[1]周澳回,翁知远,周思源,等.一种基于主题过滤和语义匹配的服务发现方法[J].郑州大学学报(工学版),2022,43(06):36-41.[doi:10.13705/j.issn.1671-6833.2022.06.003]
　ZHOU Aohui,WENG Zhiyuan,ZHOU Siyuan,et al.A Service Discovery Method Based on Topic Filtering and Semantic Matching[J].Journal of Zhengzhou University (Engineering Science),2022,43(06):36-41.[doi:10.13705/j.issn.1671-6833.2022.06.003]
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一种基于主题过滤和语义匹配的服务发现方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43卷
期数:
2022年06期
页码:
36-41
栏目:
出版日期:
2022-09-02

文章信息/Info

Title:
A Service Discovery Method Based on Topic Filtering and Semantic Matching
作者:
周澳回1;  翁知远 2;  周思源 1;  黄 乔1 ;  汪 烨1 ;  张 华1
1.浙江工商大学计算机与信息工程学院;2.内布拉斯加大学林肯分校计算机科学与工程系;

Author(s):
ZHOU Aohui1;  WENG Zhiyuan2;  ZHOU Siyuan1;  HUANG Qiao1;  WANG Ye1;  ZHANG Hua1
1.School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China; 、
2.Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln 68508, U.S.
Keywords:
service computing;  business targets;  service matching;  recurrent neural networks;  natural language processing
分类号:
TP311；O244
DOI:
10.13705/j.issn.1671-6833.2022.06.003
文献标志码:
A
摘要:
在互联网现有的大量可用的服务中,如何高效的为特定的业务目标匹配合适的服务是目前研究的一大难题。 针对这一问题,提出一种基于主题过滤和语义匹配的可用于海量服务发现的方法。 首先,使用 Word2Vec 对主题描述文本和业务目标描述文本进行相似度比较,获取业务目标主题。 其次,使用 TextRank 对服务描述文本提取服务关键句,通过提取到的业务目标主题对服务关键句进行过滤,缩小比较范围。 再次,对相应的业务目标与服务描述文本进行词向量提取,使用带注意力机制 BiLSTM 模型计算两者相似度并返回与业务目标描述文本最相似的前 N 个服务列表给业务开发人员进行选择,并对从Programmable Web上爬取的数据进行标注,以此建立实验所需的业务目标-服务句子数据集,评估本文方法的有效性。 最后,与 TextCNN 等模型进行对比,结果表明:本文方法的 MAP 比不带注意力机制的 BiLSTM 模型、TextCNN 模型、Word2VecSD 模型分别提高了 1. 41 百分点、4. 61 百分点和 4. 95 百分点,并且在今后的工作中有进一步改进的潜力。
Abstract:
Among the large number of available services on the internet, how to efficiently match the right service for a specific business target is a major challenge in current research. To address this problem, a method based on topic fitering and semantic matching was proposed that could be used for massive service discovery. The method first used Word2Vec to compare the similarity between the topic description text and the business target description text to obtain the business target topic, and then used TextRank to extract the service key sentences from the service description text. The service key sentences were filtered by the extracted business target topics to narrow the comparison range. Then, the word vector was extracted from the corresponding business goal and service description text, and the BiLSTM model with attention mechanism was used to calculate the similarity between them and return the list of the TOP-N services that were most similar to the business target description text for business developers for selection. And the data crawled from Programmable Web was annotated to build the business target-service sentence dataset required for the experiments, and evaluate the effectiveness of the methods in this study. Finally, the comparison results with models such as TextCNN , BiLSTM, and Word2VecSD showed that MAP of this method could be increase by 1.41 percentage points, 4.61 percentage points, and 4.95 percentage points. The finding of this study lay solid ground for further improvement in future work.

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更新日期/Last Update: 2022-10-03