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  • 51.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Linkoping University.
    Quality of Content Ontology Design Patterns2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 51-71Chapter in book (Refereed)
  • 52.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Reasoning Performance Indicators for Ontology Design Patterns2014In: Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns, CEUR-WS , 2014Conference paper (Refereed)
    Abstract [en]

    Ontologies are increasingly used in systems where performance is an important requirement. While there is a lot of work on reasoning performance-altering structures in ontologies, how these structures appear in Ontology Design Patterns (ODPs) is as of yet relatively unknown. This paper surveys existing literature on performance indicators in ontologies applicable to ODPs, and studies how those indicators are expressed in patterns published on two well known ODP portals. Based on this, it proposes recommendations and design principles for the development of new ODPs.

  • 53.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    The State of Ontology Pattern Research2011In: Perspectives in Business Informatics Research: Associated Workshops and Doctoral Consortium / [ed] Laila Niedrite, Renate Strazdina, Benkt Wangler, Riga, Latvia: Riga Technical University , 2011, p. 29-37Conference paper (Refereed)
    Abstract [en]

    Semantic web ontologies have several advantages over other knowledge representation formats that make them appropriate for information logistics architectures and applications. However, the construction of ontologies is still time-consuming and error prone for practitioners. One recent development that aims to remedy this situation is the introduction of ontology design patterns, codifying best practices and promoting reuse. This paper presents a literature survey into the state of research on ontology patterns, and suggests the use of such patterns for modeling information demand and distribution.

  • 54.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards an Ontology Design Pattern Quality Model2013Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The use of semantic technologies and Semantic Web ontologies in particular have enabled many recent developments in information integration, search engines, and reasoning over formalised knowledge. Ontology Design Patterns have been proposed to be useful in simplifying the development of Semantic Web ontologies by codifying and reusing modelling best practices.

    This thesis investigates the quality of Ontology Design Patterns. The main contribution of the thesis is a theoretically grounded and partially empirically evaluated quality model for such patterns including a set of quality characteristics, indicators, measurement methods and recommendations. The quality model is based on established theory on information system quality, conceptual model quality, and ontology evaluation. It has been tested in a case study setting and in two experiments.

    The main findings of this thesis are that the quality of Ontology Design Patterns can be identified, formalised and measured, and furthermore, that these qualities interact in such a way that ontology engineers using patterns need to make tradeoffs regarding which qualities they wish to prioritise. The developed model may aid them in making these choices.

    This work has been supported by Jönköing University.

  • 55.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Blomqvist, Eva
    Linköping University.
    Carral, David
    Wright State University.
    Van Erp, Marieke
    Vrije Universiteit Amsterdam.
    Fokkens, Antske
    Vrije Universiteit Amsterdam.
    Gangemi, Aldo
    CNR-ISTC.
    Van Hage, Willem Robert
    Vrije Universiteit Amsterdam.
    Hitzler, Pascal
    Wright State University.
    Janowicz, Krzysztof
    University of California, Santa Barbara.
    Karima, Nazifa
    Wright State University.
    Krisnadhi, Adila
    Wright State University.
    Narock, Tom
    Marymount University.
    Segers, Roxane
    Vrije Universiteit Amsterdam.
    Solanki, Monika
    University of Oxford.
    Svatek, Vojtech
    University of Economics, Prague.
    Collected Research Questions Concerning Ontology Design Patterns2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 189-198Chapter in book (Refereed)
    Abstract [en]

    This chapter lists and discusses open challenges for the ODP community in the coming years, both in terms of research questions that will need be answered, and in terms of tooling and infrastructure that will need be developed to increase adoption of ODPs in academia and industry. The chapter is organised into three sections: Section 1 focuses on issues pertaining to the patterns themselves, including understanding their features and qualities, and developing pattern languages and standards. Section 2 concerns the evaluation and development of methods for using, constructing, and extracting ODPs. Finally, Section 3 focuses on tooling and infrastructure development, including Ontology Engineering environments that support pattern use, pattern repository development, and sustainability and versioning issues.

  • 56.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    García-Crespo, AngelUniversity Carlos III of Madrid.Gómez Berbís, Juan MiguelUniversity Carlos III of Madrid.Radzimski, MateuszUniversity Carlos III of Madrid.Sánchez Cervantes, José LuisUniversity Carlos III of Madrid.Coppens, SamIBM Research.Knuth, MagnusHasso Plattner Institute, University of Potsdam.Neumann, MarcoKONA LLC.Ritze, DominiqueUniversity of Mannhemim.Vander Sande, MielGhent University.
    WaSABi-FEOSW 2014: Joint Proceedings of WaSABi 2014 and FEOSW 20142014Conference proceedings (editor) (Refereed)
  • 57.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hitzler, PascalWright State University, United States.Krisnadhi, AdilaUniversitas Indonesia, Indonesia.Ławrynowicz, AgnieszkaPoznan University of Technology, Poland.Nuzzolese, Andrea GiovanniISTC-CNR Rome, Italy.Solanki, MonikaUniversity of Oxford, United Kingdom.
    Advances in Ontology Design and Patterns2017Conference proceedings (editor) (Refereed)
    Abstract [en]

    The study of patterns in the context of ontology engineering for the semantic web was pioneered more than a decade ago by Blomqvist, Sandkuhl and Gangemi. Since then, this line of research has flourished and led to the development of ontology design patterns, knowledge patterns, and linked data patterns: the patterns as they are known by ontology designers, knowledge engineers, and linked data publishers, respectively. A key characteristic of those patterns is that they are modular and reusable solutions to recurrent problems in ontology engineering and linked data publishing.

    This book contains recent contributions which advance the state of the art on theory and use of ontology design patterns. The papers collected in this book cover a range of topics, from a method to instantiate content patterns, a proposal on how to document a content pattern, to a number of patterns emerging in ontology modeling in various situations.

