@misc{
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@article{
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@misc{
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This is the opening talk of the AAAI Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering.

@inproceedings{
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@inproceedings{
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@inproceedings{
 title = {Towards Hybrid Dialog Management Strategies for a Health Coach Chatbot},
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@article{
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 abstract = {Die „Boxologie“ ist die grafische Darstellung von Mustern, die häufig in hybriden Lern- und Reasoning-Systemen zu beobachten sind. Da einige hybride Systeme auch Menschen einbeziehen, besteht die Notwendigkeit, Muster mit menschlicher Beteiligung zu identifizieren. Anhand von Anwendungsfällen, an denen Menschen beteiligt sind, werden in diesem Kapitel deren typische Rollen in hybriden Systemen und deren Einfluss auf maschinelles Lernen und/oder Knowledge-Engineering-Aktivitäten diskutiert. Infolgedessen wird ein neues Element in die Boxologie eingeführt, um einen Menschen zu repräsentieren und zwei abstrakte Muster für solche Human-in-the-Loop-Szenarien zu identifizieren.},
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 chapter = {Visualisierung von Mustern für hybrides Lernen und Reasoning mit menschlicher Beteiligung},
 title = {Neue Trends in Wirtschaftsinformatik und eingesetzte Technologien}
}

Die „Boxologie“ ist die grafische Darstellung von Mustern, die häufig in hybriden Lern- und Reasoning-Systemen zu beobachten sind. Da einige hybride Systeme auch Menschen einbeziehen, besteht die Notwendigkeit, Muster mit menschlicher Beteiligung zu identifizieren. Anhand von Anwendungsfällen, an denen Menschen beteiligt sind, werden in diesem Kapitel deren typische Rollen in hybriden Systemen und deren Einfluss auf maschinelles Lernen und/oder Knowledge-Engineering-Aktivitäten diskutiert. Infolgedessen wird ein neues Element in die Boxologie eingeführt, um einen Menschen zu repräsentieren und zwei abstrakte Muster für solche Human-in-the-Loop-Szenarien zu identifizieren.

@inbook{
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}

We present a model based on coaching definitions, concepts, and theories to support AI coaching. The model represents the evidence-based coaching practice in different coaching domains by identifying the common elements in the coaching process. We then map the elements of the coaching model with Conversational AI design and development strategies to highlight how an AI coach can be instantiated from the model. We showcase the instantiation through an example use case of an HIV coaching chatbot.

@misc{
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 year = {2022},
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@article{
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Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided, e.g., in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows incorporating recommender systems into them. We suggest and compare several recommender techniques that allow incorporating the necessary contextual knowledge (e.g., company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to substantial performance improvement over the individual methods.

@inproceedings{
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@inproceedings{
 title = {A Learning Tracker using Digital Biomarkers for Autistic Preschoolers},
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@article{
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 journal = { Interactive AI Magazine, Association for the Advancement of Artificial Intelligence (AAAI)}
}

@inproceedings{
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and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21-23, 2022},
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@article{
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@article{
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Besides the typical applications of recommender systems in B2C scenarios such as movie or shopping platforms, there is a rising interest in transforming the human-driven advice provided e.g. in consultancy via the use of recommender systems. We explore the special characteristics of such knowledge-based B2B services and propose a process that allows to incorporate recommender systems into them. We suggest and compare several recommender techniques that allow to incorporate the necessary contextual knowledge (e.g. company demographics). These techniques are evaluated in isolation on a test set of business intelligence consultancy cases. We then identify the respective strengths of the different techniques and propose a new hybridisation strategy to combine these strengths. Our results show that the hybridisation leads to a substantial performance improvement over the individual methods.

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 editor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and van Harmelen, Frank},
 booktitle = {Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)}
}

@inproceedings{
 title = {Hybrid Conversational AI for Intelligent Tutoring Systems},
 type = {inproceedings},
 year = {2021},
 keywords = {Conversational AI,Intelligent tutoring systems,Problem-based learning,Project-based learning},
 volume = {2846},
 websites = {http://ceur-ws.org/Vol-2846},
 publisher = {CEUR-WS.org},
 city = {Palo Alto, California, USA},
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 abstract = {We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student's understanding of an assignment's cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.},
 bibtype = {inproceedings},
 author = {Pande, Charuta and Witschel, H.F. Hans Friedrich and Martin, Andreas and Montecchiari, Devid},
 editor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and van Harmelen, Frank},
 booktitle = {Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)}
}

We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student's understanding of an assignment's cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.

@inbook{
 type = {inbook},
 year = {2021},
 pages = {193-204},
 websites = {https://doi.org/10.1007/978-3-030-48332-6_13},
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 abstract = {``Boxology'' is the graphical representation of patterns that are commonly observed in hybrid learning and reasoning systems. Since some hybrid systems also involve humans-in-the-loop, a need to identify patterns including humans is foreseen. With the help of use cases that involve humans-in-the-loop, this chapter provides a discussion on the typical roles performed by humans in hybrid systems and how they influence machine learning and/or knowledge engineering activities. As a result, it introduces a new element in boxology to represent a human and identify two abstract patterns for such human-in-the-loop scenarios.},
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 author = {Witschel, Hans Friedrich and Pande, Charuta and Martin, Andreas and Laurenzi, Emanuele and Hinkelmann, Knut},
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 chapter = {Visualization of Patterns for Hybrid Learning and Reasoning with Human Involvement},
 title = {New Trends in Business Information Systems and Technology: Digital Innovation and Digital Business Transformation}
}

``Boxology'' is the graphical representation of patterns that are commonly observed in hybrid learning and reasoning systems. Since some hybrid systems also involve humans-in-the-loop, a need to identify patterns including humans is foreseen. With the help of use cases that involve humans-in-the-loop, this chapter provides a discussion on the typical roles performed by humans in hybrid systems and how they influence machine learning and/or knowledge engineering activities. As a result, it introduces a new element in boxology to represent a human and identify two abstract patterns for such human-in-the-loop scenarios.

