Abstract:
This Paper is an attempt to develop an efficient and comprehensive approach to web service discovery and retrieving APIs
from vast repositories based on user queries and requirements and ranking the results based on relevance. Our strategy to do so
incorporates numerous techniques like semantic search, graph-based ranking, and relevance scoring. Our module applies semantic
expansion on user queries the moment they are received, through WordNet to improve query representation. The next step is to
vectorize the expanded query through TF-IDF, which facilitates semantic similarity computation with the web services available. The
semantic similarity scores are then studied with the help of a graph where the edges are semantic similarity scores and the nodes
represent web services. Importance scores are then given to each web service on this graph with the help of PageRank, helping us
understand the relevance of the web services. Not just this, the Okapi BM25 algorithm is also applied to compute the relevance score.
The final ranking of the web service is given on the basis of integrated scores of Okapi BM25 and PageRank. This ranked list is
finally presented to the user. With the help of our module and the approach it follows, users can navigate through vast repositories
full of APIs to find the most relevant API for their use. Through the approach followed by us, web service discovery and ranking
becomes easier even for people without a lot of experience and hence it offers a strong and effective solution to web service
discovery and can be applied in multiple domains.