  • 58.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hitzler, Pascal
    Wright State University, United States.
    Krisnadhi, Adila
    Universitas Indonesia, Indonesia.
    Ławrynowicz, Agnieszka
    Poznan University of Technology, Poland.
    Nuzzolese, Andrea Giovanni
    ISTC-CNR Rome, Italy.
    Solanki, Monika
    University of Oxford, United Kingdom.
    Preface2017In: Advances in Ontology Design and Patterns / [ed] Hammar, K., Hitzler, P., Krisnadhi, A., Ławrynowicz, A., Nuzzolese, A.G. & Solanki, M., IOS Press, 2017, p. v-viConference paper (Other academic)
  • 59.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Computer and Information Science, Linköping University, Sweden.
    Presutti, Valentina
    Semantic Technology Lab, ISTC-CNR, Italy.
    Template-Based Content ODP Instantiation2017In: Advances in Ontology Design and Patterns / [ed] Karl Hammar, Pascal Hitzler, Adila Krisnadhi, Agnieszka Ławrynowicz, Andrea Giovanni Nuzzolese, Monika Solanki, IOS Press, 2017, p. 1-13Conference paper (Refereed)
    Abstract [en]

    Content Ontology Design Patterns (CODPs) are typically instantiated into a target ontology or ontology module through a process of specialisation of CODP entities. We find, from experiences in three projects, that this approach leads to ontologies that are unintuitive to some non-expert ontologists. An approach where CODPs are used as templates can be more suitable when constructing ontologies to be used or modified by such users, and we propose a method for such template-based ODP instantiation. We evaluate this method with positive results, and describe a tool that supports the use of the proposed method.

  • 60.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    The State of Ontology Pattern Research: A Systematic Review of ISWC, ESWC and ASWC 2005–20092010In: Workshop on Ontology Patterns: Papers and Patterns from the ISWC workshop / [ed] Eva Blomqvist, Vinay K. Chaudri,Oscar Corcho, Valentina Presutti, Kurt Sandkuhl, 2010, p. 5-17Conference paper (Refereed)
    Abstract [en]

    While the use of patterns in computer science is well established, research into ontology design patterns is a fairly recent development. We believe that it is important to develop an overview of the state of research in this new field, in order to stake out possibilities for future research and in order to provide an introduction for researchers new to the topic. This paper presents a systematic literature review of all papers published at the three large semantic web conferences and their associated workshops in the last five years. Our findings indicate among other things that a lot of papers in this field are lacking in empirical validation, that ontology design patterns tend to be one of the main focuses of papers that mention them, and that although research on using patterns is being performed, studying patterns as artifacts of their own is less common.

  • 61.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Shimizu, Cogan
    Kansas State University.
    Modular Graphical Ontology Engineering Evaluated: Supporting Data2019Data set
  • 62.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Lin, Feiyu
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Information Reuse and Interoperability with Ontology Patterns and Linked Data: First Experiences from the ExpertFinder Project2010In: Business information system workshops: BIS 2010 International Workshops / [ed] Witold Abramowicz, Robert Tolksdorf, Krzysztof Wecel, Berlin: Springer , 2010, p. 168-179Conference paper (Refereed)
    Abstract [en]

    Semantic web technologies show great promise in usage scenarios that involve information logistics. This paper is an experience report on improving the semantic web ontology underlying an application used in expert finding. We use ontology design patterns to find and correct poor design choices, and align the application ontology to commonly used semantic web ontologies in order to increase the interoperability of the ontology and application. Lessons learned and problems faced are discussed, and possible future developments of the project mapped out.

  • 63.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Wallin, Erik Oskar
    Idun Real Estate Solutions AB, Stockholm, Sweden.
    Karlberg, Per
    Idun Real Estate Solutions AB, Stockholm, Sweden.
    Hälleberg, David
    Akademiska Hus AB, Stockholm, Sweden.
    The RealEstateCore Ontology2019In: The Semantic Web – ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26–30, 2019, Proceedings, Part I / [ed] C. Ghidini, O. Hartig, M. Maleshkova, V. Svátek, I. Cruz, A. Hogan, J. Song, M. Lefrançois & F. Gandon, Cham: Springer, 2019, p. 130-145Conference paper (Refereed)
    Abstract [en]

    Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future.

  • 64.
    Hitzler, P.
    et al.
    Wright State University, Dayton, OH, United States.
    Fernandez, M.
    KMi, The Open University, Milton Keynes, United Kingdom.
    Janowicz, K.
    University of California, Santa Barbara, CA, United States.
    Zaveri, A.
    Maastricht University, Maastricht, Netherlands.
    Gray, A. J. G.
    Heriot-Watt University, Edinburgh, United Kingdom.
    Lopez, V.
    IBM Research, Dublin, Ireland.
    Haller, A.
    The Australian National University, Canberra, ACT, Australia.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Preface2019In: Lecture Notes in Computer Science: Volume 11503, 16th International Semantic Web Conference, ESWC 2019, Portorož, Slovenia, 2 June 2019 through 6 June 2019, Springer, 2019, p. v-viiChapter in book (Other (popular science, discussion, etc.))
  • 65.
    Hitzler, Pascal
    et al.
    Wright State University, Dayton, USA.
    Fernández, MiriamKMi, The Open University, Milton Keynes, UK.Janowicz, KrzysztofUniversity of California, Santa Barbara, USA.Zaveri, AmrapaliMaastricht University, Maastricht, The Netherlands.Gray, Alasdair J.G.Heriot-Watt University, Edinburgh, UK.Lopez, VanessaIBM Research, Dublin, Ireland.Haller, ArminThe Australian National University, Canberra, Australia.Hammar, KarlJönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    The Semantic Web: 16th International Conference, ESWC 2019, Portorož, Slovenia, June 2–6, 2019, Proceedings2019Conference proceedings (editor) (Refereed)
  • 66.
    Hugoson, Mats-Åke
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Larsson, Ulf
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Interaction between Heterogeneous Autonomous Systems: Principles and Practice2010In: Business Information Systems Workshops. BIS 2010 International Workshops, Berlin, Germany, May 3-5, 2010. Revised Papers / [ed] Witold Abramowicz, Robert Tolksdorf, Krzysztof Wecel, Springer Berlin Heidelberg , 2010, Vol. 57, p. 119-130Conference paper (Refereed)
    Abstract [en]

