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  • 1.
    Alirezaie, Marjan
    et al.
    Örebro University.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Blomqvist, Eva
    SICS - East Swedish ICT.
    SmartEnv as a Network of Ontology Patterns2018In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 9, no 6, p. 903-918Article in journal (Refereed)
    Abstract [en]

    In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects. The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

  • 2.
    Alirezaie, Marjan
    et al.
    Örebro Universitet.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). RISE SICS East AB, Linköping, Sweden.
    Blomqvist, Eva
    Linköpings Universitet.
    Nyström, Mikael
    Linköpings Universitet.
    Ivanova, Valentina
    Linköpings Universitet.
    SmartEnv Ontology in E-care@home2018In: SSN 2018 - Semantic Sensor Networks Workshop: Proceedings of the 9th International Semantic Sensor Networks Workshopco-located with 17th International Semantic Web Conference (ISWC 2018) / [ed] Maxime Lefrançois, Raúl Garcia Castro, Amélie Gyrard, Kerry Taylor, CEUR-WS , 2018, Vol. 2213, p. 72-79Conference paper (Refereed)
    Abstract [en]

    In this position paper we briefly introduce SmartEnv ontology which relies on SEmantic Sensor Network (SSN) ontology and is used to represent different aspects of smart and sensorized environments. We will also talk about E-carehome project aiming at providing an IoT-based health-care system for elderly people at their homes. Furthermore, we refer to the role of SmartEnv in Ecarehome and how it needs to be further extended to achieve semantic interoperability as one of the challenges in development of autonomous health care systems at home.

  • 3.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science; STLab, ISTC-CNR.
    Gangemi, AldoSTLab, ISTC-CNR.Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Suárez-Figueroa, Mari CarmenOntology Engineering Group, Universidad Politécnica de Madrid.
    WOP 2012: Proceedings of the 3rd Workshop on Ontology Patterns2012Conference proceedings (editor) (Refereed)
  • 4.
    Blomqvist, Eva
    et al.
    Linköping University, Linköping, Sweden.
    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).
    Presutti, Valentina
    ISTC-CNR, Rome, Italy.
    Engineering Ontologies with Patterns - The eXtreme Design Methodology2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 23-50Chapter in book (Refereed)
    Abstract [en]

    When using Ontology Design Patterns (ODPs) for modelling new parts of an ontology, i.e., new ontology modules, or even an entire ontology from scratch, ODPs can be used both as inspiration for different modelling solutions, as well as concrete templates or even “building blocks” reused directly in the new solution. This chapter discusses how ODPs, and in particular Content ODPs, can be used in ontology engineering. In particular, a specific ontology engineering methodology is presented, which was originally developed for supporting ODP use. However, this methodology, the eXtreme Design (XD), also has some characteristics that set it apart from most other ontology engineering methodologies, and which may be interesting to consider regardless of how much emphasis is put on the ODP usage. Towards the end of the chapter some XD use cases are also reported and discussed, as well as lessons learned from applying XD. The chapter is concluded through a summary and discussion about future work.

  • 5. Buendia, Ruben
    et al.
    Kogej, Thierry
    Engkvist, Ola
    Carlsson, Lars
    Linusson, Henrik
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Toccaceli, Paolo
    Ahlberg, Ernst
    Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors2019In: Journal of chemical information and modelingArticle in journal (Refereed)
  • 6.
    Coppens, Sam
    et al.
    Ghent University.
    Hammar, KarlJönköping 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).Knuth, MagnusUniversity of Potsdam.Neumann, MarcoKONA LLC, New York, USA.Ritze, DominiqueUniversity of Mannheim.Sack, HaraldUniversity of Potsdam.Vander Sande, MielGhent University.
    WaSABi 2013: Semantic Web Enterprise Adoption and Best Practice: Proceedings of the Workshop on Semantic Web Enterprise Adoption and Best Practice Co-located with 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 22, 20132013Conference proceedings (editor) (Refereed)
  • 7.
    Dórea, Fernanda C.
    et al.
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Vial, Flavie
    Epi-Connect, Skogås, Sweden.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Computer and Information Science, Linköping University, Sweden.
    Lindberg, Ann
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Lambrix, Patrick
    Department of Computer and Information Science, Linköping University, Sweden.
    Blomqvist, Eva
    Department of Computer and Information Science, Linköping University, Sweden.
    Revie, Crawford W.
    Atlantic Veterinary College, University of Prince Edward Island, Canada.
    Drivers for the development of an Animal Health Surveillance Ontology (AHSO)2019In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, p. 39-48Article in journal (Refereed)
    Abstract [en]

    Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

  • 8.
    Gangemi, Aldo
    et al.
    Université Paris 13.
    Gruninger, MichaelUniversity of Toronto.Hammar, KarlJönköping 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).Lefort, LaurentCSIRO ICT Centre.Presutti, ValentinaSTLab (ISTC-CNR).Scherp, AnsgarUniversity of Mannheim.
    Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns2014Conference proceedings (editor) (Refereed)
  • 9.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Linköpings universitet, Interaktiva och kognitiva system.
    Content Ontology Design Patterns: Qualities, Methods, and Tools2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts.

    This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives.

    The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

  • 10.
    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).
    DC Proposal: Towards an ODP Quality Model2011In: The Semantic Web – ISWC 2011: 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part II / [ed] Lora Aroyo, Chris Welty, Harith Alani, Jamie Taylor, Abraham Bernstein, Lalana Kagal, Natasha Noy and Eva Blomqvist, Berlin: Springer , 2011, p. 277-284Conference paper (Refereed)
    Abstract [en]

    The study of ontology design patterns (ODPs) is a fairly recent development. Such patterns simplify ontology development by codifying and reusing known best practices, thus lowering the barrier to entry of ontology engineering. However, while ODPs appear to be a promising addition to research and while such patterns are being presented and used, work on patterns as artifacts of their own, i.e. methods of developing, identifying and evaluating them, is still uncommon. Consequently, little is known about what ODP features or characteristics are beneficial or harmful in different ontology engineering situations. The presented PhD project aims to remedy this by studying ODP quality characteris- tics and what impact these characteristics have on the usability of ODPs themselves and on the suitability of the resulting ontologies.

  • 11.
    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).
    How to Document Ontology Design Patterns: Supporting Data Part 22016Data set
    Abstract [en]

    Survey data presented and discussed in the paper 'How to Document Ontology Design Patterns' presented at the Workshop on Ontology and Semantic Web Patterns in conjunction with the International Semantic Web Conference 2016.

  • 12.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modular Semantic CEP for Threat Detection2012In: Operations Research and Data Mining ORADM 2012 workshop proceedings / [ed] Luis Villa-Vargas, Leonid Sheremetov, and Hans-Dietrich Haasis, Mexico City: National Polytechnic Institute , 2012Conference paper (Refereed)
    Abstract [en]

    This paper introduces a generic architecture for semantic complex event processing (CEP) over sensor data, as exemplified by a reference implementation system in development for the security and surveillance domain. The system gathers data from a number of sensor subsystems and classifies this data according to ontology-based situation models and rules. The output of the system is alerts about threat situations that supports human operators in deciding when and how to deploy security personnel to manage these threats. The novelty of the proposed approach lies in the use of modular ontology design patterns for system configuration, which enable non-technical users to rapidly configure the system for particular scenarios.

  • 13.
    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).
    Motivating and Evaluating Template-Based Content ODP Instantiation: Evaluation Dataset for WOP 2016 Submission2016Data set
  • 14.
    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).
    Ontology Design Pattern Property Specialisation Strategies2014In: Knowledge Engineering and Knowledge Management: 19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014. Proceedings / [ed] Krzysztof Janowicz, Stefan Schlobach, Patrick Lambrix, Eero Hyvönen, Berlin: Springer Berlin/Heidelberg, 2014, p. 165-180Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) show potential in enabling simpler, faster, and more correct Ontology Engineering by laymen and experts. For ODP adoption to take off, improved tool support for ODP use in Ontology Engineering is required. This paper studies and evaluates the effects of strategies for object property specialisation in ODPs, and suggests tool improvements based on those strategies. Results indicate the existence of three previously unstudied strategies for ODP specialisation, the uses of which affect reasoning performance and integration complexity of resulting ontologies.

  • 15.
    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).
    Ontology Design Patterns: Adoption Challenges and Solutions2014In: Joint Proceedings of the Second International Workshop on Semantic Web Enterprise Adoption and Best Practice and Second International Workshop on Finance and Economics on the Semantic Web, EUR-WS , 2014, p. 23-32Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) are intended to guide non-experts in performing ontology engineering tasks successfully. While being the topic of significant research efforts, the uptake of these ideas outside the academic community is limited. This paper summarises some issues preventing broader adoption of Ontology Design Patterns among practitioners, suggests research directions that may help overcome these issues, and presents early results of work in these directions.

