DEVELOPMENT OF AUTOMATED WEB SERVICE CLASSIFICATION SYSTEM USING NATURAL LANGUAGE DESCRIPTION AND TEXT MINING
The development of web services in the world of service computing continues to evolve, and its application in business sector is becoming more advanced. As technology progresses, many web services are transitioning from SOAP to RESTful. Consequently, the use of structured descriptions like WSDL i...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/74779 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The development of web services in the world of service computing continues to
evolve, and its application in business sector is becoming more advanced. As
technology progresses, many web services are transitioning from SOAP to
RESTful. Consequently, the use of structured descriptions like WSDL is being
phased out. There are various service descriptions available for RESTful services,
such as OpenAPI Specification, which includes both structured and natural
language descriptions. Manually performing service discovery based on these
structured or natural language descriptions can be complex and time-consuming,
particularly when dealing with a large collection of services.
To address these challenges, automatic web service classification using service
descriptions can streamline service discovery process. Development of an
automated web service classification system involves employing text mining
methods, including dataset search, preprocessing, building a classification model,
and constructing automatic classification system. Dataset used in this study consists
of web service descriptions obtained from ProgrammableWeb, categorized into 5,
10, 15, 20 labels. Methods used to build classification model include problem
transformation, adapted algorithms, ensemble, LSTM and BERT.
Based on the conducted experiments and tests, the best model is label powerset with
SVM linear, which falls under problem transformation method. This model exhibits
most favorable evaluation metrics and can classify web services in shortest amount
of time. Hamming loss values for the 5, 10, 15, and 20 labels are 0.089, 0.065,
0.059, and 0.049, respectively. On average, the number of predictions that can be
made in one minute for the 5, 10, 15, and 20 labels are 6,122; 3,496; 2,374; and
1,220; with a total model size of 191.5MB. |
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