@inproceedings{
 title = {ChEdventure - A Chatbot-based Educational Adventure Game for Modeling Tasks in Information Systems},
 type = {inproceedings},
 year = {2021},
 keywords = {Chatbot,Gamication,Information Systems},
 websites = {https://conversations2021.wordpress.com/},
 id = {36535359-0023-3c70-b783-cc2e833fc032},
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 abstract = {Practitioners in business information systems are frequently faced with tasks that involve interpretation and representation of organizational information as models, e.g. business processes, the involved participants, and data. Usually, this information comes from varied sources like stakeholders and documents, often resulting in subjective, biased, or incomplete information. Simulating a realistic organizational environment is important for the education of future business process experts, but challenging to achieve in the educational setting. In this work, we propose a chatbot-based educational adventure game that can introduce complex and often contradictory sources of information in a fun learning approach and help the students in abstracting and interpreting this information, constructing models as an outcome. We elaborate on the rst design ideas and requirements.},
 bibtype = {inproceedings},
 author = {Pande, Charuta and Witschel, Hans Friedrich and Martin, Andreas},
 booktitle = {CONVERSATIONS 2021 – the 5th International Workshop on Chatbot Research}
}

Practitioners in business information systems are frequently faced with tasks that involve interpretation and representation of organizational information as models, e.g. business processes, the involved participants, and data. Usually, this information comes from varied sources like stakeholders and documents, often resulting in subjective, biased, or incomplete information. Simulating a realistic organizational environment is important for the education of future business process experts, but challenging to achieve in the educational setting. In this work, we propose a chatbot-based educational adventure game that can introduce complex and often contradictory sources of information in a fun learning approach and help the students in abstracting and interpreting this information, constructing models as an outcome. We elaborate on the rst design ideas and requirements.

@inproceedings{
 title = {Proceedings of the AAAI 2021 Spring Symposium on Combining Machine
Learning and Knowledge Engineering (AAAI-MAKE 2021), Stanford University,
Palo Alto, California, USA, March 22-24, 2021},
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 year = {2021},
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@inproceedings{
 title = {ArchiMEO: A Standardized Enterprise Ontology based on the ArchiMate Conceptual Model},
 type = {inproceedings},
 year = {2020},
 keywords = {ArchiMate,Enterprise Architecture,Enterprise Modeling,Enterprise Ontology},
 pages = {417-424},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0009000204170424},
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 abstract = {Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.},
 bibtype = {inproceedings},
 author = {Hinkelmann, Knut and Laurenzi, Emanuele and Martin, Andreas and Montecchiari, Devid and Spahic, Maja and Thönssen, Barbara},
 doi = {10.5220/0009000204170424},
 booktitle = {Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development}
}

Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.

@inproceedings{
 title = {Towards AI-based Solutions in the System Development Lifecycle},
 type = {inproceedings},
 year = {2020},
 pages = {6},
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 editor = {Martin, Andreas and Hinkelmann, Knut and Fill, Hans-Georg and Gerber, Aurona and Lenat, Doug and Stolle, Reinhard and van Harmelen, Frank},
 booktitle = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) - Volume I}
}

@inproceedings{
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 booktitle = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) - Volume I}
}

@inproceedings{
 title = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine
Learning and Knowledge Engineering in Practice, AAAI-MAKE 2020, Palo Alto, CA, USA, March 23-25, 2020, Volume I},
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 year = {2020},
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@inproceedings{
 title = {Learning and Engineering Similarity Functions for Business Recommenders},
 type = {inproceedings},
 year = {2019},
 pages = {6},
 volume = {2350},
 websites = {http://ceur-ws.org/Vol-2350},
 publisher = {CEUR-WS.org},
 city = {Palo Alto, California, USA},
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 abstract = {We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedbacks. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.},
 bibtype = {inproceedings},
 author = {Witschel, H.F. Hans Friedrich 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)}
}

We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedbacks. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.

@article{
 title = {Reports of the AAAI 2019 Spring Symposium Series},
 type = {article},
 year = {2019},
 pages = {59-66},
 volume = {40},
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 abstract = {The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.},
 bibtype = {article},
 author = {Baldini, Ioana and Barrett, Clark and Chella, Antonio and Cinelli, Carlos and Gamez, David and Gilpin, Leilani and Hinkelmann, Knut and Holmes, Dylan and Kido, Takashi and Kocaoglu, Murat and Lawless, William and Lomuscio, Alessio and Macbeth, Jamie and Martin, Andreas and Mittu, Ranjeev and Patterson, Evan and Sofge, Donald and Tadepalli, Prasad and Takadama, Keiki and Wilson, Shomir},
 doi = {10.1609/aimag.v40i3.5181},
 journal = {AI Magazine},
 number = {3}
}

The AAAI 2019 Spring Series was held Monday through Wednesday, March 25–27, 2019 on the campus of Stanford University, adjacent to Palo Alto, California. The titles of the nine symposia were Artificial Intelligence, Autonomous Machines, and Human Awareness: User Interventions, Intuition and Mutually Constructed Context; Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI; Combining Machine Learning with Knowledge Engineering; Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness; Privacy- Enhancing Artificial Intelligence and Language Technologies; Story-Enabled Intelligence; Towards Artificial Intelligence for Collaborative Open Science; Towards Conscious AI Systems; and Verification of Neural Networks.

@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},
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 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)}
}

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 = {Preface: Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)},
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 year = {2019},
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 booktitle = {Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)}
}

@article{
 title = {The Conversational AI Life-Cycle},
 type = {article},
 year = {2019},
 keywords = {Chatbot Development,Conversational AI,Conversational Design,Dialogue Systems},
 websites = {https://zenodo.org/record/7991800},
 month = {4},
 day = {23},
 id = {aa053aec-6b30-3667-a716-c21f063911ea},
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}

@inproceedings{
 title = {Proceedings of the AAAI 2019 Spring Symposium on Combining Machine
Learning with Knowledge Engineering (AAAI-MAKE 2019), Stanford University, Palo Alto, California, USA, March 25-27, 2019},
 type = {inproceedings},
 year = {2019},
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@inproceedings{
 title = {Training and Re-using Human Experience: A Recommender for More Accurate Cost Estimates in Project Planning},
 type = {inproceedings},
 year = {2018},
 keywords = {Case-based Reasoning,Case-based reasoning,Effort Estimation,Effort estimation,Experience Management,Experience management,Machine Learning,Machine learning},
 pages = {52-62},
 volume = {3},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006893200520062},
 publisher = {SCITEPRESS - Science and Technology Publications},
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 authored = {false},
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 hidden = {false},
 citation_key = {kmis18},
 source_type = {conference},
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 abstract = {In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), “explains” derived si milarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.},
 bibtype = {inproceedings},
 author = {Von Rohr, Christian Rudolf and Witschel, Hans Friedrich and Martin, Andreas},
 doi = {10.5220/0006893200520062},
 booktitle = {Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}
}

In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), “explains” derived si milarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.

