Towards An Assistive and Pattern Learning-driven Process Modeling Approach. Laurenzi, E., Hinkelmann, K., Jüngling, S., Montecchiari, D., Pande, C., & Martin, A. In Martin, A., Hinkelmann, K., Gerber, A., Lenat, D., Harmelen, F., v., & Clark, P., editors, Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), volume 2350, pages 6, 2019. CEUR-WS.org.
![Towards An Assistive and Pattern Learning-driven Process Modeling Approach [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper ![Towards An Assistive and Pattern Learning-driven Process Modeling Approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex
Paper Website abstract bibtex The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.
@inproceedings{
title = {Towards An Assistive and Pattern Learning-driven Process Modeling Approach},
type = {inproceedings},
year = {2019},
pages = {6},
volume = {2350},
websites = {http://ceur-ws.org/Vol-2350},
publisher = {CEUR-WS.org},
city = {Palo Alto, California, USA},
id = {c88775ae-0d21-308d-996f-92506a868b12},
created = {2022-08-22T15:10:35.188Z},
file_attached = {true},
profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
last_modified = {2022-08-22T15:10:44.996Z},
read = {true},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {Laurenzi2019},
private_publication = {false},
abstract = {The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.},
bibtype = {inproceedings},
author = {Laurenzi, Emanuele and Hinkelmann, Knut and Jüngling, Stephan and Montecchiari, Devid and Pande, Charuta and Martin, Andreas},
editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter},
booktitle = {Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)}
}Downloads: 0
{"_id":"CjLuQSfrMn3RMgyz7","bibbaseid":"laurenzi-hinkelmann-jngling-montecchiari-pande-martin-towardsanassistiveandpatternlearningdrivenprocessmodelingapproach-2019","authorIDs":["5948da51b6c4e03e6f00001b","5d944912c357a1ee01000112","8YQnBunpATPezhhEo","Mg4sEQXWRmjCNjzby","RHbMNnQbrSygqyiYa","mhKm4k6iphcKyHCvX","ycMnyBzjK3dYHDSCJ"],"author_short":["Laurenzi, E.","Hinkelmann, K.","Jüngling, S.","Montecchiari, D.","Pande, C.","Martin, A."],"bibdata":{"title":"Towards An Assistive and Pattern Learning-driven Process Modeling Approach","type":"inproceedings","year":"2019","pages":"6","volume":"2350","websites":"http://ceur-ws.org/Vol-2350","publisher":"CEUR-WS.org","city":"Palo Alto, California, USA","id":"c88775ae-0d21-308d-996f-92506a868b12","created":"2022-08-22T15:10:35.188Z","file_attached":"true","profile_id":"e4ed3238-8302-3280-bd05-0b185874fb43","group_id":"18478096-ac37-34c1-8594-c07e3e0a2145","last_modified":"2022-08-22T15:10:44.996Z","read":"true","starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Laurenzi2019","private_publication":false,"abstract":"The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.","bibtype":"inproceedings","author":"Laurenzi, Emanuele and Hinkelmann, Knut and Jüngling, Stephan and Montecchiari, Devid and Pande, Charuta and Martin, Andreas","editor":"Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter","booktitle":"Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)","bibtex":"@inproceedings{\n title = {Towards An Assistive and Pattern Learning-driven Process Modeling Approach},\n type = {inproceedings},\n year = {2019},\n pages = {6},\n volume = {2350},\n websites = {http://ceur-ws.org/Vol-2350},\n publisher = {CEUR-WS.org},\n city = {Palo Alto, California, USA},\n id = {c88775ae-0d21-308d-996f-92506a868b12},\n created = {2022-08-22T15:10:35.188Z},\n file_attached = {true},\n profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},\n group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},\n last_modified = {2022-08-22T15:10:44.996Z},\n read = {true},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Laurenzi2019},\n private_publication = {false},\n abstract = {The practice of business process modeling not only requires modeling expertise but also significant domain expertise. Bringing the latter into an early stage of modeling contributes to design models that appropriately capture an underlying reality. For this, modeling experts and domain experts need to intensively cooperate, especially when the former are not experienced within the domain they are modeling. This results in a time-consuming and demanding engineering effort. To address this challenge we propose a process modeling approach that assists domain experts in the creation and adaptation of process models. To get an appropriate assistance, the approach is driven by semantic patterns and learning. Semantic patterns are domain-specific and consist of process model fragments (or end-to-end process models), which are continuously learned from feedback from domain as well as process modeling experts. This enables to incorporate good practices of process modeling into the semantic patterns. To this end, both machine-learning and knowledge engineering techniques are employed, which allow the semantic patterns to adapt over time and thus to keep up with the evolution of process modeling in the different business domains.},\n bibtype = {inproceedings},\n author = {Laurenzi, Emanuele and Hinkelmann, Knut and Jüngling, Stephan and Montecchiari, Devid and Pande, Charuta and Martin, Andreas},\n editor = {Martin, Andreas and Hinkelmann, Knut and Gerber, Aurona and Lenat, Doug and Harmelen, Frank van and Clark, Peter},\n booktitle = {Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)}\n}","author_short":["Laurenzi, E.","Hinkelmann, K.","Jüngling, S.","Montecchiari, D.","Pande, C.","Martin, A."],"editor_short":["Martin, A.","Hinkelmann, K.","Gerber, A.","Lenat, D.","Harmelen, F., v.","Clark, P."],"urls":{"Paper":"https://bibbase.org/service/mendeley/e4ed3238-8302-3280-bd05-0b185874fb43/file/75460108-ff8d-6659-7bb8-f7d2bebcd078/Laurenzi_et_al___2019___Towards_An_Assistive_and_Pattern_Learning_driven_Process_Modeling_Approach.pdf.pdf","Website":"http://ceur-ws.org/Vol-2350"},"biburl":"https://bibbase.org/service/mendeley/e4ed3238-8302-3280-bd05-0b185874fb43","bibbaseid":"laurenzi-hinkelmann-jngling-montecchiari-pande-martin-towardsanassistiveandpatternlearningdrivenprocessmodelingapproach-2019","role":"author","metadata":{"authorlinks":{"martin, a":"https://bibbase.org/service/mendeley/e4ed3238-8302-3280-bd05-0b185874fb43/group/18478096-ac37-34c1-8594-c07e3e0a2145"}},"downloads":0},"bibtype":"inproceedings","creationDate":"2019-04-26T18:37:38.631Z","downloads":0,"keywords":[],"search_terms":["towards","assistive","pattern","learning","driven","process","modeling","approach","laurenzi","hinkelmann","jüngling","montecchiari","pande","martin"],"title":"Towards An Assistive and Pattern Learning-driven Process Modeling Approach","year":2019,"biburl":"https://bibbase.org/service/mendeley/e4ed3238-8302-3280-bd05-0b185874fb43","dataSources":["sjAoEKginzum6i3Ln","ya2CyA73rpZseyrZ8","jurZeGzSpYdkQ8rm4","cEgnxBqYRFNdjfnYF","za7JCis3wBjciTGFW"]}