    Today, many enterprises use information systems to support their activities but as a rule these systems are heterogeneous and incompatible. When enterprises need to cooperate with each other, they have to overcome this heterogeneity and establish interaction between their information systems. Based on a case study from international business development, this paper analyzes principles and conditions for creating interaction between autonomous systems. Furthermore, an approach to interaction between heterogeneous information systems in the case study is presented, which is based on a service-oriented architecture and Web services. In this context, a business process model describes how the distributed autonomous systems should be used by companies in different countries to support establishment of business relationships. These systems have different data models, and use different industrial classifications. Our main contribution to research in the field of interaction between autonomous systems are (1) a proposal for principles to be taken into account when federating autonomous systems and (2) an experience report when putting these principles into practice in the case study presented.

  • 67.
    Huhnstock, Nikolas Alexander
    et al.
    University of Skövde, Skövde, Sweden.
    Karlsson, Alexander
    University of Skövde, Skövde, Sweden.
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). University of Skövde, Skövde, Sweden.
    Steinhauer, H. Joe
    University of Skövde, Skövde, Sweden.
    An Infinite Replicated Softmax Model for Topic Modeling2019In: Modeling Decisions for Artificial Intelligence: 16th International Conference, MDAI 2019, Milan, Italy, September 4–6, 2019, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani, Springer, 2019, p. 307-318Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.

  • 68. Janowicz, Krzysztof
    et al.
    Krisnadhi, Adila Alfa
    Poveda Villalón, María
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Shimizu, Cogan
    Preface2019In: Workshop on Ontology Design and Patterns 2019: Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019), co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 2019 / [ed] Krzysztof Janowicz, Adila Alfa Krisnadhi, María Poveda Villalón, Karl Hammar & Cogan Shimizu, CEUR-WS , 2019, p. 1-1Chapter in book (Other academic)
  • 69. Janowicz, Krzysztof
    et al.
    Krisnadhi, Adila AlfaPoveda Villalón, MaríaHammar, KarlJönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Shimizu, Cogan
    WOP 2019: Workshop on Ontology Design and Patterns 2019: Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019), co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 20192019Conference proceedings (editor) (Refereed)
  • 70.
    Johansson, Christian
    et al.
    NODA, Karlshamn, Sweden.
    Bergkvist, Markus
    NODA, Karlshamn, Sweden.
    Geysen, Davy
    EnergyVille, Genk, Belgium.
    De Somer, Oscar
    EnergyVille, Genk, Belgium.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Vanhoudt, Dirk
    EnergyVille, Genk, Belgium.
    Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms2017In: 15TH INTERNATIONAL SYMPOSIUM ON DISTRICT HEATING AND COOLING (DHC15-2016) / [ed] Ulseth, R, Elsevier, 2017, p. 208-216Conference paper (Refereed)
    Abstract [en]

    Heat demand forecasting is in one form or another an integrated part of most optimisation solutions for district heating and cooling (DHC). Since DHC systems are demand driven, the ability to forecast this behaviour becomes an important part of most overall energy efficiency efforts. This paper presents the current status and results from extensive work in the development, implementation and operational service of online machine learning algorithms for demand forecasting. Recent results and experiences are compared to results predicted by previous work done by the authors. The prior work, based mainly on certain decision tree based regression algorithms, is expanded to include other forms of decision tree solutions as well as neural network based approaches. These algorithms are analysed both individually and combined in an ensemble solution. Furthermore, the paper also describes the practical implementation and commissioning of the system in two different operational settings where the data streams are analysed online in real-time. It is shown that the results are in line with expectations based on prior work, and that the demand predictions have a robust behaviour within acceptable error margins. Applications of such predictions in relation to intelligent network controllers for district heating are explored and the initial results of such systems are discussed.

  • 71.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Gabrielsson, Patrick
    Dept. of Information Technology, University of Borås, Sweden.
    Are Traditional Neural Networks Well-Calibrated?2019In: Proceedings of the International Joint Conference on Neural Networks, IEEE, 2019, Vol. July, article id 8851962Conference paper (Refereed)
    Abstract [en]

    Traditional neural networks are generally considered to be well-calibrated. Consequently, the established best practice is to not try to improve the calibration using general techniques like Platt scaling. In this paper, it is demonstrated, using 25 publicly available two-class data sets, that both single multilayer perceptrons and ensembles of multilayer perceptrons in fact often are poorly calibrated. Furthermore, from the experimental results, it is obvious that the calibration can be significantly improved by using either Platt scaling or Venn-Abers predictors. These results stand in sharp contrast to the standard recommendations for the use of neural networks as probabilistic classifiers. The empirical investigation also shows that for bagged ensembles, it is beneficiary to calibrate on the out-of-bag instances, despite the fact that this leads to using substantially smaller ensembles for the predictions. Finally, an outright comparison between Platt scaling and Venn-Abers predictors shows that the latter most often produced significantly better calibrations, especially when calibrated on out-of-bag instances. 

