SIMILARITY IN A STRUCTURED DATASET
Detecting similarity in a structured dataset is disclosed. One example is a system including a converter, and an evaluator. A structured dataset is received via a processing system, the dataset including a plurality of objects, each object of the plurality of objects associated with a category, and each category associated with an object label. The converter converts, for each object of the plurality of objects, the object label into a semantic term, The evaluator determines, via the processing system, a term similarity for a pair of object labels in a given category, the term similarity indicative of a correlation between the respective semantic terms in the given category.
1 . A system comprising:
a structured dataset received via a processing system, the dataset comprising:
a plurality of objects,
each object of the plurality of objects associated with a category, and
each category associated with an object label;
a converter to convert, for each object of the plurality of objects, the object label into a semantic term; and
an evaluator to determine, via the processing system, a term similarity for a pair of object labels in a given category, the term similarity indicative of a correlation between the respective semantic terms in the given category.
2 . The system of claim 1 , wherein the object label for each object of the plurality of objects is numeric data, and the converter converts the numeric data into the semantic term based on a statistical distribution of object labels associated with the plurality of objects.
3 . The system of claim 1 , wherein the object label is procedural data, and the converter converts the procedural data into binary data.
4 . The system of claim 1 , wherein the evaluator determines the term similarity based on latent semantic analysis.
5 . The system of claim 1 , wherein the evaluator determines an object similarity for a given pair of objects of the plurality of objects, the object similarity based on the respective semantic terms for the given pair.
6 . The system of claim 1 , wherein the plurality of objects is a plurality of individuals, and the object label is healthcare data.
7 . The system of claim 1 , further including a classifier to classify the plurality of objects based on the respective term similarities.
8 . A method to classify objects, the method comprising:
receiving, via a processor, a structured dataset comprising:
a plurality of objects,
each object of the plurality of objects associated with a category, and
each category associated with an object label;
converting, for each object of the plurality of objects, the object label into a semantic term;
determining, via the processor, a term similarity for a pair of object labels in a given category, the term similarity indicative of a correlation between the respective semantic terms in the given category; and
classifying the plurality of objects based on the respective term similarities.
9 . The method of claim 8 , wherein the object label for each object of the plurality of objects is numeric data, and converting the object label into the semantic term is based on a statistical distribution of object labels associated with the plurality of objects.
10 . The method of claim 8 , wherein the object label is procedural data, and converting the object label into the semantic term includes converting the procedural data into binary data.
11 . The method of claim 8 , wherein determining the term similarity is based on latent semantic analysis.
12 . The method of claim 8 , further comprising:
receiving a search query via the processor; and
providing an object of the plurality of objects based on the search query and the classification.
13 . The method of claim 8 , wherein the plurality of objects is a plurality of individuals, and the object label is healthcare data.
14 . A non-transitory computer readable medium comprising executable instructions to:
receive, via a processor, a structured dataset comprising:
a plurality of objects,
each object of the plurality of objects associated with a category, and
each category associated with a numerical object label;
convert, for each object of the plurality of objects, the numerical object label into a semantic term;
determine, via the processor, a term similarity for a pair of object labels in a given category, the term similarity indicative of a correlation between the respective semantic terms in the given category; and
determine, via the processor, an object similarity for a given pair of objects of the plurality of objects, the object similarity based on the respective semantic terms for the given pair.
15 . The non-transitory computer readable medium of claim 14 , wherein determining the term similarity is based on latent semantic analysis.