Word Embeddings for Improving REST Services Discoverability

LIZARRALDE, I.; RODRIGUEZ, J. M.; MATEOS, C.; ZUNINO, A.

Cordoba

Simposio; Simposio Latinoamericano de Ingeniería de Software, XLIII Latin American Computing Conference (CLEI 2017); 2017

CLEI & SADIO

Web Services have become essential to the software industry as they provide reusable, remotely-accessible functionality and data, thus accelerating client application development and relieving users from maintaining the consumed services. Since Web Services-particularly their descriptions-must be discovered before being consumed, many discovery approaches based on classic Information Retrieval techniques, which store and process textual service descriptions, have arisen. These discovery approaches are affected by natural language ambiguity, such as synonymy and homonymy. Such issues are known as term mismatch, since descriptions relevant to a keyword-based query can be retrieved only if they share many words. Recently, Word Embeddings emerged and tried to cope with this problem by representing words in a language as vectors in a continuous vector space. An interesting property of these vectors is that two different words with similar meaning are represented by vectors that are close in the space. Word Embeddings aim at categorising and quantifying words so that semantic relationships can be established through simple vector distance measures. In this paper, we exploit Word Embeddings to find hidden relationships between service descriptions and queries for the case of REST services, a recent alternative to SOAP-oriented services. The results showed improvements over classical service retrieval techniques such as Vector Space Model or Latent Semantic Analysis of up to 20% in Precision, 39% in Recall, 35% in F-Measure and 10% in NDCG.