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  • 51.
    Tan, He
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    SAMBO results for the Ontology Alignment Evaluation Initiative 20072007In: Ontology Matching (OM-2007): papers from the ISWC + ASWC Workshop, 2007, p. 236-243Conference paper (Refereed)
    Abstract [en]

    This article describes a system for ontology alignment, SAMBO, and presents its results for the benchmark and anatomy tasks in the 2007 Ontology Alignment Evaluation Initiative. For the benchmark task we have used a strategy based on string matching as well as the use of a thesaurus, and obtained good results in many cases. For the anatomy task we have used a combination of string matching and the use of domain knowledge. This combination performed well in former evaluations using other anatomy ontologies.

  • 52.
    Tan, He
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Selecting an ontology for biomedical text mining2009Conference paper (Refereed)
  • 53.
    Tan, He
    et al.
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Selecting an ontology for biomedical text mining2009In: Proceedings of the Workshop on BioNLP, 2009, p. 55-62Conference paper (Refereed)
    Abstract [en]

    Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applicationson ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system.

  • 54.
    Tan, He
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. 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).
    Resmini, Andrea
    Jönköping University, Jönköping International Business School, JIBS, Informatics.
    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).
    Adlemo, Anders
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Workplace innovation in Swedish local organizations - technology aspect: BIS 2015 International Workshops, Poznań, Poland, June 24-26, 2015, Revised Papers2015In: Business Information Systems Workshops / [ed] Witold Abramowicz, 2015, Vol. 228, p. 139-147Conference paper (Refereed)
    Abstract [en]

    Workplace innovation (WI) is important to provide betterwork opportunities and increase productivity. WI at the individual tasklevel concerns the structure of individual work tasks. A number of surveyshave been done that measured WI at the individual task level, howeverthey paid little attention to work environment, in particular to supportivetechnology. This paper presents the case study of WI in two Swedishorganisations with focus on the alignment of ICT and the individual worktasks. We carried out seven interviews of workers at dierent levels ofjob and in dierent sectors. The qualitative data analysis identied fourthemes: business processes, working roles, data sources, and technology.The analysis was facilitated by constructing BPMN (Business ProcessModel Notation) diagrams for the identied business processes. We discoveredthat the supportive technology in the organisations is adequatebut downright traditional. We argue that technology is an important factorand enabler for WI. Finally, we present an architectural model thatprovides a direction for future work on WI taking ICT as the basis.

  • 55.
    Tan, He
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Adlemo, Anders
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Lessons Learned from an Application of Ontologies in Software Testing2019In: CEUR Workshop Proceedings / [ed] Adrien Barton, Selja Seppälä, Daniele Porello, et.al., CEUR-WS , 2019, Vol. 2518Conference paper (Refereed)
    Abstract [en]

    Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. In previous work we proposed an ontologybased approach to alleviate this problem. In this paper we discuss the lessons learned from the implementation and application of the approach in a use case from the avionic industry. The lessons are related to the areas of ontology development, ontology evaluation, the OWL language and rule-based reasoning.

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  • 56.
    Tan, He
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Fourlakidis, Vasilios
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Diószegi, Attila
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Data-driven modeling of mechanical properties of cast iron using fuzzy logic2020In: Fuzzy Systems and Data Mining VI: Proceedings of FSDM 2020 / [ed] Antonio J. Tallón-Ballesteros, Amsterdam: IOS Press, 2020, p. 656-662Conference paper (Refereed)
    Abstract [en]

    For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

  • 57.
    Tan, He
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Jarfors, Anders E.W.
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Seifeddine, Salem
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    A design of fuzzy inference systems to predict tensile properties of as-cast alloy2021In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 113, no 3-4, p. 1111-1123Article in journal (Refereed)
    Abstract [en]

