Auflistung nach Autor:in "Martin, Andreas"

Gerade angezeigt 1 - 20 von 36

A Case Modelling Language for Process Variant Management in Case-based Reasoning
(2015) Cognini, Riccardo; Hinkelmann, Knut; Martin, Andreas
04B - Beitrag Konferenzschrift
A flexible, extendable and adaptable model to support AI coaching
(Springer, 2023) Duhan, Ritu; Pande, Charuta; Martin, Andreas; Hinkelmann, Knut; López-Pellicer, Francisco J.; Polini, Andrea
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.
04B - Beitrag Konferenzschrift
A hybrid intelligent approach for the support of higher education students in literature discovery
(2022) Prater, Ryan; Laurenzi, Emanuele; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
In this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar
04B - Beitrag Konferenzschrift
A new Retrieval Function for Ontology-Based Complex Case Descriptions
(2015) Emmenegger, Sandro; Lutz, Jonas; Witschel, Hans Friedrich; Martin, Andreas
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.
04B - Beitrag Konferenzschrift
A 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
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
Agile Process Execution with KISSmir
(03.06.2010) Brun, Roman; Martin, Andreas
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.
04B - Beitrag Konferenzschrift
An 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
01A - Beitrag in wissenschaftlicher Zeitschrift
Applying Organizational Learning to Enterprise Knowledge Maturing
(04.09.2009) Martin, Andreas; Brun, Roman
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 learning 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.
04B - Beitrag Konferenzschrift
ArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model
(2020) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Montecchiari, Devid; Spahic, Maja; Thönssen, Barbara; Hammoudi, Slimane; Ferreira Pires, Luis; Selić, Bran
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.
04B - Beitrag Konferenzschrift
Case-based reasoning for process experience
(Springer, 2018) Martin, Andreas; Hinkelmann, Knut; Dornberger, Rolf
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
ChEdventure - A chatbot-based educational adventure game for modeling tasks in information systems
(2021) Pande, Charuta; Witschel, Hans Friedrich; Martin, Andreas
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 first design ideas and requirements.
06 - Präsentation
Combining machine learning with knowledge engineering to detect fake news in social networks - A survey
(2019) Ahmed, Sajjad; Hinkelmann, Knut; Corradini, Flavio; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; van Harmelen, Frank
Due to extensive spread of fake news on social and news media it became an emerging research topic now a days that gained attention. In the news media and social media the information is spread highspeed but without accuracy and hence detection mechanism should be able to predict news fast enough to tackle the dissemination of fake news. It has the potential for negative impacts on individuals and society. Therefore, detecting fake news on social media is important and also a technically challenging problem these days. We knew that Machine learning is helpful for building Artificial intelligence systems based on tacit knowledge because it can help us to solve complex problems due to real word data. On the other side we knew that Knowledge engineering is helpful for representing experts knowledge which people aware of that knowledge. Due to this we proposed that integration of Machine learning and knowledge engineering can be helpful in detection of fake news. In this paper we present what is fake news, importance of fake news, overall impact of fake news on different areas, different ways to detect fake news on social media, existing detections algorithms that can help us to overcome the issue, similar application areas and at the end we proposed combination of data driven and engineered knowledge to combat fake news. We studied and compared three different modules text classifiers, stance detection applications and fact checking existing techniques that can help to detect fake news. Furthermore, we investigated the impact of fake news on society. Experimental evaluation of publically available datasets and our proposed fake news detection combination can serve better in detection of fake news.
04B - Beitrag Konferenzschrift
Combining symbolic and sub-symbolic AI in the context of education and learning
(2020) Telesko, Rainer; Jüngling, Stephan; Gachnang, Phillip; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank
Abstraction abilities are key to successfully mastering the Business Information Technology Programme (BIT) at the FHNW (Fachhochschule Nordwestschweiz). Object-Orientation (OO) is one example - which extensively requires analytical capabilities. For testing the OO-related capabilities a questionnaire (OO SET) for prospective and 1st year students was developed based on the Blackjack scenario. Our main target of the OO SET is to identify clusters of students which are likely to fail in the OO-related modules without a substantial amount of training. For the interpretation of the data the Kohonen Feature Map (KFM) is used which is nowadays very popular for data mining and exploratory data analysis. However, like all sub-symbolic approaches the KFM lacks to interpret and explain its results. Therefore, we plan to add - based on existing algorithms - a “postprocessing” component which generates propositional rules for the clusters and helps to improve quality management in the admission and teaching process. With such an approach we synergistically integrate symbolic and sub-symbolic artificial intelligence by building a bridge between machine learning and knowledge engineering.
04B - Beitrag Konferenzschrift
Hybrid conversational AI for intelligent tutoring systems
(Sun SITE, Informatik V, RWTH Aachen, 2021) Pande, Charuta; Witschel, Hans Friedrich; Martin, Andreas; Montecchiari, Devid; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Dough; Stolle, Reinhard; Harmelen, Frank van
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.
04B - Beitrag Konferenzschrift
Integrating an Enterprise Architecture Ontology in a Case-Based Reasoning Approach for Project Knowledge
(08.11.2013) Martin, Andreas; Emmenegger, Sandro; Wilke, Gwendolin
04B - Beitrag Konferenzschrift
Learning and engineering similarity functions for business recommenders
(2019) Witschel, Hans Friedrich; Martin, Andreas; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Harmelen, Frank van; Clark, Peter
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 feedback. 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.
04B - Beitrag Konferenzschrift
Leverage white-collar workers with AI
(2019) Jüngling, Stephan; Hofer, Angelin; Martin, Andreas; Hinkelmann, Knut; Gerber, Aurona; Lenat, Doug; Clark, Peter
Based on the example of automated meeting minutes taking, the paper highlights the potential of optimizing the allocation of tasks between humans and machines to take the particular strengths and weaknesses of both into account. In order to combine the functionality of supervised and unsupervised machine learning with rule-based AI or traditionally programmed software components, the capabilities of AI-based system actors need to be incorporated into the system design process as early as possible.
04B - Beitrag Konferenzschrift
Linked Enterprise Models and Objects providing Context and Content for Creating Metadata
(Hochschule für Wirtschaft FHNW, 2010) Martin, Andreas; Hinkelmann, Knut; Thönssen, Barbara
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 improved. 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 formalise the information in an enterprise. And not only the information, LEMO tries to make the relationships / links between different enterprise objects, documents, people, customers, money, almost everything in an enterprise explicit and machine processable using an ontology called enterprise model ontology (EMO). This EMO can be seen as context description of an entire enterprise. And this context can be used to create metadata using rules....
11 - Studentische Arbeit
Mining of Agile Business Processes
(21.03.2011) Brander, Simon; Hinkelmann, Knut; Martin, Andreas; Thönssen, Barbara
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.
04B - Beitrag Konferenzschrift
New hybrid techniques for business recommender systems
(MDPI, 2022) Pande, Charuta; Witschel, Hans Friedrich; Martin, Andreas
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.
01A - Beitrag in wissenschaftlicher Zeitschrift