Martin, Andreas
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- PublikationProceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)(CEUR Workshop Proceedings, 2019) Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter03 - Sammelband
- PublikationReports of the AAAI 2019 Spring Symposium Series(American Association for Artificial Intelligence, 2019) Baldini, Ioana; Barrett, Clark; Chella, Antonio; Cinelli, Carlos; Gamez, David; Gilpin, Leilani H.; Hinkelmann, Knut; Holmes, Dylan; Kido, Takashi; Kocaoglu, Murat; Lawless, William F.; Lomuscio, Alessio; Macbeth, Jamie C.; Martin, Andreas; Mittu, Ranjeev; Patterson, Evan; Sofge, Donald; Tadepalli, Prasad; Takadama, Keiki; Wilson, Shomir [in: AI Magazine]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.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationRandom walks on human knowledge: incorporating human knowledge into data-driven recommenders(2018) Witschel, Hans Friedrich; Martin, Andreas; Bernardino, Jorge; Salgado, Ana; Filipe, Joaquim [in: IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings]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.04B - Beitrag Konferenzschrift
- PublikationCase-based reasoning for process experience(Springer, 2018) Martin, Andreas; Hinkelmann, Knut; Dornberger, Rolf [in: 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.04A - Beitrag Sammelband
- PublikationTraining and re-using human experience: a recommender for more accurate cost estimates in project planning(SciTePress, 2018) Rudolf von Rohr, Christian; Witschel, Hans Friedrich; Martin, Andreas [in: IC3K 2018 - 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 similarities 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.04B - Beitrag Konferenzschrift
- PublikationOntology-based metamodeling(Springer, 2018) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Thönssen, Barbara; Dornberger, Rolf [in: 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.04A - Beitrag Sammelband
- PublikationA viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management(Taylor & Francis, 28.03.2016) Martin, Andreas; Emmenegger, Sandro; Hinkelmann, Knut; Thönssen, Barbara [in: Enterprise Information Systems]The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprise-specific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such 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 was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationAn Ontology-based and Case-based Reasoning supported Workplace Learning Approach(Springer, 2016) Emmenegger, Sandro; Thönssen, Barbara; Laurenzi, Emanuele; Martin, Andreas; Zhang Sprenger, Congyu; Hinkelmann, Knut; Witschel, Hans Friedrich [in: Communications in Computer and Information Science]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationA Case Modelling Language for Process Variant Management in Case-based Reasoning(2015) Cognini, Riccardo; Hinkelmann, Knut; Martin, Andreas [in: AdaptiveCM 2015 – 4th International Workshop on Adaptive Case Management and other non-workflow approaches to BPM]04B - Beitrag Konferenzschrift
- PublikationIntegrating an Enterprise Architecture Ontology in a Case-Based Reasoning Approach for Project Knowledge(08.11.2013) Martin, Andreas; Emmenegger, Sandro; Wilke, Gwendolin04B - Beitrag Konferenzschrift