The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Alexander Maedche - One of the best experts on this subject based on the ideXlab platform.
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ICIS - Designing a Chatbot Social Cue Configuration System
2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:Social cues (e.g., gender, age) are important design features of chatbots. However, choosing a social cue design is challenging. Although much research has empirically investigated social cues, chatbot engineers have difficulties to access this Knowledge. Descriptive Knowledge is usually embedded in research articles and difficult to apply as prescriptive Knowledge. To address this challenge, we propose a chatbot social cue configuration system that supports chatbot engineers to access Descriptive Knowledge in order to make justified social cue design decisions (i.e., grounded in empirical research). We derive two design principles that describe how to extract and transform Descriptive Knowledge into a prescriptive and machine-executable representation. In addition, we evaluate the prototypical instantiations in an exploratory focus group and at two practitioner symposia. Our research addresses a contemporary problem and contributes with a generalizable concept to support researchers as well as practitioners to leverage existing Descriptive Knowledge in the design of artifacts.
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leveraging machine executable Descriptive Knowledge in design science research the case of designing socially adaptive chatbots
Design Science Research in Information Systems and Technology, 2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:In Design Science Research (DSR) it is important to build on Descriptive (Ω) and prescriptive (Λ) state-of-the-art Knowledge in order to provide a solid grounding. However, existing Knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant Knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted Knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-Knowledge base in a machine-executable form using ontologies and a Knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-Knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-Knowledge.
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DESRIST - Leveraging Machine-Executable Descriptive Knowledge in Design Science Research – The Case of Designing Socially-Adaptive Chatbots
Lecture Notes in Computer Science, 2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:In Design Science Research (DSR) it is important to build on Descriptive (Ω) and prescriptive (Λ) state-of-the-art Knowledge in order to provide a solid grounding. However, existing Knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant Knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted Knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-Knowledge base in a machine-executable form using ontologies and a Knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-Knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-Knowledge.
Qing Wang - One of the best experts on this subject based on the ideXlab platform.
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applying little jil to describe process agent Knowledge and support project planning in softpm research sections
Software Process: Improvement and Practice, 2007Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:SoftPM is a toolkit that supports a process-based approach to software project management. It relies upon a software process modeling method based upon the idea of an Organization-Entity to define standard processes and model project processes. The Process-Agent is the core of this modeling method and is a well-defined unit whose role is to encapsulate an Organization-Entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described. The article also demonstrates how this Knowledge can be useful in supporting project planning activities, such as time estimation. Copyright © 2007 John Wiley & Sons, Ltd.
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applying little jil to describe process agent Knowledge and support project planning in softpm
Software Process: Improvement and Practice, 2007Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:SoftPM is a toolkit that supports a process-based approach to software project management. It relies upon a software process modeling method based upon the idea of an Organization-Entity to define standard processes and model project processes. The Process-Agent is the core of this modeling method and is a well-defined unit whose role is to encapsulate an Organization-Entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described. The article also demonstrates how this Knowledge can be useful in supporting project planning activities, such as time estimation. Copyright © 2007 John Wiley & Sons, Ltd.
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SPW/ProSim - Applying Little-JIL to describe process-agent Knowledge in SoftPM
Software Process Change, 2006Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:In a software process modeling method based upon the Organization-Entity capability, the Process-Agent is a well-defined unit whose role is to encapsulate an entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described.
Jasper Feine - One of the best experts on this subject based on the ideXlab platform.
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ICIS - Designing a Chatbot Social Cue Configuration System
2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:Social cues (e.g., gender, age) are important design features of chatbots. However, choosing a social cue design is challenging. Although much research has empirically investigated social cues, chatbot engineers have difficulties to access this Knowledge. Descriptive Knowledge is usually embedded in research articles and difficult to apply as prescriptive Knowledge. To address this challenge, we propose a chatbot social cue configuration system that supports chatbot engineers to access Descriptive Knowledge in order to make justified social cue design decisions (i.e., grounded in empirical research). We derive two design principles that describe how to extract and transform Descriptive Knowledge into a prescriptive and machine-executable representation. In addition, we evaluate the prototypical instantiations in an exploratory focus group and at two practitioner symposia. Our research addresses a contemporary problem and contributes with a generalizable concept to support researchers as well as practitioners to leverage existing Descriptive Knowledge in the design of artifacts.