  • 16.
    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).
    Ontology Design Patterns: Improving Findability and Composition2014In: The Semantic Web: ESWC 2014 Satellite Events: ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers / [ed] Valentina Presutti, Eva Blomqvist, Raphael Troncy, Harald Sack, Ioannis Papadakis, Anna Tordai, Berlin: Springer Berlin/Heidelberg, 2014, p. 3-13Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) are intended to guide non-experts in performing ontology engineering tasks successfully. While being the topic of significant research efforts, the uptake of these ideas outside the academic community is limited. This paper summarises issues preventing broader adoption of Ontology Design Patterns among practitioners, with an emphasis on finding and composing such patterns, and presents early results of work aiming to overcome these issues.

  • 17.
    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).
    Ontology Design Patterns in Use: Lessons Learnt from an Ontology Engineering Case2012In: Proceedings of the 3rd Workshop on Ontology Patterns, 2012Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns show promise in enabling simpler, faster, more correct Ontology Engineering by laymen and experts alike. Evaluation of such patterns has typically been performed in experiments set up with artificial scenarios and measured by quantitative metrics and surveys. This paper presents an observational case study of content pattern usage in configuration of an event processing system. Results indicate that while structural characteristics of patterns are of some importance, greater emphasis needs to be put on pattern metadata and the development of pattern catalogue features.

  • 18.
    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).
    Ontology Design Patterns in WebProtégé2015In: Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015), Betlehem, USA, October 11, 2015 / [ed] Serena Villata, Jeff Z. Pan, Mauro Dragoni, CEUR-WS , 2015Conference paper (Refereed)
    Abstract [en]

    The use of Ontology Design Patterns (ODPs) in ontology engineering has been shown to have beneficial effects on the quality of developed ontologies, and promises increased interoperability of those same ontologies. Unfortunately, the lack of user-friendly integrated ODP tooling has prevented the adoption of pattern use. This paper demonstrates an extension to the WebProt´eg´e ontology engineering environment supporting the finding, specialisation, and integration of ODPs. The extension combines existing approaches with new developments in ODP search, specialisation strategies, and alignment.

  • 19.
    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)
  • 20.
    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.

  • 21.
    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.

  • 22.
    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.

  • 23.
    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.

  • 24.
    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)
  • 25.
    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.

  • 26.
    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)
  • 27.
    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.

  • 28.
    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.

  • 29.
    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.
    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.

  • 30.
    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.))
  • 31.
    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)
  • 32.
    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.
    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. 

  • 33.
    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, Tuwe
    Department of Information Technology, University of Borås, Sweden.
    Sundell, Håkan
    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.

  • 34.
    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.
    Sundell, Håkan
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    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.

  • 35.
    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.

  • 36.
    Löfström, Tuve
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. 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. 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 %.

  • 37.
    Skjæveland, Martin G.
    et al.
    University of Oslo.
    Hu, YingjieUniversity at Buffalo.Hammar, KarlJönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Svátek, VojtěchUniversity of Economics, Prague.Ławrynowicz, AgnieszkaPoznan University of Technology .
    WOP 2018: Workshop on Ontology Design and Patterns2018Conference proceedings (editor) (Refereed)
  • 38.
    Sundell, Håkan
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Univ Boras, Dept Informat Technol, Boras, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. 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.
    Explorative multi-objective optimization of marketing campaigns for the fashion retail industry2018In: 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. 1551-1558Conference paper (Refereed)
    Abstract [en]

    We show how an exploratory tool for association rule mining can be used for efficient multi-objective optimization of marketing campaigns for companies within the fashion retail industry. We have earlier designed and implemented a novel digital tool for mining of association rules from given basket data. The tool supports efficient finding of frequent itemsets over multiple hierarchies and interactive visualization of corresponding association rules together with numerical attributes. Normally when optimizing a marketing campaign, factors that cause an increased level of activation among the recipients could in fact reduce the profit, i.e., these factors need to be balanced, rather than optimized individually. Using the tool we can identify important factors that influence the search for an optimal campaign in respect to both activation and pro fit. We show empirical results from a real-world case-study using campaign data from a well-established company within the fashion retail industry, demonstrating how activation and profit can be simultaneously targeted, using computer-generated algorithms as well as human-controlled visualization.

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