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LS3: Latent Semantic Analysis-based Similarity Search for Process Models

dc.contributor.authorSchoknecht, Andreas
dc.contributor.authorOberweis, Andreas
dc.date.accessioned2019-02-10T22:43:05Z
dc.date.available2019-02-10T22:43:05Z
dc.date.issued2017
dc.description.abstractLarge process model collections in use today contain hundreds or even thousands of conceptual process models. Search functionalities can help in handling such large collections for purposes such as duplicate detection or reuse of models. One popular stream of search functionalities is similarity-based search which utilizes similarity measures for finding similar models in a large collection. Most of these approaches base on an underlying alignment between the activities of the compared process models. Yet, such an alignment seems to be quite difficult to achieve according to the results of the Process Model Matching contests conducted in recent years. Therefore, the Latent Semantic Analysis-based Similarity Search (LS3) technique presented in this article does not rely on such an alignment, but uses a Latent Semantic Analysis-based similarity measure for retrieving similar models. An evaluation with 138 real-life process models shows a strong performance in terms of Precision, Recall, F-Measure, R-Precision and Precision-at-k, thereby outperforming five other techniques for similarity-based search. Additionally, the run time of the LS3 query calculation is significantly faster than any of the other approaches.en
dc.identifier.doi10.18417/emisa.12.2
dc.identifier.pissn1866-3621
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/20085
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofEnterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 12, Nr. 2
dc.subjectprocess model querying
dc.subjectsimilarity-based search
dc.subjectprocess model search
dc.subjectlatent semantic analysis
dc.titleLS3: Latent Semantic Analysis-based Similarity Search for Process Modelsen
dc.typeText/Journal Article
gi.citation.endPage24
gi.citation.publisherPlaceBerlin
gi.citation.startPage1

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