@inproceedings{
 title = {Random Walks on Human Knowledge: Incorporating Human Knowledge into Data-Driven Recommenders},
 type = {inproceedings},
 year = {2018},
 keywords = {Knowledge Representation,Knowledge representation,Random Walks,Random walks,Recommender Systems,Recommender systems},
 pages = {63-72},
 volume = {3},
 websites = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006893900630072},
 publisher = {SCITEPRESS - Science and Technology Publications},
 institution = {INSTICC},
 id = {72925453-d93e-3c6f-8ab0-4ccb29f68c3d},
 created = {2022-08-22T15:10:35.131Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
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 last_modified = {2022-08-22T15:10:44.264Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {kmis18},
 source_type = {conference},
 private_publication = {false},
 abstract = {We explore the use of recommender systems in business scenarios such as consultancy. In these situations, apart from personal preferences of users, knowledge about objective business-driven criteria plays a role. We investigate strategies for representing and incorporating such knowledge into data-driven recommenders. As a baseline, we choose a robust and flexible paradigm that is based on a simple graph-based representation of past customer cases and choices, in combination with biased random walks. On a real data set from a business intelligence consultancy firm, we study how the incorporation of two important types of explicit human knowledge – namely taxonomic and associative knowledge – impacts the effectiveness of a data-driven recommender. Our results show no consistent improvement for taxonomic knowledge, but quite substantial and significant gains when using associative knowledge.},
 bibtype = {inproceedings},
 author = {Witschel, H.F. Hans Friedrich and Martin, Andreas},
 doi = {10.5220/0006893900630072},
 booktitle = {Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}
}

We explore the use of recommender systems in business scenarios such as consultancy. In these situations, apart from personal preferences of users, knowledge about objective business-driven criteria plays a role. We investigate strategies for representing and incorporating such knowledge into data-driven recommenders. As a baseline, we choose a robust and flexible paradigm that is based on a simple graph-based representation of past customer cases and choices, in combination with biased random walks. On a real data set from a business intelligence consultancy firm, we study how the incorporation of two important types of explicit human knowledge – namely taxonomic and associative knowledge – impacts the effectiveness of a data-driven recommender. Our results show no consistent improvement for taxonomic knowledge, but quite substantial and significant gains when using associative knowledge.

@inbook{
 type = {inbook},
 year = {2018},
 pages = {177-194},
 websites = {https://doi.org/10.1007/978-3-319-74322-6_12},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {892899b3-dfef-3d31-b20f-5db4db7fe538},
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 read = {true},
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 hidden = {false},
 citation_key = {Hinkelmann2018},
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 private_publication = {false},
 abstract = {Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.},
 bibtype = {inbook},
 author = {Hinkelmann, Knut and Laurenzi, Emanuele and Martin, Andreas and Thönssen, Barbara},
 editor = {Dornberger, Rolf},
 doi = {10.1007/978-3-319-74322-6_12},
 chapter = {Ontology-Based Metamodeling},
 title = {Business Information Systems and Technology 4.0: New Trends in the Age of Digital Change}
}

Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.

@inbook{
 type = {inbook},
 year = {2018},
 pages = {47-63},
 websites = {https://doi.org/10.1007/978-3-319-74322-6_4},
 publisher = {Springer International Publishing},
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 citation_key = {Martin2018},
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 abstract = {The following chapter describes an integrated case-based reasoning (CBR) approach to process learning and experience management. This integrated CBR approach reflects domain knowledge and contextual information based on an enterprise ontology. The approach consists of a case repository, which contains experience items described using a specific case model. The case model reflects, on the one hand, the process logic, i.e. the flow of work, and on the other the business logic, which is the knowledge that can be used to achieve a result.},
 bibtype = {inbook},
 author = {Martin, Andreas and Hinkelmann, Knut},
 editor = {Dornberger, Rolf},
 doi = {10.1007/978-3-319-74322-6_4},
 chapter = {Case-Based Reasoning for Process Experience},
 title = {Business Information Systems and Technology 4.0: New Trends in the Age of Digital Change}
}

The following chapter describes an integrated case-based reasoning (CBR) approach to process learning and experience management. This integrated CBR approach reflects domain knowledge and contextual information based on an enterprise ontology. The approach consists of a case repository, which contains experience items described using a specific case model. The case model reflects, on the one hand, the process logic, i.e. the flow of work, and on the other the business logic, which is the knowledge that can be used to achieve a result.

@article{
 title = {A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management},
 type = {article},
 year = {2017},
 keywords = {case-based reasoning,enterprise architecture,enterprise ontology,experience management,ontology-based case-based reasoning,similarity,viewpoint},
 pages = {551-575},
 volume = {11},
 websites = {http://dx.doi.org/10.1080/17517575.2016.1161239},
 month = {4},
 day = {21},
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 read = {true},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2017},
 private_publication = {false},
 abstract = {The accessibility of project knowledge that is obtained from experiences is an important and crucial issue in enterprises. Therefore, we introduce an ontology-based case-based reasoning approach that utilises an enterprise ontology, which is based on an enterprise architecture. Utilising this enterprise ontology enables improvements of the case-based reasoning system through the systematic inclusion of enterprise-specific knowledge. This enterprise- specific knowledge is captured in the enterprise ontology named ArchiMEO, an ontological realisation of the enterprise architecture framework ArchiMate and others. This ontology, containing historical cases and specific enterprise domain knowledge, is applied in the new ontology-based case-based reasoning approach. Apart from that, we observed that different people in different roles have their own information needs. Therefore, we enhanced our ontology-based case-based reasoning approach in a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach is implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.},
 bibtype = {article},
 author = {Martin, Andreas and Emmenegger, Sandro and Hinkelmann, Knut and Thönssen, Barbara},
 doi = {10.1080/17517575.2016.1161239},
 journal = {Enterprise Information Systems},
 number = {4}
}

