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  • 1. Aroyo, Lora
    et al.
    Welty, ChrisAlani, HarithTaylor, JamieBernstein, AbrahamKagal, LalanaNoy, Natasha FridmanBlomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I2011Conference proceedings (editor) (Other academic)
  • 2. Aroyo, Lora
    et al.
    Welty, ChrisAlani, HarithTaylor, JamieBernstein, AbrahamKagal, LalanaNoy, Natasha FridmanBlomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part II2011Conference proceedings (editor) (Other academic)
  • 3.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.
    Semi-automatic Ontology Construction based on Patterns2009Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis aims to improve the ontology engineering process, by providing better semiautomatic support for constructing ontologies and introducing knowledge reuse through ontology patterns. The thesis introduces a typology of patterns, a general framework of pattern-based semi-automatic ontology construction called OntoCase, and provides a set of methods to solve some specific tasks within this framework. Experimental results indicate some benefits and drawbacks of both ontology patterns, in general, and semi-automatic ontology engineering using patterns, the OntoCase framework, in particular.

    The general setting of this thesis is the field of information logistics, which focuses on how to provide the right information at the right moment in time to the right person or organisation, sent through the right medium. The thesis focuses on constructing enterprise ontologies to be used for structuring and retrieving information related to a certain enterprise. This means that the ontologies are quite 'light weight' in terms of logical complexity and expressiveness.

    Applying ontology content design patterns within semi-automatic ontology construction, i.e. ontology learning, is a novel approach. The main contributions of this thesis are a typology of patterns together with a pattern catalogue, an overall framework for semi-automatic patternbased ontology construction, specific methods for solving partial problems within this framework, and evaluation results showing the characteristics of ontologies constructed semiautomatically based on patterns. Results show that it is possible to improve the results of typical existing ontology learning methods by selecting and reusing patterns. OntoCase is able to introduce a general top-structure to the ontologies, and by exploiting background knowledge, the ontology is given a richer structure than when patterns are not applied.

  • 4.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    The use of Semantic Web technologies for decision support – a survey2014In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 5, no 3, p. 177-201Article in journal (Refereed)
    Abstract [en]

    The Semantic Web shares many goals with Decision Support Systems (DSS), e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields (Semantic Web and DSS), based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios.

  • 5.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Gangemi, AldoISTC-CNR.Hammar, KarlJönköping University.Suárez-Figueroa, María del Carmen
    Proceedings of the 3rd Workshop on Ontology Patterns, Boston, USA, November 12, 20122012Conference proceedings (editor) (Other academic)
  • 6.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Groza, Tudor
    Proceedings of the ISWC 2013 Posters & Demonstrations Track, Sydney, Australia, October 23, 20132013Conference proceedings (editor) (Other academic)
  • 7.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Hammar, Karl
    Computer Science and Informatics, School of Engineering, Jönköping University, Sweden.
    Presutti, Valentina
    Institute of Cognitive Sciences and Technologies, Italy.
    Engineering Ontologies with Patterns: The eXtreme Design Methodology2016In: Ontology Engineering with Ontology Design Patterns: Foundations and Applications / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 23-50Chapter in book (Other academic)
    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

    In fact, throughout this chapter when mentioning ODPs, this mainly refers to Content ODPs if not specified further.

    , 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.

  • 8.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Hitzler, Pascal
    Wright State University, OH 45435 USA.
    Janowicz, Krzysztof
    University of Calif Santa Barbara, CA 93106 USA.
    Krisnadhi, Adila
    Wright State University, OH 45435 USA; University of Indonesia, Indonesia.
    Narocke, Tom
    Marymount University, VA USA.
    Solanki, Monika
    University of Oxford, England.
    Editorial Material: Considerations regarding Ontology Design Patterns in SEMANTIC WEB, vol 7, issue 1, pp2016In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 7, no 1Article in journal (Other academic)
    Abstract [en]

    n/a

  • 9.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Rinne, Mikko
    Aalto University, Finland.
    The Event Processing ODP2013In: Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns co-located with 12th International Semantic Web Conference (ISWC 2013), CEUR-WS , 2013, Vol. 1188Conference paper (Refereed)
    Abstract [en]

    In this abstract we present a model for representing heterogeneous event objects in RDF, building on pre-existing work and focusing on structural aspects, which have not been addressed before, such as composite event objects encapsulating other event objects. The model extends the SSN and Event-F ontologies, and is available for download in the ODP portal.