  • 72.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Linusson, H.
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Boström, H.
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Model-agnostic nonconformity functions for conformal classification2017In: Proceedings of the International Joint Conference on Neural Networks, IEEE, 2017, p. 2072-2079Conference paper (Refereed)
    Abstract [en]

    A conformai predictor outputs prediction regions, for classification label sets. The key property of all conformai predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset significance level. Thus, the key performance metric for evaluating conformal predictors is the size of the output prediction regions, where smaller (more informative) prediction regions are said to be more efficient. All conformal predictions rely on nonconformity functions, measuring the strangeness of an input-output pair, and the efficiency depends critically on the quality of the chosen nonconformity function. In this paper, three model-agnostic nonconformity functions, based on well-known loss functions, are evaluated with regard to how they affect efficiency. In the experimentation on 21 publicly available multi-class data sets, both single neural networks and ensembles of neural networks are used as underlying models for conformal classifiers. The results show that the choice of nonconformity function has a major impact on the efficiency, but also that different nonconformity functions should be used depending on the exact efficiency metric. For a high fraction of single-label predictions, a margin-based nonconformity function is the best option, while a nonconformity function based on the hinge loss obtained the smallest label sets on average.

  • 73.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Interpretable regression trees using conformal prediction2018In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 97, p. 394-404Article in journal (Refereed)
    Abstract [en]

    A key property of conformal predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset level of confidence. For regression, this is achieved by turning the point predictions of the underlying model into prediction intervals. Thus, the most important performance metric for evaluating conformal regressors is not the error rate, but the size of the prediction intervals, where models generating smaller (more informative) intervals are said to be more efficient. State-of-the-art conformal regressors typically utilize two separate predictive models: the underlying model providing the center point of each prediction interval, and a normalization model used to scale each prediction interval according to the estimated level of difficulty for each test instance. When using a regression tree as the underlying model, this approach may cause test instances falling into a specific leaf to receive different prediction intervals. This clearly deteriorates the interpretability of a conformal regression tree compared to a standard regression tree, since the path from the root to a leaf can no longer be translated into a rule explaining all predictions in that leaf. In fact, the model cannot even be interpreted on its own, i.e., without reference to the corresponding normalization model. Current practice effectively presents two options for constructing conformal regression trees: to employ a (global) normalization model, and thereby sacrifice interpretability; or to avoid normalization, and thereby sacrifice both efficiency and individualized predictions. In this paper, two additional approaches are considered, both employing local normalization: the first approach estimates the difficulty by the standard deviation of the target values in each leaf, while the second approach employs Mondrian conformal prediction, which results in regression trees where each rule (path from root node to leaf node) is independently valid. An empirical evaluation shows that the first approach is as efficient as current state-of-the-art approaches, thus eliminating the efficiency vs. interpretability trade-off present in existing methods. Moreover, it is shown that if a validity guarantee is required for each single rule, as provided by the Mondrian approach, a penalty with respect to efficiency has to be paid, but it is only substantial at very high confidence levels.

  • 74.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Calibrating probability estimation trees using Venn-Abers predictors2019In: SIAM International Conference on Data Mining, SDM 2019, Society for Industrial and Applied Mathematics, 2019, p. 28-36Conference paper (Refereed)
    Abstract [en]

    Class labels output by standard decision trees are not very useful for making informed decisions, e.g., when comparing the expected utility of various alternatives. In contrast, probability estimation trees (PETs) output class probability distributions rather than single class labels. It is well known that estimating class probabilities in PETs by relative frequencies often lead to extreme probability estimates, and a number of approaches to provide more well-calibrated estimates have been proposed. In this study, a recent model-agnostic calibration approach, called Venn-Abers predictors is, for the first time, considered in the context of decision trees. Results from a large-scale empirical investigation are presented, comparing the novel approach to previous calibration techniques with respect to several different performance metrics, targeting both predictive performance and reliability of the estimates. All approaches are considered both with and without Laplace correction. The results show that using Venn-Abers predictors for calibration is a highly competitive approach, significantly outperforming Platt scaling, Isotonic regression and no calibration, with respect to almost all performance metrics used, independently of whether Laplace correction is applied or not. The only exception is AUC, where using non-calibrated PETs together with Laplace correction, actually is the best option, which can be explained by the fact that AUC is not affected by the absolute, but only relative, values of the probability estimates. 

  • 75.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Linusson, Henrik
    Högskolan i Borås, Department of Information Technology, Borås, Sweden.
    Boström, Henrik
    The Royal Institute of Technology (KTH), School of Electrical Engineering and Computer Science, Stockholm, Sweden.
    Efficient Venn Predictors using Random Forests2019In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 108, no 3, p. 535-550Article in journal (Refereed)
    Abstract [en]

    Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. In addition, a probabilistic classifier must, of course, also be as accurate as possible. In this paper, Venn predictors, and its special case Venn-Abers predictors, are evaluated for probabilistic classification, using random forests as the underlying models. Venn predictors output multiple probabilities for each label, i.e., the predicted label is associated with a probability interval. Since all Venn predictors are valid in the long run, the size of the probability intervals is very important, with tighter intervals being more informative. The standard solution when calibrating a classifier is to employ an additional step, transforming the outputs from a classifier into probability estimates, using a labeled data set not employed for training of the models. For random forests, and other bagged ensembles, it is, however, possible to use the out-of-bag instances for calibration, making all training data available for both model learning and calibration. This procedure has previously been successfully applied to conformal prediction, but was here evaluated for the first time for Venn predictors. The empirical investigation, using 22 publicly available data sets, showed that all four versions of the Venn predictors were better calibrated than both the raw estimates from the random forest, and the standard techniques Platt scaling and isotonic regression. Regarding both informativeness and accuracy, the standard Venn predictor calibrated on out-of-bag instances was the best setup evaluated. Most importantly, calibrating on out-of-bag instances, instead of using a separate calibration set, resulted in tighter intervals and more accurate models on every data set, for both the Venn predictors and the Venn-Abers predictors.

