SEMANTIC SPARSE WEB SERVICE DISCOVERY METHOD BASED ON GAUSSIAN ATM AND WORD EMBEDDING

摘要

The present invention provides a semantic sparse web service discovery method based on Gaussian ATM and word embedding. The method is performed in the following order: collecting a Web service description document, parsing the collected Web service description document, obtaining a feature vocabulary in the Web service description document, and preprocessing the vocabulary in the Web service description document to obtain a set of prototype words; using a word embedding training model Word2Vec to train the set of prototype words to obtain a continuous vector representation of each word in the set of prototype words; using the set of prototype words obtained by the Gaussian ATM model to train, and obtaining each Web service hierarchical structure; using the set of continuous vectors obtained by training to enrich user query, and obtaining an extended user query; using the obtained service hierarchical structure and using a probability ranking method to obtain query output corresponding to the extended user query. The method provided in the present invention can implement accurate and efficient Web service discovery.