The accessibility of project knowledge that is obtained from experiences is an important and crucial issue in enterprises. Therefore, we introduce an ontology-based case-based reasoning approach that utilises an enterprise ontology, which is based on an enterprise architecture. Utilising this enterprise ontology enables improvements of the case-based reasoning system through the systematic inclusion of enterprise-specific knowledge. This enterprise- specific knowledge is captured in the enterprise ontology named ArchiMEO, an ontological realisation of the enterprise architecture framework ArchiMate and others. This ontology, containing historical cases and specific enterprise domain knowledge, is applied in the new ontology-based case-based reasoning approach. Apart from that, we observed that different people in different roles have their own information needs. Therefore, we enhanced our ontology-based case-based reasoning approach in a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach is implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.

@inbook{
 type = {inbook},
 year = {2017},
 keywords = {Case-based reasoning,Ontology supported learning,Ontology-based Case-based reasoning,Personalized learning,Public administration,Recommender system,Workplace learning},
 pages = {23},
 volume = {692},
 websites = {https://doi.org/10.1007/978-3-319-66302-9_17},
 publisher = {Springer Berlin / Heidelberg},
 id = {aea0b76e-733d-387d-ae6a-4a0d1dcd078a},
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 hidden = {false},
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 abstract = {The support of workplace learning is increasingly relevant as the change in every form determines today's working world in the industry and public administrations alike. Adapting quickly to a new job, a new task or a new team is a significant challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it is aligned with business goals. Our approach supports workplace learning by suggesting historical cases and providing recommendations of experts and learning resources. We utilize users' workplace environment, we consider their learning preferences, provide them with useful prior lessons, and compare required and acquired competencies to issue the best-suited recommendations. Our research work follows a Design Science Research strategy and is part of the European funded project Learn PAd. The recommender system introduced here is evaluated in an iterative manner, first by comparing it to previously elicited user requirements and then through practical application in a test process conducted by the project application partner},
 bibtype = {inbook},
 author = {Emmenegger, Sandro and Hinkelmann, Knut and Laurenzi, Emanuele and Martin, Andreas and Thönssen, Barbara and Witschel, H.F. Hans Friedrich and Zhang, Congyu},
 doi = {978-3-319-66302-9_17},
 chapter = {An Ontology-based and Case-based Reasoning supported Workplace Learning Approach},
 title = {Model-Driven Engineering and Software Development, Communications in Computer and Information Science (CCIS), In Press}
}

The support of workplace learning is increasingly relevant as the change in every form determines today's working world in the industry and public administrations alike. Adapting quickly to a new job, a new task or a new team is a significant challenge that must be dealt with ever faster. Workplace learning differs significantly from school learning as it is aligned with business goals. Our approach supports workplace learning by suggesting historical cases and providing recommendations of experts and learning resources. We utilize users' workplace environment, we consider their learning preferences, provide them with useful prior lessons, and compare required and acquired competencies to issue the best-suited recommendations. Our research work follows a Design Science Research strategy and is part of the European funded project Learn PAd. The recommender system introduced here is evaluated in an iterative manner, first by comparing it to previously elicited user requirements and then through practical application in a test process conducted by the project application partner

@phdthesis{
 title = {A combined Case-Based Reasoning and Process Execution Approach for Knowledge-Intensive Work},
 type = {phdthesis},
 year = {2016},
 keywords = {business process management,case-based reasoning,enterprise architecture,enterprise ontology,experience management,knowledge work,knowledge-intensive processes,ontology-based case-based reasoning,process flexibility,semantic web,workflow systems},
 pages = {339},
 websites = {http://hdl.handle.net/10500/22796},
 institution = {University of South Africa},
 department = {College of Science, Engineering and Technology},
 id = {4d753d8a-ba26-3e5d-8cd2-2f5e64fe08ef},
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 read = {true},
 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2016},
 user_context = {PhD Thesis},
 private_publication = {false},
 abstract = {Knowledge and knowledge work are key factors of today’s successful companies. This study devises an approach for increasing the performance of knowledge work by shifting it towards a process orientation. Business process management and workflow management are methods for structured and predefined work but are not flexible enough to support knowledge work in a comprehensive way. Case-based reasoning (CBR) uses the knowledge of previously experienced cases in order to propose a solution to a problem. CBR can be used to retrieve, reuse, revise, retain and store functional and process knowledge. The aim of the research was to develop an approach that combines CBR and process execution to improve knowledge work. The research goals are: a case description for knowledge work that can be integrated into a process execution system and that contains both functional and process knowledge; a similarity algorithm for the retrieval of functional and procedural knowledge; and an adaptation mechanism that deals with the different granularities of solution parts. This thesis contains a profound literature framework and follows a design science research (DSR) strategy. During the awareness phase of the design science research process, an application scenario was acquired using the case study research method, which is the admission process for a study programme at a university. This application scenario is used to introduce and showcase the combined CBR and process execution approach called ICEBERG-PE, which consists of a case model and CBR services. The approach is implemented as a prototype and can be instantiated using the ICEBERG-PE procedure model, a specific procedure model for ontology-based, CBR projects. The ICEBERG-PE prototype has been evaluated using triangulated evaluation data and different evaluation settings to confirm that the approach is transferable to other contexts. Finally, this thesis concludes with potential recommendations for future research.},
 bibtype = {phdthesis},
 author = {Martin, Andreas}
}

