Extraction and processing of procedural experiential knowledge in workflows - quality, interactivity, and transferability
Final Report Abstract
In the EVER project we investigated new methods in Process-Oriented Case-Based Reasoning (POCBR) and related fields for extracting, representing, and processing procedural experiential knowledge. The knowledge can be represented in a semi-structured manner such as in maintenance instructions or cooking recipes or it can be represented in a more structured form such as in business process models or in executable scientific workflows. During the first funding period of this project, we focused on the extraction of workflows from textual sources, the similarity-based retrieval of workflows for a particular goal of a user, and the automatic adaptation of workflows based on automatically learned adaptation knowledge as the core of automated reasoning tasks. Within the second funding period we extended our work regarding interactivity and we explored more complex workflow domains. In preparation for this, we have further tested the adaptation. We investigated whether the methods are suitable for adequately fulfilling multi-objective queries such as (i) controlling the complexity of generated workflows with desired structure and (ii) considering more complex queries with additional constraints on overall workflow properties. Concerning the interactivity in POCBR, we developed an interactive retrieval approach. The approach makes use of the automatically acquired adaptation knowledge. As a new application domain, we investigated scientific workflows for data mining with a particular focus put on the potentials for digital humanities research. We further developed the adaptation methods for applying them to scientific workflows and addressed the issue of efficient retrieval of workflows that we particularly faced with increased complexity of workflows. Based on our adaptation methods, we developed novel, ontology-based transfer learning methods for procedural knowledge that aim to adapt workflows across the boundaries of application domains. In particular, we contributed with automatic transformation of BPMN workflows into an ontology and utilization of the transfer strategy with the methods of analogical substitution, abstraction, and generalization. We used a knowledge discovery approach based on word embeddings to enrich the ontology by analogical knowledge between workflow tasks. We also operationalised the measurement of transfer distance. Surprisingly, the transfer distance seems to be more difficult to determine for POCBR than for traditional transfer learning approaches based on machine learning. It depends on the content of the workflow repository (data), the ontology (model of the transfer knowledge) and the transfer strategy (transfer process). The costs are calculated post mortem using the edit traces of adaptation operators. We elaborated different transfer strategies to guide the transfer process and implemented a transfer framework for POCBR. Experiments in the application domains passenger and baggage handling at the airport and SAP warehouse management confirmed that transfer learning of workflows - like ”normal” adaptation within one application domain - requires interactivity. Last but not least, we combined the retrieval and adaptation methods in a conversational POCBR approach for large repositories of complex workflows. There are various application domains for applying the methods developed in our work. For instance, we investigate use cases to enhance workflow flexibility in cyber-physical production systems in Industry 4.0. Another promising application domain are argumentation machines to support the synthesis and deliberation of arguments represented as graphs. The project results are of high interest for the data science platforms, also for commercial vendors of such platforms. Together with software partners and application partners, we are currently investigating the transfer of the project results into such a platform.
Publications
- “On the Transferability of Process-Oriented Cases”. In: Case-Based Reasoning Research and Development - 24th International Conference, ICCBR 2016, Proceedings. Vol. 9969. LNCS. Springer, 2016, pp. 281–294
Mirjam Minor, Ralph Bergmann, Jan-Martin Müller, and Alexander Spät
(See online at https://doi.org/10.1007/978-3-319-47096-2_19) - “Complexity-Aware Generation of Workflows by Process- Oriented Case-Based Reasoning”. In: KI 2017: Advances in Artificial Intelligence - 40th Annual German Conference on AI, 2017, Proceedings. Vol. 10505. LNCS. Springer, 2017, pp. 207–221
Gilbert Müller and Ralph Bergmann
(See online at https://doi.org/10.1007/978-3-319-67190-1_16) - “Conversational Process-Oriented Case-Based Reasoning”. In: Case-Based Reasoning Research and Development - 25th International Conference, ICCBR 2017, Proceedings. LNCS. Springer, 2017, pp. 403–419
Christian Zeyen, Gilbert Müller, and Ralph Bergmann
(See online at https://doi.org/10.1007/978-3-319-61030-6_28) - “Query Model and Similarity-Based Retrieval for Workflow Reuse in the Digital Humanities”. In: Proceedings of “Lernen, Wissen, Daten, Analysen”, LWDA 2018. Vol. 2191. CEUR Workshop Proceedings. CEUR-WS.org, 2018, pp. 251–262
Lukas Malburg, Nicolas Münster, Christian Zeyen, and Ralph Bergmann
- “Adaptation of Scientific Workflows by Means of Process-Oriented Case-Based Reasoning”. In: Case-Based Reasoning Research and Development: 27th International Conference, ICCBR 2019, Proceedings. LNCS. Springer, 2019, pp. 388–403
Christian Zeyen, Lukas Malburg, and Ralph Bergmann
(See online at https://doi.org/10.1007/978-3-030-29249-2_26) - “A*-based Similarity Assessment of Semantic Graphs”. In: Case-Based Reasoning Research and Development: 28th International Conference, ICCBR 2020, Proceedings. LNCS. Springer, 2020
Christian Zeyen and Ralph Bergmann
(See online at https://doi.org/10.1007/978-3-030-58342-2_2) - “Discovery of Analogical Knowledge for the Transfer of Workflow Tasks”. In: Artificial Intelligence & Knowledge Engineering (AIKE 2020). IEEE Computer Society Press, 2020, pp. 77–83
Mirjam Minor and Miriam Herold
(See online at https://doi.org/10.1109/AIKE48582.2020.00020) - “Using Siamese Graph Neural Networks for Similarity-Based Retrieval in Process-Oriented Case-Based Reasoning”. In: Case-Based Reasoning Research and Development: 28th International Conference, ICCBR 2020, Proceedings. Vol. 12311. LNCS. Springer, 2020, pp. 229–244
Maximilian Hoffmann, Lukas Malburg, Patrick Klein, and Ralph Bergmann
(See online at https://doi.org/10.1007/978-3-030-58342-2_15) - “Ontology-based Transfer Learning in the Airport and Warehouse Logistics Domains”. In: Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE). Vol. 2846. CEUR Workshop Proceedings. CEUR-WS.org, 2021
Miriam Herold and Mirjam Minor
- “Transfer Learning Operators for Process-oriented Cases”. In: Proc. 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering AIKE 2021. IEEE Computer Society Press, 2021, pp. 9–16
Mirjam Minor, Miriam Herold, Julius Rubbe, Stefan Dufner, and Georgios Brussas
(See online at https://doi.org/10.1109/AIKE52691.2021.00008)
