution of the one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:
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constructing a trainable semantic vector for each dataset;
receiving a query containing information indicative of desired datasets;
constructing a trainable semantic vector for the query;
comparing the trainable semantic vector for the query to the trainable semantic vector of each dataset; and
selecting datasets whose trainable semantic vectors are closest to the trainable semantic vector for the query. - View Dependent Claims (75)
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Specification