Topic-based crossing-workflow fragment discovery

Zhou, Zhangbing; Wen, Jinfeng; Wang, Yasha; Xue, Xiao; Hung, Patrick C. K.; Nguyen, Long D.

doi:10.1016/j.future.2020.05.029

Cited by 8 publications

(8 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the modules' alignment is determined based on the relationship between the predicted value and the predefined threshold. Attribute similarity‐based approach (ASA) 24 : As the most commonly used method for module alignment problems, it calculates the attribute similarity of module pairs based on fundamental attributes to match modules. In our experiment, we adopt the same computation approach introduced in Section 4.2 for a reasonable comparison. Topic model‐based approach (TMA) 36 : This approach quantifies the semantic relevance of modules by their representative topics, which are generated by biterm topic model 37 using the name and description of modules. WordNet‐based approach (WNA) 21 : This method employs WordNet , 38 a lexical database of the English language, to enhance the semantic meanings of words and compute the similarity between them. Specifically, WordNet is used to calculate the similarity of the modules' names, while the xsimilarity algorithm 39 is utilized to compute the similarity of their descriptions.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, some researchers have begun to extract implicit semantic information for alignment 21 . Interoperability of services can be mined from related ontology descriptions, 22,23 but this approach relies on the existence of well‐curated ontologies and authors using these ontologies to tag their modules 24 .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some researchers have begun to extract implicit semantic information for alignment. 21 Interoperability of services can be mined from related ontology descriptions, 22,23 but this approach relies on the existence of well-curated ontologies and authors using these ontologies to tag their modules. 24 To address these limitations, the authors of References 25 and 26 propose a Service Social Knowledge Graph to integrate and manage social information between services and developers.…”

mentioning

confidence: 99%

See 2 more Smart Citations

MANSOR: A module alignment method based on neighbor information for scientific workflow

Cao

Qian

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

MANSOR: A module alignment method based on neighbor information for scientific workflow

Cao

Qian

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Ontology-based behavioural similarity analyses and excerpts information of operating models to gather characteristics enclosed by event logs. Zhou and others [14] Semantic WordNet To encourage the restate or remodel of estate experimental procedures, this research developed a new crossing-workflow fragment discovery technique. BTM generates representative topics that measure the semantic applicability of events and procedure sections.…”

Section: Ontologymentioning

confidence: 99%

A Graph-based Method for Merging Business Process Models by Considering Semantic Similarity

2023

IJIES

View full text Add to dashboard Cite

A process model describes business process flow as the activities that employees must carry out. Nowadays, many companies have similar business processes, so they do not establish their process model from scratch but build the model based on an existing process model or a combination of some process models. Several process mining methods approaches matching rules to define similarities of a model, and others consider the semantic side; however, none use the similarity to merge some business process models. This paper proposed graphbased semantic similarity, a method that merges two process models considering the semantic similarity between those activities. The utilized semantic similarity methods are SBERT and TF-IDF. The evaluations compare SBERT and TF-IDF with other methods and use a similarity method with the highest score in graph-based semantic similarity. Based on the semantic similarity score, graph-based semantic similarity with SBERT has higher similarity scores than existing graph-based semantic similarity, i.e., node similarity and Jaro-Winkler distance. With the highest similarity scores among existing methods, the evaluations also prove that graph-based semantic similarity with SBERT correctly combines business process models based on semantic similarity.

show abstract

“…The discovery of workflow fragments is closely related to service discovery, which exploits: Web Service Decription Language-based keyword search, semantic matching based on domain knowledge or ontologies, context awareness, or Quality of Service (QoS)-based discovery, and machine learning techniques adopted to examine service relevance. ZhangBing Zhou et al proposed in paper [2] "a novel crossing workflow fragment discovery mechanism, where an activity knowledge graph is constructed to capture flat invocation relations between activities, and hierarchical parent-child relations specified upon sub-workflows and their corresponding activities". Semantic relevance of activities and sub-workflows is calculated based on their representative topics.…”

Section: Topic-based Crossing-workflow Fragment Discoverymentioning

confidence: 99%