  • 76.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Venn predictors using lazy learners2018In: Proceedings of the 2018 International Conference on Data Science, ICDATA'18 / [ed] R. Stahlbock, G. M. Weiss & M. Abou-Nasr, CSREA Press, 2018, p. 220-226Conference paper (Refereed)
    Abstract [en]

    Probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. Venn predictors, which can be used on top of any classifier, are automatically valid multiprobability predictors, making them extremely suitable for probabilistic classification. A Venn predictor outputs multiple probabilities for each label, so the predicted label is associated with a probability interval. While all Venn predictors are valid, their accuracy and the size of the probability interval are dependent on both the underlying model and some interior design choices. Specifically, all Venn predictors use so called Venn taxonomies for dividing the instances into a number of categories, each such taxonomy defining a different Venn predictor. A frequently used, but very basic taxonomy, is to categorize the instances based on their predicted label. In this paper, we investigate some more finegrained taxonomies, that use not only the predicted label but also some measures related to the confidence in individual predictions. The empirical investigation, using 22 publicly available data sets and lazy learners (kNN) as the underlying models, showed that the probability estimates from the Venn predictors, as expected, were extremely well-calibrated. Most importantly, using the basic (i.e., label-based) taxonomy produced significantly more accurate and informative Venn predictors compared to the more complex alternatives. In addition, the results also showed that when using lazy learners as underlying models, a transductive approach significantly outperformed an inductive, with regard to accuracy and informativeness. This result is in contrast to previous studies, where other underlying models were used.

  • 77.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Sönströd, Cecilia
    Dept. of Information Technology, University of Borås, Sweden.
    Interpretable and Specialized Conformal Predictors2019In: Conformal and Probabilistic Prediction and Applications / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Evgueni Smirnov, 2019, p. 3-22Conference paper (Refereed)
    Abstract [en]

    In real-world scenarios, interpretable models are often required to explain predictions, and to allow for inspection and analysis of the model. The overall purpose of oracle coaching is to produce highly accurate, but interpretable, models optimized for a specific test set. Oracle coaching is applicable to the very common scenario where explanations and insights are needed for a specific batch of predictions, and the input vectors for this test set are available when building the predictive model. In this paper, oracle coaching is used for generating underlying classifiers for conformal prediction. The resulting conformal classifiers output valid label sets, i.e., the error rate on the test data is bounded by a preset significance level, as long as the labeled data used for calibration is exchangeable with the test set. Since validity is guaranteed for all conformal predictors, the key performance metric is efficiency, i.e., the size of the label sets, where smaller sets are more informative. The main contribution of this paper is the design of setups making sure that when oracle-coached decision trees, that per definition utilize knowledge about test data, are used as underlying models for conformal classifiers, the exchangeability between calibration and test data is maintained. Consequently, the resulting conformal classifiers retain the validity guarantees. In the experimentation, using a large number of publicly available data sets, the validity of the suggested setups is empirically demonstrated. Furthermore, the results show that the more accurate underlying models produced by oracle coaching also improved the efficiency of the corresponding conformal classifiers.

  • 78.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Gidenstam, Anders
    Department of Information Technology, University of Borås, Sweden.
    Boström, Henrik
    School of Information and Communication Technology, Royal Institute of Technology, Sweden.
    Venn predictors for well-calibrated probability estimation trees2018In: Conformal and Probabilistic Prediction and Applications / [ed] A. Gammerman, V. Vovk, Z. Luo, E. Smirnov, & R. Peeters, 2018, p. 3-14Conference paper (Refereed)
    Abstract [en]

    Successful use of probabilistic classification requires well-calibrated probability estimates, i.e., the predicted class probabilities must correspond to the true probabilities. The standard solution is to employ an additional step, transforming the outputs from a classifier into probability estimates. In this paper, Venn predictors are compared to Platt scaling and isotonic regression, for the purpose of producing well-calibrated probabilistic predictions from decision trees. The empirical investigation, using 22 publicly available data sets, showed that the probability estimates from the Venn predictor were extremely well-calibrated. In fact, in a direct comparison using the accepted reliability metric, the Venn predictor estimates were the most exact on every data set.

  • 79.
    Johansson, Ulf
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sonstrod, C.
    Dept. of Information Technology, University of Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Bostrom, H.
    School of Electrical Engineering and Computer Science, Kth Royal Institute of Technology, Sweden.
    Customized interpretable conformal regressors2019In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 221-230, article id 8964179Conference paper (Refereed)
    Abstract [en]

    Interpretability is recognized as a key property of trustworthy predictive models. Only interpretable models make it straightforward to explain individual predictions, and allow inspection and analysis of the model itself. In real-world scenarios, these explanations and insights are often needed for a specific batch of predictions, i.e., a production set. If the input vectors for this production set are available when generating the predictive model, a methodology called oracle coaching can be used to produce highly accurate and interpretable models optimized for the specific production set. In this paper, oracle coaching is, for the first time, combined with the conformal prediction framework for predictive regression. A conformal regressor, which is built on top of a standard regression model, outputs valid prediction intervals, i.e., the error rate on novel data is bounded by a preset significance level, as long as the labeled data used for calibration is exchangeable with production data. Since validity is guaranteed for all conformal predictors, the key performance metric is the size of the prediction intervals, where tighter (more efficient) intervals are preferred. The efficiency of a conformal model depends on several factors, but more accurate underlying models will generally also lead to improved efficiency in the corresponding conformal predictor. A key contribution in this paper is the design of setups ensuring that when oracle coached regression trees, that per definition utilize knowledge about production data, are used as underlying models for conformal regressors, these remain valid. The experiments, using 20 publicly available regression data sets, demonstrate the validity of the suggested setups. Results also show that utilizing oracle-coached underlying models will generally lead to significantly more efficient conformal regressors, compared to when these are built on top of models induced using only training data. 

