US 12,762,430 B2 · Combined commodity mining method based on knowledge graph rule embedding

Abstract

The present invention is a combined commodity mining method based on knowledge graph rule embedding, comprising: expressing rules, commodities, attributes, and attribute values as embeddings; splicing and inputting the embeddings of the rules and the embeddings of the attributes into a first neural network to obtain a importance scores of the attributes; splicing and inputting the rules and attributes into a second neural network to obtain the embeddings of the attribute values that the rules should take under the attributes; calculating a similarity between the value of two inputted commodities under the attribute and the embedding of the attribute value calculated by a model; after calculating scores of all attribute-attribute value pairs, summing up to obtain scores of these two commodities under this rule; then making the cross entropy loss with the real scores of these two commodities, and iteratively training based on an optimization algorithm having gradient descent; after the model is trained, parsing the embeddings of the rules in a similar way to obtain rules that can be understood by human beings.

A computer-implemented combined commodity mining method based on knowledge graph rule embeddings, comprising:

(1) constructing a knowledge graph of commodities, wherein for each ternary group data in the knowledge graph, a head entity is a commodity I, a relation is a commodity attribute P, a tail entity is a commodity attribute value V;
(2) expressing the commodity I, commodity attribute P, and commodity attribute value V as embeddings, respectively, and randomly initializing the embeddings of a plurality of rules;
(3) splicing and inputting the embedding of each rule and the embedding of each commodity attribute into a first neural network to obtain a importance scores1 of the commodity attribute;
(4) splicing and inputting the embedding of each rule and the embedding of each commodity attribute into a second neural network to obtain the embedding of the attribute value that the rule should obtain under that attribute;
Vpred;
(5) splicing and inputting the embedding of each rule and the embedding of each commodity attribute into a third neural network, and calculating a probability score p of the same attribute value of a certain attribute under a certain rule;
(6) if attribute values of two commodities under a certain attribute are different, calculating a similarity score S21 of Vpred and V1, and a similarity score S22 of Vpred and V2;
and if the attribute values of two commodities under the certain attribute are the same, calculating a similarity score S2 of the Vpred and Vtrue;
wherein, V1 represents an embedding of an attribute value of one of the two commodities under the attribute, V2 is an embedding of an attribute value of another commodity under the attribute, Vture is an embedding of the same attribute value;
(7) when an importance score s1 of a certain attribute is greater than a threshold thres1, and the attribute values of the two commodities are the same under the certain attribute, summing up to obtain a scoreij of this attribute-attribute value pair as s1×(p+(1−p)×s2);
when the importance score s1 of a certain attribute is greater than the thres1, and the attribute values of the two commodities under the certain attribute are different, summing up to obtain the scoreij of this attribute-attribute value pair as 0.5×s1×(s21+s22);
when the importance score s1 of a certain attribute is less than or equal to the thres1, taking the score of this attribute-attribute value pair as 0;
(8) summing up the scores “scoreij” of m attribute-attribute value pairs of a commodity pair to obtain scorei:
score
i
=
∑
j
=
1
m
⁢
score
ij
(9) summing up the scores “scorei” of the commodity pair under n rules, and obtaining a final score “score” of the commodity pair:
score
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∑
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n
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score
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n
(10) comparing the obtained score of the commodity pair with two labels 0 or 1 indicating whether belonging to a combined commodity to obtain a cross entropy loss;
iteratively solving based on an optimization algorithm having gradient descent until a loss value converges and parameters of the three neural networks are trained, and obtaining the embeddings that have learned the rules at the same time;
and
(11) for the embeddings that have learned the rules, utilizing the trained neural network for analysis to obtain the rules of commodity combination;
wherein, in step (2), the commodity I, the commodity attribute P, the commodity attribute value V and a plurality of rules are respectively numbered as ids, and then each of the ids constitutes a one-hot vector, and then the one-hot vector is mapped into an embedding, which is continuously optimized with a model training process;
wherein, in step (6), the similarity scores s21, s22 and s2 are all calculated by cosine similarity, and the specific formulas are:
s
21
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1
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1
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;
s
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pred
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;
s
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true
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pred
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true
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;
wherein, in step (10), the cross entropy loss function is:
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wherein, prob(i) and y(i) are both probability distribution functions, 0≤i<K and i are integers, y(i)∈{0, 1} is a real probability distribution and 0≤prob(i)≤1 is a probability distribution predicted by the model,
∑
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,
and K refers to a total number of categories, herein, K is 2;
this cross entropy function is used to measure a difference between two distributions, the larger the value calculated by this formula, the greater the difference between the two distributions;
wherein, in step (10), the optimization algorithm having the gradient descent is SGD or Adam;
and
wherein, the specific process of step (11) is:
for the learned rule embedding and each commodity pair, splicing and inputting the rule embedding and the embedding of each attribute of the commodity pair into the first network to obtain the importance score of each attribute;
if the importance score s1 of the attribute is greater than the threshold thres1, then including the attribute in this rule;
if the attribute is comprised in this rule, and the attribute values of the two commodities under this attribute are the same, calculating a probability p of taking “same” under this attribute;
if p is greater than the threshold thres2, then taking the values under this attribute as the same;
if p is less than or equal to the threshold thres2, then calculating the similarity score s2 of the two commodities under this attribute;
if s2 is greater than the threshold thres3, then taking, by the rule, the attribute value shared by the two commodities under this attribute;
if the attribute is comprised in this rule, and the attribute values of the two commodities under this attribute are not the same,
then calculating the similarity scores s11 and s12, if both s11 and s12 are greater than the threshold thres3, then taking, by the rule, the two attribute values of these two commodities under this attribute.

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Combined commodity mining method based on knowledge graph rule embedding

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Combined commodity mining method based on knowledge graph rule embedding

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Abstract

Claims3