API recommendation method based on knowledge graph and collaborative filtering

A technology of knowledge graph and collaborative filtering, which is applied in character and pattern recognition, instruments, electrical digital data processing, etc., can solve the problem of data sparseness and achieve the effect of improving accuracy

Pending Publication Date: 2021-09-17
ZHEJIANG GONGSHANG UNIVERSITY
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  • Claims
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AI Technical Summary

Problems solved by technology

[0005] In order to efficiently and accurately recommend APIs for corresponding Mashups, this invention proposes an API recommendation method based on knowledge graphs and collaborative filtering. sparse problem

Method used

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  • API recommendation method based on knowledge graph and collaborative filtering
  • API recommendation method based on knowledge graph and collaborative filtering
  • API recommendation method based on knowledge graph and collaborative filtering

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Embodiment Construction

[0057] The technical solution of the present invention will be described in further detail below through embodiments and in conjunction with the accompanying drawings.

[0058] Such as figure 1 As shown, the embodiment of the present invention provides an API recommendation method based on knowledge graph and collaborative filtering, and the specific steps are as follows:

[0059] (1) Construct a service knowledge map based on Mashup and existing API. For example according to figure 2 Part of the knowledge map constructed by the target Mashup information named sportlogger is as follows image 3 shown. The relationship between Mashup and the used API is used. For example, sportlogger and twitter form a triple (sportlogger, use, twitte). The relationship between the target Mashup and category is belong_to. For example, sportlogger and Travel form a triplet (sportlogger, belong_to, twitte). The relationship between Mashup and tag is Tag, such as sportlogger and Social form...

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Abstract

The invention discloses an API recommendation method based on a knowledge graph and collaborative filtering. The method comprises the following steps: constructing a service knowledge graph; embedding API entities in the knowledge graph into a low-dimensional space, and calculating the similarity Sim1 between APIs; obtaining APIs used by the target Mashup, and enabling similar APIs of the APIs to form a recommendation set RS1 based on Sim1; extracting functions between the Mashups according to the Mashup description, and calculating the similarity Sim2 between the target Mashup and other Mashups through the extracted functions; obtaining a similar Mashup of the target Mashup based on the Sim2, and combining APIs used by the similar Mashup into a recommendation set RS2; constructing a usage matrix of the Mashup and a usage matrix of the API based on a historical usage relationship between the Mashup and the API, and calculating the similarity Sim3 between the Mashup matrixes and the similarity Sim4 between the API matrixes; obtaining a similar Mashup of the target Mashup based on the Sim3, and enabling APIs used by the similar Mashup to form a recommendation list RS3; obtaining APIs used by the target Mashup, and enabling similar APIs of the APIs to form a recommendation list RS4 based on Sim4; and finally, obtaining a final API recommendation result according to the RS1, the RS2, the RS3 and the RS4.

Description

technical field [0001] The invention relates to the field of Mashup-oriented service recommendation and the technical field of knowledge graph, in particular to an API recommendation method based on knowledge graph and collaborative filtering. Background technique [0002] With the rapid development of the Internet, Web services are becoming a major technology. Nowadays, we rely more and more on APIs, and a single Web API can no longer meet our needs. At this time, multiple APIs often need to work together to achieve our ultimate goal. [0003] In recent years, the concept of Mashup has gradually become popular, which is a Web application that utilizes data and Web APIs by combining existing Web resources. A mashup often combines two or more Web APIs. For example, a mashup named sportlogger is an application that provides sports blogging functions. This Mashup integrates three APIs, namely google maps, twitter, and janrain engage. Mashup is easy to realize end-user requir...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F16/9535G06K9/62
CPCG06F16/367G06F16/9535G06F18/22
Inventor 姜波杨俊琛王慕抽秦艳斌王恬潘伟丰
Owner ZHEJIANG GONGSHANG UNIVERSITY
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