  • 80.
    Johansson, Örjan
    et al.
    Umeå Universitet.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    CaseMaster – an interactive tool for case-based learning over the network2004In: @ - Learning in Higher Education II / [ed] F. Buchberger & K. Enser, Linz: Trauner Verlag , 2004, p. 66-80Chapter in book (Other academic)
    Abstract [en]

    Research in the field of networked learning is steadily growing due to high capabilities of computer and telecommunication systems. Tools are developed to support web-based learning and the case method is often used in different ways to teach various subjects. The goal of the CaseMaster project was to develop a Web-based platform supporting presentation of and work with cases as well as other learning scenarios over the Web and to test this way of working. CaseMaster allows creating cases (course content) as a non-linear structure like a story with one start, but with many possible different endings. A typical case often includes problems that need to be solved, connected questions, and a portfolio. First, a teacher creates a case and stores it in CaseMaster, then students work with the case and the teacher overviews the students' results, and, finally, a follow-up seminar is conducted. CaseMaster has been successfully used in the PharmaPaC project for learning pharmacology and the SwedKid project for learning more about i.e. treatment of minorities, the position of recent refugees and immigrants. The platform was also used in the course "ICT and learning" at IML. The advantages of this platform are as follows. CaseMaster advocates human interaction and gives possibility for solving problems together. CaseMaster encourages a blended learning with human meetings and discussions without attempts to replace the teacher. It does not direct the students through the content. The students will be able to create their own paths through the case and argue for their decisions. In further research, we will concentrate on evaluation of the technical functionality of CaseMaster and investigation of how much CaseMaster affects the learning process compared with traditional ways of working.

  • 81.
    Karima, Nazifa
    et al.
    Data Semantics Lab, Wright State University, USA.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hitzler, Pascal
    Data Semantics Lab, Wright State University, USA.
    How to Document Ontology Design Patterns2017In: Advances in Ontology Design and Patterns / [ed] Karl Hammar, Pascal Hitzler, Adila Krisnadhi, Agnieszka Ławrynowicz, Andrea Giovanni Nuzzolese, Monika Solanki, IOS Press, 2017Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns are reusable building blocks for ontology modelling. As such, Ontology Design Patterns need to be understood by the humans who use them for ontology engineering tasks. In order to make it easier for ontology engineers to understand a previously unknown Ontology Design Pattern, the quality of the documentation of the pattern plays a central role. However, the question how to document Ontology Design Patterns effectively has so far largely been neglected in the research literature. In this paper, we investigate the topic systematically. We discuss the results of three separate surveys to determine the central aspects of good documentation for Ontology Design Patterns. We find that the surveys, which were conducted independently of each other, by two separate groups, essentially agree on the importance of key aspects of documentation.

  • 82.
    Kashevnik, Alexey
    et al.
    St.Petersburg Institute for Informatics and Automation of the RAS.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Shilov, Nikolay
    St.Petersburg Institute for Informatics and Automation of the RAS.
    Smirnov, Alexander
    St.Petersburg Institute for Informatics and Automation of the RAS.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Business Community Creation Based on Competence Management2008In: Collaboration and the Knowledge Economy: Issues, Applications, Case Studies / [ed] Paul Cunningham and Miriam Cunningham, Amsterdam: IOS Press , 2008, p. 1085-1092Conference paper (Refereed)
    Abstract [en]

    Business communities are networked organisations with members from different industries aiming at coordinating their activities for production of the final product/service. Forming a network of companies requires “understanding” of the different companies’ organisational competences. Competence management can help solving this task. This paper starts with presenting our earlier work in competence management projects aimed at supporting creation of business networks. Three cases are introduced: formation of business relationships with developing countries, competence supply in flexible supply networks, and collaborative product innovation. Based on experiences from these projects, competence management requirements for business community creation are identified and a conceptual framework for supporting the identified requirements is proposed. This framework considers competence management as an essential part of participation in business communities and conceptually integrates organisational and individual competence development. Enterprise models can be employed for supporting competence management and competence development in business community within the framework.

  • 83.
    Khan, Nadeem Ahmed
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering.
    Carstensen, Anders
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Transformation of Enterprise Model to Enterprise Ontology2011In: Perspectives in Business Informatics Research: Local proc. of 10th Int. Conf., BIR 2011. Associated Workshops and Doctoral Consortium / [ed] Laila Niedrite, Renate Strazdina, Benkt Wangler, Riga Technical University , 2011, p. 63-70Conference paper (Refereed)
    Abstract [en]

    The research presented in this paper has the objective to develop a process for transforming an enterprise model into an enterprise ontology. The focus is to preserve as much as possible of the semantics and the information content. A suitable approach to base the development of the transformation process on has been selected in a comparative study of three different approaches. The selected approach uses a meta-model to support the transformation process. The outcome of the research is both the improved transformation process based on the meta-model based transformation approach and a tool named EM2EO for processing the transformation. The tool reads an XML-file containing an enterprise model and produces an OWL-file containing the enterprise ontology.

  • 84.
    Krizhanovsky, Andrew
    et al.
    St.Petersburg Institute for Informatics and Automation RAS.
    Lin, Feiyu
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Shilov, Nikolai
    St.Petersburg Institute for Informatics and Automation RAS.
    Smirnov, Alexander
    St.Petersburg Institute for Informatics and Automation RAS.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Logistics-as-a-service: ontology-based architecture and approach2012Conference paper (Other academic)
    Abstract [en]

    Cyber-Physics System (CPS) is a relatively new term assuming tight integration of physical systems andcyber (IT) systems interacting in real time. Such systems aim at providing a flexible and extensibleinfrastructure supporting a variety of inputs (e.g. sensor-based and customer needs) and outputs(actuators or indicators/displays). CPSs rely on communication, computation and control infrastructuresto provide for efficient utilization of logistics infrastructure resources. In this context, Logistics-as-a-Service (LaaS) is a logistics network of organizations, people, information and resources supported byservice-oriented cyber-physical systems. Intelligent multimodal logistics network is an important node inthe worldwide logistics, involved in moving a product from supplier to customer or providing anaccompanying service. The paper presents a generic architecture scheme for LaaS, which is based onrepresenting elements of the logistics networks as services. In this environment, the role of anapplication ontology and integration of individual and organizational competences is investigated andthe use of ontology matching for finding suitable resources in a multi-lingual logistics network isdiscussed.