    In this study, a design of Mamdani type fuzzy inference systems is presented to predict tensile properties of as-cast alloy. To improve manufacturing of light weight cast components, understanding of mechanical properties of cast components under load is important. The ability of deterministic models to predict the performance of a cast component is limited due to the uncertainty and imprecision in casting data. Mamdani type fuzzy inference systems are introduced as a promising solution. Compared to other artificial intelligence approaches, Mandani type fuzzy models allow for a better result interpretation. The fuzzy inference systems were designed from data and experts’ knowledge and optimized using a genetic algorithm. The experts’ knowledge was used to set up the values for the inference engine and initial values for the database parameters. The rule base was automatically generated from the data which were collected from casting and tensile testing experiments. A genetic algorithm with real-valued coding was used to optimize the database parameters. The quality of the constructed systems was evaluated by comparing predicted and actual tensile properties, including yield strength, Y.modulus, and ultimate tensile strength, of as-case alloy from two series of casting and tensile testing experimental data. The obtained results showed that the quality of the systems has satisfactory accuracy and is similar to or better than several machine learning methods. The evaluation results also demonstrated good reliability and stability of the approach.

  • 58.
    Tan, He
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Jarfors, Anders E.W.
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Seifeddine, Salem
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Fuzzy Logic Based Modelling of Cast Component Properties2019In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 52, no 13, p. 1132-1137Article in journal (Refereed)
    Abstract [en]

    Digitalization of manufacturing requires building models to represent accumulated data and knowledge on the products and processes. The use of formal knowledge models allows for increase of the automation level leading to more sustainable manufacturing. Casting is important for different industries because it offers a great freedom of designing for weight reduction. This paper presents an approach to modelling of cast component properties that is based on fuzzy logic. The approach includes learning of the fuzzy inference rules from the data. The constructed fuzzy logic models can be used to tune the manufacturing process to produces cast components with desired properties. The evaluation of the results demonstrates that the accuracy of the two created models are 3.58% and 3.15% respectively with the learned fuzzy inference rules being identical to the manually created ones. The presented approach can help to automate the management of cast component manufacturing. 

  • 59.
    Tarasov, Vladimir
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tan, He
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Adlemo, Anders
    Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
    Automation of software testing process using ontologies2019In: Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD / [ed] J. Dietz, D. Aveiro & J. Filipe, SciTePress, 2019, p. 57-66Conference paper (Refereed)
    Abstract [en]

    Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. Methods based on knowledge representation and reasoning can alleviate this problem. This paper presents an approach to enhance the automation of the testing process using ontologies and inference rules. The approach takes software requirements specifications written in structured text documents as input and produces the output, i.e. test scripts. The approach makes use of ontologies to deal with the knowledge embodied in requirements specifications and to represent the desired structure of test cases, as well as makes use of a set of inference rules to represent strategies for deriving test cases. The implementation of the approach, in the context of an industrial case, proves the validity of the overall approach.

  • 60.
    Tarasov, Vladimir
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tan, He
    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.
    Ismail, Muhammad
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Mats, Johansson
    Saab AB, Avisonics.
    Olsson, Daniel
    AddQ AB.
    Ontology-based Software Test Case Generation (OSTAG)2015In: European Projects in Knowledge Applications and Intelligent Systems - Volume 1: EPS Lisbon 2016 / [ed] R. J. Machado, J. Sequeira,H. Plácido da Silva, & J. Filipe, SciTePress, 2015, p. 135-159Conference paper (Refereed)
    Abstract [en]

    Testing is a paramount quality assurance activity in every software developmentproject, especially for embedded, safety critical systems. During thetest process, a lot of effort is put into the generation of test cases. The presentedOSTAG project aimed at developing methods and techniques to automate thesoftware test case generation for black-box testing. The proposed approach wasbased on the creation of a software requirements ontology and the applicationof inference rules on the ontology to derive test cases. The ontology representsknowledge of the requirements, the software system and the corresponding applicationdomain while the inference rules formalize knowledge from documentsand experienced testers in the domain of test planning and test case generation.A software prototype of the approach was implemented and one of the industrialproject partners evaluated the results. An alternative method for generating testcases, based on genetic algorithms, was also explored.