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leveraging machine executable Descriptive Knowledge in design science research the case of designing socially adaptive chatbots
Design Science Research in Information Systems and Technology, 2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:In Design Science Research (DSR) it is important to build on Descriptive (Ω) and prescriptive (Λ) state-of-the-art Knowledge in order to provide a solid grounding. However, existing Knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant Knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted Knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-Knowledge base in a machine-executable form using ontologies and a Knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-Knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-Knowledge.
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DESRIST - Leveraging Machine-Executable Descriptive Knowledge in Design Science Research – The Case of Designing Socially-Adaptive Chatbots
Lecture Notes in Computer Science, 2019Co-Authors: Jasper Feine, Stefan Morana, Alexander MaedcheAbstract:In Design Science Research (DSR) it is important to build on Descriptive (Ω) and prescriptive (Λ) state-of-the-art Knowledge in order to provide a solid grounding. However, existing Knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant Knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted Knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-Knowledge base in a machine-executable form using ontologies and a Knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-Knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-Knowledge.
J. Van Der Lei - One of the best experts on this subject based on the ideXlab platform.
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Computer-assisted Acquisition Of Formalized Knowledge In Pathology And Its Significance For Diagnostic Support
[1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1Co-Authors: A.m. Van Ginneken, W. Jansen, Arnold W. M. Smeulders, J. Van Der LeiAbstract:Diagnostic support in pathology based on findings requires a formal representation of Knowledge. A tool is introduced for the acquisition of formalized Descriptive Knowledge directly from the expert. The nature of ovarian pathology was explored at the meta level. The resulting meta Knowledge defining the proper terminology, structure and scope for the domain was incorporated in the tool. Via a menu-driven interface the expert is guided in the process of formalizing Knowledge. The Knowledge cquisition (KA) tool also serves as a versatile instrument for further analysis of the KA process.
Junchao Xiao - One of the best experts on this subject based on the ideXlab platform.
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applying little jil to describe process agent Knowledge and support project planning in softpm research sections
Software Process: Improvement and Practice, 2007Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:SoftPM is a toolkit that supports a process-based approach to software project management. It relies upon a software process modeling method based upon the idea of an Organization-Entity to define standard processes and model project processes. The Process-Agent is the core of this modeling method and is a well-defined unit whose role is to encapsulate an Organization-Entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described. The article also demonstrates how this Knowledge can be useful in supporting project planning activities, such as time estimation. Copyright © 2007 John Wiley & Sons, Ltd.
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applying little jil to describe process agent Knowledge and support project planning in softpm
Software Process: Improvement and Practice, 2007Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:SoftPM is a toolkit that supports a process-based approach to software project management. It relies upon a software process modeling method based upon the idea of an Organization-Entity to define standard processes and model project processes. The Process-Agent is the core of this modeling method and is a well-defined unit whose role is to encapsulate an Organization-Entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described. The article also demonstrates how this Knowledge can be useful in supporting project planning activities, such as time estimation. Copyright © 2007 John Wiley & Sons, Ltd.
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SPW/ProSim - Applying Little-JIL to describe process-agent Knowledge in SoftPM
Software Process Change, 2006Co-Authors: Junchao Xiao, Leon J. Osterweil, Lei Zhang, Alexander Wise, Qing WangAbstract:In a software process modeling method based upon the Organization-Entity capability, the Process-Agent is a well-defined unit whose role is to encapsulate an entity's Knowledge, skill etc. The Process-Agent's infrastructure comprises Descriptive Knowledge, process Knowledge and an experience library. The process Knowledge is represented by process steps, whose execution determines the behaviors of the Process-Agent. This causes Process-Agent Knowledge to be precisely described and well organized. In this paper, Little-JIL, a well-known process modeling language, is used to define a Process-Agent's process Knowledge. Benefits for process element Knowledge representation arising from Little-JIL's simplicity, semantic richness, expressiveness, formal and precise yet graphical syntax etc., are described.