  • 10.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Seil Sepour, Azam
    Jönköping University, Department of Computer and Electrical Engineering.
    Presutti, Valentina
    ISTC-CNR.
    Ontology Testing - Methodology and Tool2012Conference paper (Other academic)
    Abstract [en]

    Ontology engineering is lacking methods for verifying that ontological requirements are actually fulfilled by an ontology. There is a need for practical and detailed methodologies and tools for carrying out testing procedures and storing data about a test case and its execution. In this paper we first describe a methodology for conducting ontology testing, as well as three examples of this methodology for testing specic types of requirements. Next, we describe a tool that practically supports the methodology.We conclude that there is a need to support users in this crucial part of ontology engineering, and that our proposed methodology is a step in this direction.

  • 11.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Thollander, Patrik
    Linköping University, Department of Management and Engineering, Energy Systems. Linköping University, Faculty of Science & Engineering.
    An integrated dataset of energy efficiency measures published as linked open data2015In: Energy Efficiency, ISSN 1570-646X, E-ISSN 1570-6478, Vol. 8, no 6, p. 1125-1147Article in journal (Refereed)
    Abstract [en]

    Despite an extensive energy efficiency potential, measures are sometimes not adopted due to barriers, such as lack of information. An integrated database of available energy efficiency measures, which has not existed previously, is one step towards overcoming such barriers. To address this, we present a dataset (i.e., data-base) integrating energy efficiency data from Sweden (from the Swedish Energy Agency) and the USA (from the Department of Energys Industrial Assessment Centers), and publishing the data on the Web, using standardized Web languages and following the principles and best practices of so-called linked data. Additionally, several demonstration interfaces to access the data are provided, in order to show the potential of the result. These are entirely novel results, since this is the first dataset we are aware of that publishes this type of data using linked data principles and standards, thus integrating data from entirely different sources making them jointly searchable and reusable. Our results show that such data integration is possible, and that the integrated dataset has several benefits for different categories of users, e.g., supporting industry and energy efficiency auditors in overcoming the information barrier for investment in energy efficiency measures, and supporting application developers to more easily integrate such data into support tools for energy efficiency assessment.

  • 12.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Zhang, Ziqi
    University of Sheffield, UK.
    Gentile, Anna Lisa
    University of Sheffield, UK.
    Augenstein, Isabelle
    University of Sheffield, UK.
    Ciravegna, Fabio
    University of Sheffield, UK.
    Statistical Knowledge Patterns for Characterising Linked Data2013In: Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns (WOP 2013)  co-located with 12th International Semantic Web Conference (ISWC 2013), CEUR-WS , 2013, Vol. 1188Conference paper (Refereed)
    Abstract [en]

    Knowledge Patterns (KPs), and even more specifically Ontology Design Patterns (ODPs), are no longer only generated in a top-down fashion, rather patterns are being extracted in a bottom-up fashion from online ontologies and data sources, such as Linked Data. These KPs can assist in tasks such as making sense of datasets and formulating queries over data, including performing query expansion to manage the diversity of properties used in datasets. This paper presents an extraction method for generating what we call Statistical Knowledge Patterns (SKPs) from Linked Data. SKPs describe and characterise classes from any reference ontology, by presenting their most frequent properties and property characteristics, all based on analysis of the underlying data. SKPs are stored as small OWL ontologies but can be continuously updated in a completely automated fashion. In the paper we exemplify this method by applying it to the classes of the DBpedia ontology, and in particular we evaluate our method for extracting range axioms from data. Results show that by setting appropriate thresholds, SKPs can be generated that cover (i.e. allow us to query, using the properties of the SKP) over 94% of the triples about individuals of that class, while only needing to care about 27% of the total number of distinct properties that are used in the data.