Knowledge and knowledge work are key factors of today’s successful companies. This study devises an approach for increasing the performance of knowledge work by shifting it towards a process orientation. Business process management and workflow management are methods for structured and predefined work but are not flexible enough to support knowledge work in a comprehensive way. Case-based reasoning (CBR) uses the knowledge of previously experienced cases in order to propose a solution to a problem. CBR can be used to retrieve, reuse, revise, retain and store functional and process knowledge. The aim of the research was to develop an approach that combines CBR and process execution to improve knowledge work. The research goals are: a case description for knowledge work that can be integrated into a process execution system and that contains both functional and process knowledge; a similarity algorithm for the retrieval of functional and procedural knowledge; and an adaptation mechanism that deals with the different granularities of solution parts. This thesis contains a profound literature framework and follows a design science research (DSR) strategy. During the awareness phase of the design science research process, an application scenario was acquired using the case study research method, which is the admission process for a study programme at a university. This application scenario is used to introduce and showcase the combined CBR and process execution approach called ICEBERG-PE, which consists of a case model and CBR services. The approach is implemented as a prototype and can be instantiated using the ICEBERG-PE procedure model, a specific procedure model for ontology-based, CBR projects. The ICEBERG-PE prototype has been evaluated using triangulated evaluation data and different evaluation settings to confirm that the approach is transferable to other contexts. Finally, this thesis concludes with potential recommendations for future research.

@inbook{
 type = {inbook},
 year = {2016},
 pages = {30-42},
 websites = {http://dx.doi.org/10.1007/978-3-319-42887-1_3,https://speakerdeck.com/andreasmartin/a-case-modelling-language-for-process-variant-management-in-case-based-reasoning},
 publisher = {Springer International Publishing},
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 author = {Cognini, Riccardo and Hinkelmann, Knut and Martin, Andreas},
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 doi = {10.1007/978-3-319-42887-1_3},
 chapter = {A Case Modelling Language for Process Variant Management in Case-Based Reasoning},
 title = {Business Process Management Workshops: BPM 2015, 13th International Workshops, AdaptiveCM 2015: 4th International Workshop on Adaptive Case Management and other non-workflow approaches to BPM, Innsbruck, Austria, August 31 -- September 3, 2015, Revised Pa}
}

@inproceedings{
 title = {A new Retrieval Function for Ontology-Based Complex Case Descriptions},
 type = {inproceedings},
 year = {2015},
 publisher = {ibai-publishing},
 city = {Hamburg},
 id = {11bf3ebd-a01c-3a28-9b3c-162792ee4a3b},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Witschel2015},
 private_publication = {false},
 abstract = {This work focuses on case-based reasoning in domains where cases have complex structures with relationships to an arbitrary number of other (potentially complex and structured) entities and where case characterisations (queries) are potentially incomplete. We summarise the requirements for such domains in terms of case representation and retrieval functions. We then analyse properties of existing similarity measures used in CBR  above all symmetry  and argue that some of these properties are not desirable. By exploiting analogies with retrieval functions in the area of information retrieval  where similar functions have been replaced by new ones not exhibiting the aforementioned undesired properties  we derive a new asymmetric ranking function for case retrieval. On a generated test-bed, we show that indeed the new function results in di erent ranking of cases  and use testbed examples to illustrate why this is desirable from a user's perspective.},
 bibtype = {inproceedings},
 author = {Witschel, Hans Friedrich and Martin, Andreas and Emmenegger, Sandro and Lutz, Jonas},
 booktitle = {International Workshop Case-Based Reasoning CBR-MD 2015}
}

This work focuses on case-based reasoning in domains where cases have complex structures with relationships to an arbitrary number of other (potentially complex and structured) entities and where case characterisations (queries) are potentially incomplete. We summarise the requirements for such domains in terms of case representation and retrieval functions. We then analyse properties of existing similarity measures used in CBR above all symmetry and argue that some of these properties are not desirable. By exploiting analogies with retrieval functions in the area of information retrieval where similar functions have been replaced by new ones not exhibiting the aforementioned undesired properties we derive a new asymmetric ranking function for case retrieval. On a generated test-bed, we show that indeed the new function results in di erent ranking of cases and use testbed examples to illustrate why this is desirable from a user's perspective.

@inproceedings{
 title = {Integrating an enterprise architecture ontology in a case-based reasoning approach for project knowledge},
 type = {inproceedings},
 year = {2013},
 pages = {1-12},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6690082},
 month = {11},
 publisher = {IEEE},
 city = {Cape Town},
 id = {b65614bd-6fb1-3898-ae2d-ce405c5b3c46},
 created = {2022-08-22T15:10:35.347Z},
 accessed = {2014-01-16},
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 citation_key = {Martin2013},
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 bibtype = {inproceedings},
 author = {Martin, Andreas and Emmenegger, Sandro and Wilke, Gwendolin},
 doi = {10.1109/ES.2013.6690082},
 booktitle = {Proceedings of the First International Conference on Enterprise Systems: ES 2013}
}

@misc{
 title = {Wissensarbeit ist nicht Routinearbeit},
 type = {misc},
 year = {2013},
 source = {UnternehmerZeitung (UZ)},
 pages = {32-33},
 websites = {http://hdl.handle.net/11654/9566},
 city = {Zürich},
 id = {057f862a-5f02-3118-847a-b247eedde743},
 created = {2022-08-22T15:10:35.467Z},
 file_attached = {true},
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 last_modified = {2022-08-22T15:10:49.711Z},
 read = {true},
 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2013a},
 private_publication = {false},
 abstract = {Um heutzutage erfolgreich zu sein, genügt es nicht mehr, nur Daten zu managen und Prozesse zu beschreiben – heute und in Zukunft muss Wissensarbeit gezielt gefördert werden.},
 bibtype = {misc},
 author = {Martin, Andreas}
}

Um heutzutage erfolgreich zu sein, genügt es nicht mehr, nur Daten zu managen und Prozesse zu beschreiben – heute und in Zukunft muss Wissensarbeit gezielt gefördert werden.

