A probabilistic evaluation procedure for process model matching techniques

One of the key tasks in the service oriented architecture that Semantic Web services aim to automate is the discovery of services that can fulfill the applications or user needs. OWL-S is one of the proposals for describing semantic metadata about Web services, which is based on the OWL ontology language. Majority of current approaches for matching OWL-S processes take into account only the inputs/outputs service profile. This chapter argues that, in many situations the service matchmaking should take into account also the process model. We present matching techniques that operate on OWL-S process models and allow retrieving in a given repository, the processes most similar to the query. To do so, the chapter proposes to reduce the problem of process matching to a graph matching problem and to adapt existing algorithms for this purpose. It proposes a similarity measure used to rank the discovered services. This measure captures differences in process structure and semantic differences between input/outputs used in the processes.

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A Novel Approach for Business Process Model Matching Using Genetic Algorithms

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2020010101 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1-19

Author(s):

Mostefai Abdelkader ◽

Ignacio García Rodríguez de Guzmán

Keyword(s):

Genetic Algorithms ◽

Business Process ◽

Process Model ◽

Optimization Problem ◽

Process Models ◽

Similarity Metrics ◽

Model Matching ◽

Matching Problem ◽

Novel Approach ◽

The One

This paper formulates the process model matching problem as an optimization problem and presents a heuristic approach based on genetic algorithms for computing a good enough alignment. An alignment is a set of not overlapping correspondences (i.e., pairs) between two process models(i.e., BP) and each correspondence is a pair of two sets of activities that represent the same behavior. The first set belongs to a source BP and the second set to a target BP. The proposed approach computes the solution by searching, over all possible alignments, the one that maximizes the intra-pairs cohesion while minimizing inter-pairs coupling. Cohesion of pairs and coupling between them is assessed using a proposed heuristic that combines syntactic and semantic similarity metrics. The proposed approach was evaluated on three well-known datasets. The results of the experiment showed that the approach has the potential to match business process models effectively.

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Improving Effectiveness of Process Model Matchers Using Wordnet Glosses

International Journal of Systems and Software Security and Protection ◽

10.4018/ijsssp.2019070101 ◽

2019 ◽

Vol 10 (2) ◽

pp. 1-19

Author(s):

Mostefai Abdelkader

Keyword(s):

Business Process ◽

Process Model ◽

Business Process Management ◽

Process Management ◽

Cosine Similarity ◽

Process Models ◽

Model Matching ◽

Similarity Metric

Process model matching is a key activity in many business process management tasks. It is an activity that consists of detecting an alignment between process models by finding similar activities in two process models. This article proposes a method based on WordNet glosses to improve the effectiveness of process model matchers. The proposed method is composed of three steps. In the first step, all activities of the two BPs are extracted. Second, activity labels are expanded using word glosses and finally, similar activities are detected using the cosine similarity metric. Two experiments were conducted on well-known datasets to validate the effectiveness of the proposed approach. In the first one, an alignment is computed using the cosine similarity metric only and without a process of expansion. While, in the second experiment, the cosine similarity metric is applied to the expanded activities using glosses. The results of the experiments were promising and show that expanding activities using WordNet glosses improves the effectiveness of process model matchers.

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