  • 85.
    Kusetogullari, Hüseyin
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering2017In: 2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016, IEEE, 2017, p. 305-310, article id 7886054Conference paper (Refereed)
    Abstract [en]

    In this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods.

  • 86.
    König, Rikard
    et al.
    University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). University of Borås, Borås, Sweden.
    Riveiro, Maria
    University of Skövde, Skövde, Sweden.
    Brattberg, Peter
    University of Borås, Borås, Sweden.
    Modeling golf player skill using machine learning2017In: Machine Learning and Knowledge Extraction, Springer, 2017, p. 275-294Conference paper (Refereed)
    Abstract [en]

    In this study we apply machine learning techniques to Modeling Golf Player Skill using a dataset consisting of 277 golfers. The dataset includes 28 quantitative metrics, related to the club head at impact and ball flight, captured using a Doppler-radar. For modeling, cost-sensitive decision trees and random forest are used to discern between less skilled players and very good ones, i.e., Hackers and Pros. The results show that both random forest and decision trees achieve high predictive accuracy, with regards to true positive rate, accuracy and area under the ROC-curve. A detailed interpretation of the decision trees shows that they concur with modern swing theory, e.g., consistency is very important, while face angle, club path and dynamic loft are the most important evaluated swing factors, when discerning between Hackers and Pros. Most of the Hackers could be identified by a rather large deviation in one of these values compared to the Pros. Hackers, which had less variation in these aspects of the swing, could instead be identified by a steeper swing plane and a lower club speed. The importance of the swing plane is an interesting finding, since it was not expected and is not easy to explain. © 2017, IFIP International Federation for Information Processing.

  • 87.
    Lantow, Birger
    et al.
    University of Rostock.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Reuse Engineering and Ontology Development2015In: KEOD 2015 - Proceedings of the International Conference on Knowledge Engineering and Ontology Development, part of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015), Volume 2, Lisbon, Portugal, November 12-14, 2015 / [ed] Ana Fred, Jan Dietz, David Aveiro, Kecheng Liu, Joaquim Filipe, SciTePress, 2015, p. 163-170Conference paper (Refereed)
    Abstract [en]

    While the main purpose of Ontology Design Patterns (ODPs) is to support the process of ontology engineering, they can also be used to improve existing ontologies. This paper has a focus on ODP selection and integration for ontology improvement. Based on the case of the ExpertFinder ontology, which allows for competency description of researchers, selection and integration of ODP is investigated with an explorative view. The current state of ODP selection strategies is discussed and problems arising during integration of ODP are shown. On this base, suggestions for improvements are made. Although this study deals with the integration into an existing ontology, most of the assumptions and suggestions are also valid for the general case of ODP usage.

  • 88.
    Lantow, Birger
    et al.
    University of Rostock, Germany.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. University of Rostock, Germany.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Selecting Content Ontology Design Patterns for Ontology Quality Improvement2013In: Proceedings of the 6th International Workshop on Information Logistics, Knowledge Supply and Ontologies in Information Systems, Warzaw, Poland, September 23rd, 2013 / [ed] Birger Lantow, Kurt Sandkuhl, Ulf Seigerroth, CEUR-WS , 2013, p. 68-79Conference paper (Refereed)
  • 89.
    Levashova, Tatiana
    et al.
    St. Petersburg Institute for Informatics and Automation, RAS.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Shilov, Nikolay
    St. Petersburg Institute for Informatics and Automation, RAS.
    Smirnov, Alexander
    St. Petersburg Institute for Informatics and Automation, RAS.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Product Design Network Self-Contextualization: Enterprise Knowledge-Based Approach and Agent-Based Technological Framework2009In: Holonic and Multi-Agent Systems for Manufacturing: Proceedings of 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems HoloMAS 2009 / [ed] V. Marik, T. Strasser & A. Zoitl, Berlin: Springer , 2009, p. 61-71Conference paper (Refereed)
    Abstract [en]

    The paper introduces self-contextualization in a service infrastructure for product design networks as novel application field for multi-agent technology. The main contributions of this paper are (1) identification of requirements from product design networks to the supporting service infrastructure, (2) the use of enterprise knowledge modelling techniques for the representation of computable context models, (3) a technological framework based on agent technology for self-contextualization based on enterprise knowledge models.

  • 90.
    Lin, Feiyu
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Neshnega, Lidetu Sahile
    Subba, Bikash
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    A Framework for Context Automatic Integration in Ubiquitous Computing2012Conference paper (Refereed)
  • 91.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden.
    Efficient conformal predictor ensembles2019In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286Article in journal (Refereed)
    Abstract [en]

    In this paper, we study a generalization of a recently developed strategy for generating conformal predictor ensembles: out-of-bag calibration. The ensemble strategy is evaluated, both theoretically and empirically, against a commonly used alternative ensemble strategy, bootstrap conformal prediction, as well as common non-ensemble strategies. A thorough analysis is provided of out-of-bag calibration, with respect to theoretical validity, empirical validity (error rate), efficiency (prediction region size) and p-value stability (the degree of variance observed over multiple predictions for the same object). Empirical results show that out-of-bag calibration displays favorable characteristics with regard to these criteria, and we propose that out-of-bag calibration be adopted as a standard method for constructing conformal predictor ensembles.

  • 92.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Boström, Henrik
    School of Electrical Engineering and Computer Science, Royal Institute of Technology, Kista, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Classification with reject option using conformal prediction2018In: Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part I, Springer, 2018, p. 94-105Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a practically useful means of interpreting the predictions produced by a conformal classifier. The proposed interpretation leads to a classifier with a reject option, that allows the user to limit the number of erroneous predictions made on the test set, without any need to reveal the true labels of the test objects. The method described in this paper works by estimating the cumulative error count on a set of predictions provided by a conformal classifier, ordered by their confidence. Given a test set and a user-specified parameter k, the proposed classification procedure outputs the largest possible amount of predictions containing on average at most k errors, while refusing to make predictions for test objects where it is too uncertain. We conduct an empirical evaluation using benchmark datasets, and show that we are able to provide accurate estimates for the error rate on the test set. 