@inproceedings{
 title = {Mining of Agile Business Processes},
 type = {inproceedings},
 year = {2011},
 keywords = {AAAI Technical Report SS-11-03},
 pages = {9-14},
 volume = {SS-11-03},
 websites = {http://www.aaai.org/ocs/index.php/SSS/SSS11/paper/download/2402/2917},
 id = {44766160-cb0a-3479-9e2b-82b619a254eb},
 created = {2022-08-22T15:10:35.116Z},
 accessed = {2011-04-01},
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 read = {true},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Brander2011},
 private_publication = {false},
 abstract = {Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.},
 bibtype = {inproceedings},
 author = {Brander, Simon and Hinkelmann, Knut and Martin, Andreas and Thönssen, Barbara},
 booktitle = {2011 AAAI Spring Symposium Series}
}

Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.

@inproceedings{
 title = {Refining Process Models through the Analysis of Informal Work Practice},
 type = {inproceedings},
 year = {2011},
 pages = {116-131},
 volume = {6896},
 websites = {http://dx.doi.org/10.1007/978-3-642-23059-2_12},
 publisher = {Springer Berlin / Heidelberg},
 series = {Lecture Notes in Computer Science},
 id = {13da8d47-7e8e-3f13-8bc9-58d53ae72828},
 created = {2022-08-22T15:10:35.258Z},
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 citation_key = {springerlink:10.1007/978-3-642-23059-2_12},
 source_type = {incollection},
 notes = {<b>From Duplicate 1 (<i>Refining Process Models through the Analysis of Informal Work Practice</i> - Brander, Simon; Hinkelmann, Knut; Hu, Bo; Martin, Andreas; Riss, Uwe; Thönssen, Barbara; Witschel, Hans-Friedrich)<br/></b><br/>10.1007/978-3-642-23059-2_12},
 private_publication = {false},
 abstract = {The work presented in this paper explores the potential of leveraging the traces of informal work and collaboration in order to improve business processes over time. As process executions often differ from the original design due to individual preferences, skills or competencies and exceptions, we propose methods to analyse personal preferences of work, such as email communication and personal task execution in a task management application. Outcome of these methods is the detection of internal substructures (subtasks or branches) of activities on the one hand and the recommendation of resources to be used in activities on the other hand, leading to the improvement of business process models. Our first results show that even though human intervention is still required to operationalise these insights it is indeed possible to derive interesting and new insights about business processes from traces of informal work and infer suggestions for process model changes.},
 bibtype = {inproceedings},
 author = {Brander, Simon and Hinkelmann, Knut and Hu, Bo and Martin, Andreas and Riss, Uwe U.V. and Thönssen, Barbara and Witschel, Hans Friedrich},
 editor = {Rinderle-Ma, Stefanie and Toumani, Farouk and Wolf, Karsten},
 doi = {10.1007/978-3-642-23059-2_12},
 booktitle = {Business Process Management}
}

The work presented in this paper explores the potential of leveraging the traces of informal work and collaboration in order to improve business processes over time. As process executions often differ from the original design due to individual preferences, skills or competencies and exceptions, we propose methods to analyse personal preferences of work, such as email communication and personal task execution in a task management application. Outcome of these methods is the detection of internal substructures (subtasks or branches) of activities on the one hand and the recommendation of resources to be used in activities on the other hand, leading to the improvement of business process models. Our first results show that even though human intervention is still required to operationalise these insights it is indeed possible to derive interesting and new insights about business processes from traces of informal work and infer suggestions for process model changes.

@misc{
 title = {Linked Enterprise Models and Objects providing Context and Content for creating Metadata},
 type = {misc},
 year = {2010},
 keywords = {Enterprise Architectures,Enterprise Model Ontology,Enterprise Ontology,Linked Enterprise Models and Objects,Metadata,Semantic Rules,Semantic Web},
 pages = {133},
 institution = {University of Applied Sciences Northwestern Switzerland FHNW},
 department = {Institute for Information Systems},
 id = {deea4805-5a72-33e0-a9ca-d7c2bff54b42},
 created = {2022-08-22T15:10:35.002Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:40.412Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2010a},
 source_type = {Master Thesis},
 user_context = {Master Thesis},
 private_publication = {false},
 abstract = {Even the smallest enterprise has to manage so much information and documents, that a system for arranging these things is needed; even if it is a small binder. Now when we think about the amount of information which today exists in a company, we have surely to say, that information and knowledge management is not done by only one binder – the companies nowadays need something more sophisticated. What companies nowadays need is information about information – metadata. If metadata is available, then the finding and filing process can be dramatically im-proved. But if the metadata is not available, it needs to be created – and this has to be done in most of the cases by hand. Would it not be great to have an automatic approach? This thesis introduces an approach for creating metadata in an automatic way based on rules and a formal description of an enterprise. We often hear the statement that a company has the information available – “We have the information in our systems.” But it is the question how the information is available. The Linked Enterprise Models and Objects (LEMO) approach gives the possibility to formalize the information in an enterprise. And not only the infor-mation, LEMO tries to make the relationships / links between different enterprise objects, documents, people, customers, money, almost everything in an enterprise explicit and machine process able using an ontology called enterprise model ontolo-gy (EMO). This EMO can be seen as context description of an entire enterprise. And this context can be used to create metadata using rules. The thesis provides beside the EMO and LEMO approach a demonstrator who shows the possibility of creating metadata using the mentioned ontology and semantic rules. The whole approach comes accompanied by an application scenario based on a real world case.},
 bibtype = {misc},
 author = {Martin, Andreas}
}

Even the smallest enterprise has to manage so much information and documents, that a system for arranging these things is needed; even if it is a small binder. Now when we think about the amount of information which today exists in a company, we have surely to say, that information and knowledge management is not done by only one binder – the companies nowadays need something more sophisticated. What companies nowadays need is information about information – metadata. If metadata is available, then the finding and filing process can be dramatically im-proved. But if the metadata is not available, it needs to be created – and this has to be done in most of the cases by hand. Would it not be great to have an automatic approach? This thesis introduces an approach for creating metadata in an automatic way based on rules and a formal description of an enterprise. We often hear the statement that a company has the information available – “We have the information in our systems.” But it is the question how the information is available. The Linked Enterprise Models and Objects (LEMO) approach gives the possibility to formalize the information in an enterprise. And not only the infor-mation, LEMO tries to make the relationships / links between different enterprise objects, documents, people, customers, money, almost everything in an enterprise explicit and machine process able using an ontology called enterprise model ontolo-gy (EMO). This EMO can be seen as context description of an entire enterprise. And this context can be used to create metadata using rules. The thesis provides beside the EMO and LEMO approach a demonstrator who shows the possibility of creating metadata using the mentioned ontology and semantic rules. The whole approach comes accompanied by an application scenario based on a real world case.

@inproceedings{
 title = {A Collaborative Approach to Maturing Process-related Knowledge},
 type = {inproceedings},
 year = {2010},
 keywords = {Adaptive and context-aware processes,Knowledge-intensive processes,Process learning,Task Management},
 volume = {6336 LNCS},
 publisher = {Springer},
 city = {New York, USA},
 id = {93ba8d36-53e1-3463-90a0-cdf0823bc1ee},
 created = {2022-08-22T15:10:35.297Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:45.573Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Witschel2010a},
 private_publication = {false},
 abstract = {We introduce a new approach supporting knowledge workers in sharing process-related knowledge. It is based on the insight that – while offering valuable context information – traditional business process modelling approaches are too rigid and inflexible to capture the actual way processes are executed. Therefore, business process models are made agile and open for changes during execution. To achieve this, the strict distinction between build time modelling and run time execution are softened and process activities are represented to the users in a way that allows for individual adaptations. That can be done by attaching resources, commenting on an issue or adding problems and solutions to an activity or process. In addition activities can be delegated or new (sub-)activities can be added. Thus, the model can adapt to the reality of actual process executions and valuable resources and experiences are proactively presented to users in the right context. A double-staged approach is chosen to apply the model in the real application scenario of an university.},
 bibtype = {inproceedings},
 author = {Witschel, Hans Friedrich and Hu, Bo and Riss, Uwe V. U.V. and Thönssen, Barbara and Brun, Roman and Martin, Andreas and Hinkelmann, Knut},
 doi = {10.1007/978-3-642-15618-2_24},
 booktitle = {In Proceedings of the 8th International Conference on Business Process Management}
}

We introduce a new approach supporting knowledge workers in sharing process-related knowledge. It is based on the insight that – while offering valuable context information – traditional business process modelling approaches are too rigid and inflexible to capture the actual way processes are executed. Therefore, business process models are made agile and open for changes during execution. To achieve this, the strict distinction between build time modelling and run time execution are softened and process activities are represented to the users in a way that allows for individual adaptations. That can be done by attaching resources, commenting on an issue or adding problems and solutions to an activity or process. In addition activities can be delegated or new (sub-)activities can be added. Thus, the model can adapt to the reality of actual process executions and valuable resources and experiences are proactively presented to users in the right context. A double-staged approach is chosen to apply the model in the real application scenario of an university.

@inproceedings{
 title = {Agile Process Execution with KISSmir},
 type = {inproceedings},
 year = {2010},
 keywords = {Agility,Knowledge-intensive activity,Ontology,Process execution},
 volume = {682},
 city = {Heraklion, Greece},
 id = {a4f18727-10c4-3a89-9e4a-bbbf401840f6},
 created = {2022-08-22T15:10:35.412Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:48.872Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2010},
 private_publication = {false},
 abstract = {In this paper, we describe an approach for agile business process execution and its developed prototype. In a rapidly changing environment an enterprise must be flexible and able to quickly react. Traditional business process modelling approaches are too rigid and inflexible. To achieve more agility, the modelling during build-time must be less strict and more open in a way that users are able to perform individual adaptations during run-time, which leads to more flexibility. Being able to react fast is also depending on the enterprise knowledge. Employees must be aware of it and able to access it in an easy way. The approach proposes to use ontologies to store information and appropriate services to receive context-relevant information to tackle these challenges.},
 bibtype = {inproceedings},
 author = {Martin, Andreas and Brun, Roman},
 booktitle = {5th International Workshop on Semantic Business Process Management collocated with 7th Extended Semantic Web Conference}
}

In this paper, we describe an approach for agile business process execution and its developed prototype. In a rapidly changing environment an enterprise must be flexible and able to quickly react. Traditional business process modelling approaches are too rigid and inflexible. To achieve more agility, the modelling during build-time must be less strict and more open in a way that users are able to perform individual adaptations during run-time, which leads to more flexibility. Being able to react fast is also depending on the enterprise knowledge. Employees must be aware of it and able to access it in an easy way. The approach proposes to use ontologies to store information and appropriate services to receive context-relevant information to tackle these challenges.

@inproceedings{
 title = {Support Knowledge Intensive Work with Semantic Technologies},
 type = {inproceedings},
 year = {2009},
 keywords = {Demonstrator,Knowledge Intensive Work,NEPOMUK,Process Reasoning},
 city = {Dublin, Ireland},
 id = {b2a93894-4f4f-3a80-9494-c83e7b5c9699},
 created = {2022-08-22T15:10:35.007Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:38.520Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Brun2009},
 private_publication = {false},
 abstract = {This paper introduces an approach to support knowledge intensive work with semantic technologies by semantically enriching processes and then applying reasoning techniques on them. Further, the proposed use cases show the abilities of semantically enriched processes. Finally the paper introduces a possible architecture of a system which facilitates the knowledge intensive work with semantic technologies.},
 bibtype = {inproceedings},
 author = {Brun, Roman and Martin, Andreas},
 booktitle = {International Business Informatics Challenge and Conference 2009}
}

This paper introduces an approach to support knowledge intensive work with semantic technologies by semantically enriching processes and then applying reasoning techniques on them. Further, the proposed use cases show the abilities of semantically enriched processes. Finally the paper introduces a possible architecture of a system which facilitates the knowledge intensive work with semantic technologies.

@inproceedings{
 title = {Applying Organizational Learning to Enterprise Knowledge Maturing},
 type = {inproceedings},
 year = {2009},
 keywords = {Dimensions of knowledge potentials,Method template,Methods of organizational learning,Organizational learning,Organizational learning views,Processes of organization learning,Types of organizational learning,dimensions of knowledge potentials,learning,method template,methods of organizational learning,organizational learning,organizational learning views,processes of organization learning,types of organizational},
 pages = {39-50},
 city = {Graz, Austria},
 id = {cc890e1e-90ce-3f23-837a-a137b54ad431},
 created = {2022-08-22T15:10:36.382Z},
 file_attached = {true},
 profile_id = {e4ed3238-8302-3280-bd05-0b185874fb43},
 group_id = {18478096-ac37-34c1-8594-c07e3e0a2145},
 last_modified = {2022-08-22T15:10:55.345Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2009},
 private_publication = {false},
 abstract = {We first describe the state of the art of organizational learning, mentioning the theories and types of it. The need of organizational learning, contributing processes and the main processes are further explained. Various methods of organizational learn- ing are introduced. A template for a short description is proposed, which gives an overview about existing methods. The template then offers the possibility to indicate which method can be applied on Enterprise Knowledge Maturing.},
 bibtype = {inproceedings},
 author = {Martin, Andreas and Brun, Roman},
 booktitle = {Proceedings of IKNOW ’09 and ISEMANTICS ’09}
}

We first describe the state of the art of organizational learning, mentioning the theories and types of it. The need of organizational learning, contributing processes and the main processes are further explained. Various methods of organizational learn- ing are introduced. A template for a short description is proposed, which gives an overview about existing methods. The template then offers the possibility to indicate which method can be applied on Enterprise Knowledge Maturing.

@misc{
 title = {Evaluation eines Petri-Netz/XPDL-basierten Ansatzes zur Implementierung von Geschäftsprozessen},
 type = {misc},
 year = {2008},
 websites = {http://www.fhnw.ch/wirtschaft/dienstleistung/studierendenprojekte/olten/bisherige-projekte/diplomarbeiten-2008-2/evaluation-eines-petri-netz-xpdl-2013-basierten-ansatzes-zur-impementierung-von/evaluation-eines-petri-netz-xpdl2013basierten-ansatzes-zur-imp},
 institution = {University of Applied Sciences Northwestern Switzerland FHNW},
 department = {Institute for Information Systems},
 id = {410e2be6-b52e-3ea4-829c-1d748c7c2c5b},
 created = {2022-08-22T15:10:35.228Z},
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 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Martin2008},
 source_type = {Bachelor Thesis},
 user_context = {Bachelor Thesis},
 private_publication = {false},
 abstract = {Die Income Suite der Firma dient der Modellierung, Analyse, Optimierung und Dokumentierung von Geschäftsprozessen auf Basis von Petri-Netzen. Die Income Toolsuite blickt auf ein über zehnjähriges Bestehen zurück und wird zurzeit in der Version 4 produktiv bei diversen Kunden eingesetzt. Die Version 5 von Income ist zurzeit in Entwicklung, existiert als Beta- Version und basiert wie die vorangegangen Versionen auch auf Petri- Netzen. Im Rahmen der elektronischen Ausführung von Geschäftsprozessen mittels Workflowsystemen ist der BPEL-Standard in aller Munde. Oft wird durch Presse und grosse Hersteller suggeriert, Geschäftsprozessmanagement kann nur mit BPEL umgesetzt werden. Jedoch sieht die Realität anders aus – XPDL und andere auch proprietäre Sprachen sind gang und gäbe. Der Auftrag war es, der Firma eine Grundlage mit konkreten Handlungsempfehlungen für die weitere Entwicklung der Income Suite im Bereich von Workflowmanagement zu liefern. Die konkreten Ziele dieser Arbeit sind für Income eine geeignete Transformationssprache zu finden, Integrationsansätze für IT- Systeme aufzuzeigen und ein Workflow- Konzept zu erstellen. Ein weiteres Ziel war es eine lauffähige Workflow- Implementation basierend auf einem DEMO Prozess zu erstellen. Die Ergebnisse liegen in schriftlich, in Form eines Berichts und elektronisch in Form von implementierten Geschäftsprozessen vor. Die wichtigsten Ergebnisse sind: 1. Transformation von Geschäftsprozessen 2. Gegenüberstellung von XPDL und BPEL 3. Evaluation von Workflow- Systemen 4. Modell der Web service- basierten Workflow Implementation 5. Anwendungsbeispiel 6. Handlungsempfehlungen für die Income Suite},
 bibtype = {misc},
 author = {Martin, Andreas}
}

Die Income Suite der Firma dient der Modellierung, Analyse, Optimierung und Dokumentierung von Geschäftsprozessen auf Basis von Petri-Netzen. Die Income Toolsuite blickt auf ein über zehnjähriges Bestehen zurück und wird zurzeit in der Version 4 produktiv bei diversen Kunden eingesetzt. Die Version 5 von Income ist zurzeit in Entwicklung, existiert als Beta- Version und basiert wie die vorangegangen Versionen auch auf Petri- Netzen. Im Rahmen der elektronischen Ausführung von Geschäftsprozessen mittels Workflowsystemen ist der BPEL-Standard in aller Munde. Oft wird durch Presse und grosse Hersteller suggeriert, Geschäftsprozessmanagement kann nur mit BPEL umgesetzt werden. Jedoch sieht die Realität anders aus – XPDL und andere auch proprietäre Sprachen sind gang und gäbe. Der Auftrag war es, der Firma eine Grundlage mit konkreten Handlungsempfehlungen für die weitere Entwicklung der Income Suite im Bereich von Workflowmanagement zu liefern. Die konkreten Ziele dieser Arbeit sind für Income eine geeignete Transformationssprache zu finden, Integrationsansätze für IT- Systeme aufzuzeigen und ein Workflow- Konzept zu erstellen. Ein weiteres Ziel war es eine lauffähige Workflow- Implementation basierend auf einem DEMO Prozess zu erstellen. Die Ergebnisse liegen in schriftlich, in Form eines Berichts und elektronisch in Form von implementierten Geschäftsprozessen vor. Die wichtigsten Ergebnisse sind: 1. Transformation von Geschäftsprozessen 2. Gegenüberstellung von XPDL und BPEL 3. Evaluation von Workflow- Systemen 4. Modell der Web service- basierten Workflow Implementation 5. Anwendungsbeispiel 6. Handlungsempfehlungen für die Income Suite