  • 13. Cudré-Mauroux, Philippe
    et al.
    Heflin, JeffSirin, EvrenTudorache, TaniaEuzenat, JérômeHauswirth, ManfredParreira, Josiane XavierHendler, JimSchreiber, GuusBernstein, AbrahamBlomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    The Semantic Web - ISWC 2012 - 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I2012Conference proceedings (editor) (Other academic)
  • 14. Cudré-Mauroux, Philippe
    et al.
    Heflin, JeffSirin, EvrenTudorache, TaniaEuzenat, JérômeHauswirth, ManfredParreira, Josiane XavierHendler, JimSchreiber, GuusBernstein, AbrahamBlomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    The Semantic Web - ISWC 2012 - 11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part II2012Conference proceedings (editor) (Other academic)
  • 15.
    Dragisic, Zlatan
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Lambrix, Patrick
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Integrating Ontology Debugging and Matching into the eXtreme Design Methodology2015In: Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) / [ed] Eva Blomqvist; Pascal Hitzler; Adila Krisnadhi; Tom Narock; Monika Solanki, Rheinisch-Westfaelische Technische Hochschule Aachen University , 2015Conference paper (Refereed)
    Abstract [en]

    Ontology design patterns (ODPs) and related ontology development methodologies were designed as ways of sharing and reusing best practices in ontology engineering. However, while the use of these reduces the number of issues in the resulting ontologies defects can still be introduced into the ontology due to improper use or misinterpretation of the patterns. Thus, the quality of the developed ontologies is still a major concern. In this paper we address this issue by describing how ontology debugging and matching can be integrated in a state-of-the-art ontology development methodology based on ontology design patterns- the eXtreme Design methodology, and show the advantages in a case study based on a real world ontology.

  • 16.
    Hammar, Karl
    et al.
    Jönköping University.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Carral, David
    Technische Universität Dresden, Germany.
    van Erp, Marieke
    Fokkens, Antske
    Gangemi, Aldo
    van Hage, Willem Robert
    Hitzler, Pascal
    Janowicz, Krzysztof
    Karima, Nazifa
    Krisnadhi, Adila
    Narock, Tom
    Segers, Roxane
    Solanki, Monika
    Svátek, Vojtech
    Collected Research Questions Concerning Ontology Design Patterns2016In: Ontology Engineering with Ontology Design Patterns: Foundations and Applications / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 189-198Chapter in book (Other academic)
  • 17.
    Keskisärkkä, Robin
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Event Object Boundaries in RDF Streams2013In: Proceedings of the 2nd International Workshop on Ordering and Reasoning, Co-located with the 12th International Semantic Web Conference (ISWC 2013), CEUR-WS , 2013, Vol. 1059, p. 37-42Conference paper (Refereed)
    Abstract [en]

    The amount of information available as online streams is increasing steadily. A number of RDF stream processing systems have been developed in an attempt to leverage existing Semantic Web technologies, and to support typical stream operations, but very little attention has been paid to the way in which event objects (i.e. data records representing events) are streamed. In this position paper, we present the issue of respecting event object boundaries in RDF streams, and discuss some pros and cons of the various solutions

  • 18.
    Keskisärkkä, Robin
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Supporting Real-Time Monitoring in Criminal Investigations2015In: SEMANTIC WEB: ESWC 2015 SATELLITE EVENTS, SPRINGER INT PUBLISHING AG , 2015, Vol. 9341, p. 82-86Conference paper (Refereed)
    Abstract [en]

    Being able to analyze information collected from streams of data, generated by different types of sensors, is becoming increasingly important in many domains. This paper presents an approach for creating a decoupled semantically enabled event processing system, which leverages existing Semantic Web technologies. By implementing the actor model, we show how we can create flexible and robust event processing systems, which can leverage different technologies in the same general workflow. We argue that in this context RSP systems can be viewed as generic systems for creating semantically enabled event processing agents. In the demonstration scenario we show how real-time monitoring can be used to support criminal intelligence analysis, and describe how the actor model can be leveraged further to support scalability.

  • 19.
    Lambrix, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
    Hyvönen, EeroAalto University, Finland.Blomqvist, EvaLinköping University, