A word-embedding-based approach for accurate identification of corresponding activities. (September 2019)

• Record Type: Journal Article
• Authors: Shahzad, Khurram; Kanwal, Safia; Malik, Kamran; Aslam, Faisal; Ali, Muhammad
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 218
• Page End: 229
• Publication Date: 2019-09
• Subjects: Software Engineering -- Smart Process Repositories -- Machine Learning -- Deep Learning -- Process Model Matching -- First-line Matcher; 00-01 -- 99-00; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.07.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Smart process repositories have been developed to effectively manage large collections of business process models. Several key features of such repositories require accurate matching of corresponding activities between a pair of process models. However, the existing matching techniques have not yet achieved the desired accuracy, which impedes the effectiveness of process repositories. To that end, this paper proposes a word-embedding-based approach that significantly improves the accuracy of matching. For a comprehensive evaluation of the proposed approach, we have performed experiments using three state-of-the-art word embeddings, two syntactic measures, six semantic measures, and four datasets. The results show that the use of word embeddings outperforms all the syntactic as well as the semantic similarity measures. Moreover, the use of fastText-based embeddings in our proposed technique achieves the highest F1 score, compared to both Word2vec- and GloVe-based embeddings.