  • 93.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    On the calibration of aggregated conformal predictors2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Machine Learning Research , 2017, p. 154-173Conference paper (Refereed)
    Abstract [en]

    Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.

  • 94.
    Löfström, Tuwe
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    Balkow, Jenny
    Univ Boras, Swedish Sch Text, Boras, Sweden.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Univ Boras, Dept Informat Technol, Boras, Sweden.
    A data-driven approach to online fitting services2018In: Data Science And Knowledge Engineering For Sensing Decision Support / [ed] Liu, J, Lu, J, Xu, Y, Martinez, L & Kerre, EE, World Scientific, 2018, Vol. 11, p. 1559-1566Conference paper (Refereed)
    Abstract [en]

    Being able to accurately predict several attributes related to size is vital for services supporting online fitting. In this paper, we investigate a data-driven approach, while comparing two different supervised modeling techniques for predictive regression; standard multiple linear regression and neural networks. Using a fairly large, publicly available, data set of high quality, the main results are somewhat discouraging. Specifically, it is questionable whether key attributes like sleeve length, neck size, waist and chest can be modeled accurately enough using easily accessible input variables as sex, weight and height. This is despite the fact that several services online offer exactly this functionality. For this specific task, the results show that standard linear regression was as accurate as the potentially more powerful neural networks. Most importantly, comparing the predictions to reasonable levels for acceptable errors, it was found that an overwhelming majority of all instances had at least one attribute with an unacceptably high prediction error. In fact, if requiring that all variables are predicted with an acceptable accuracy, less than 5 % of all instances met that criterion. Specifically, for females, the success rate was as low as 1.8 %.

  • 95.
    Mazalov, Vladimir
    et al.
    Institute of Applied Mathematical Research, KarRC RAS.
    Vdovitsyn, Vladimir
    Institute of Applied Mathematical Research, KarRC RAS.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Negotiation on data reallocation in distributed information systems1999In: Proceedings of the 1st International Workshop of Central and Eastern Europe on Multi-Agent Systems, CEEMAS'99, St. Petersburg, St. Petersburg, 1999, p. 47-48Conference paper (Other academic)
    Abstract [en]

    Abstract This paper concerns the problem of data reallocation in systems consisting of several informational servers. This problem may emerge when each server processes queries of the clients in its geographical area and it has to often retrieve the needed data from the other servers, that resulting in profit losses. Under these circumstances data reallocation may take place after the negotiation among the servers.

  • 96.
    Resmini, Andrea
    et al.
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Tan, He
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Adlemo, Anders
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics.
    #ViewFromTheOffice - Reconceptualizing the Workplace as an Information-based Ecosystem2016In: Proceedings of the 6th STS Conference on Socio-technical Ecosystems, 2016Conference paper (Refereed)
  • 97.
    Riveiro, Maria
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). University of Skövde.
    Explainable AI for maritime anomaly detection and autonomous driving2020In: Dagstuhl Reports, E-ISSN 2192-5283, Vol. 9, no 11, p. 29-30Article in journal (Refereed)
  • 98.
    Sandkuhl, Kurt
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Hao, Do Duc
    National Economics University, Hanoi.
    Henoch, Bengt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Hugoson, Mats-Åke
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Larsson, Ulf
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Roren, Hilde
    National Economics University, Hanoi.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    ICT-Support for Formation of Business Relationships with Developing Countries Based on Immigrant Competence. Pilot Study Vietnam: Final project report2005Report (Other academic)
  • 99.
    Sandkuhl, Kurt
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Henoch, Bengt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Le Hieu, H
    Hanoi University of Technology.
    Hugoson, Mats-Åke
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Larsson, Ulf
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    ICT-Support for Formation of Business Relationships with Developing Countries Based on Immigrant Competence. Phase 2: Integrating Competence Models into Chamber Trade: Final project report2007Report (Other academic)
  • 100.
    Sandkuhl, Kurt
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Lin, Feiyu
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering.
    Shilov, Nikolay
    St.Petersburg Institute for Informatics and Automation RAS, St. Petersburg, Russia.
    Smirnov, Alexander
    St.Petersburg Institute for Informatics and Automation RAS, St. Petersburg, Russia.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Krizhanovsky, Andrew
    Institute of Applied Mathematical Research of the Karelian Research Centre RAS, Petrozavodsk, Russia.
    Logistics-as-a-service: Ontology-based architecture and approach2013In: Revista Investigación Operacional, ISSN 0257-4306, E-ISSN 2224-5405, Vol. 34, no 3, p. 188-194Article in journal (Refereed)
    Abstract [en]

    Cyber-Physics System (CPS) is a relatively new term assuming tight integration of physical systems and cyber (IT) systems interacting in real time. Such systems aimat providing aflexible and extensible infrastructure supporting a variety of inputs (e.g. sensor-based and customer needs) and outputs (actuators or indicators/displays). CPSs rely on communication, computation and control infrastructures to provide for efficient utilizationof logistics infrastructure resources. In this context, Logistics-as-a-Service(LaaS) is a logistics network of organizations, people, information and resources supported by service-oriented cyber-physical systems. Intelligent multimodal logistics network is an important node in the worldwide logistics, involved in moving a product from supplier to customer or providing an accompanying service. The paper presents a generic architecture for LaaS, which is based on representing elements of the logistics networks as services. In this environment, the role of an application ontology and integration of individual and organizational competences is investigated and the use of ontology matching for finding suitable resources in a multi-lingual logistics network is discussed.

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