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  • 61.
    Tarasov, Vladimir
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tan, He
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ismail, Muhammad
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Adlemo, Anders
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Johansson, Mats
    Saab AB.
    Application of inference rules to a software requirements ontology to generate software test cases2017In: OWL: Experiences and Directions – Reasoner Evaluation: 13th International Workshop, OWLED 2016, and 5th International Workshop, ORE 2016, Bologna, Italy, November 20, 2016, Revised Selected Papers / [ed] Dragoni, Mauro; Poveda-Villalón, María; Jimenez-Ruiz, Ernesto, Cham: Springer, 2017, p. 82-94Chapter in book (Refereed)
    Abstract [en]

    Testing of a software system is resource-consuming activity. One of the promising ways to improve the efficiency of the software testing process is to use ontologies for testing. This paper presents an approach to test case generation based on the use of an ontology and inference rules. The ontology represents requirements from a software requirements specification, and additional knowledge about components of the software system under development. The inference rules describe strategies for deriving test cases from the ontology. The inference rules are constructed based on the examination of the existing test documentation and acquisition of knowledge from experienced software testers. The inference rules are implemented in Prolog and applied to the ontology that is translated from OWL functional-style syntax to Prolog syntax. The first experiments with the implementation showed that it was possible to generate test cases with the same level of detail as the existing, manually produced, test cases.

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    Fulltext
  • 62.
    Tarasov, Vladimir
    et al.
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Tan, He
    Jönköping University, School of Engineering, JTH, Department of Computing, Jönköping AI Lab (JAIL).
    Jarfors, Anders E.W.
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Seifeddine, Salem
    Jönköping University, School of Engineering, JTH, Materials and Manufacturing.
    Fuzzy logic-based modelling of yield strength of as-cast A356 alloy2020In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 32, no 10, p. 5833-5844Article in journal (Refereed)
    Abstract [en]

    Uncertain and imprecise data are inherent to many domains, e.g. casting lightweight components. Fuzzy logic offers a way to handle such data, which makes it possible to create predictive models even with small and imprecise data sets. Modelling of cast components under fatigue load leads to understanding of material behaviour on component level. Such understanding is important for the design for minimum warranty risk and maximum weight reduction of lightweight cast components. This paper contributes with a fuzzy logic-based approach to model fatigue-related mechanical properties of as-cast components, which has not been fully addressed by the current research. Two fuzzy logic models are constructed to map yield strength to the chemical composition and the rate of solidification of castings for two A356 alloys. Artificial neural networks are created for the same data sets and then compared to the fuzzy logic approach. The comparison shows that although the neural networks yield similar prediction accuracy, they are less suitable for the domain because they are opaque models. The prediction errors exhibited by the fuzzy logic models are 3.53% for the model and 3.19% for the second, which is the same error level as reported in related work. An examination of prediction errors indicated that these are affected by parameters of the membership functions of the fuzzy logic model.

  • 63.
    Wächter, Thomas
    et al.
    TU Dresden.
    Tan, He
    (Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Wobst, André
    TU Dresden.
    Lambrix, Patrick
    (Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems.
    Schroeder, Michael
    TU Dresden.
    A Corpus-driven Approach for Design, Evolution and Alignment of Ontologies2006In: Proceedings of the Winter Simulation Conference, 2006, WSC 06, 2006, p. 1595-1602Conference paper (Refereed)
    Abstract [en]

    Bio-ontologies are hierarchical vocabularies, which are used to annotate other data sources such as sequence and structure databases. With the wide use of ontologies their integration, design, and evolution becomes an important problem. We show how textmining on relevant text corpora can be used to identify matching ontology terms of two separate ontologies and to propose new ontology terms for a given term. We evaluate these approaches on the GeneOntology

12 51 - 63 of 63
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  • ieee
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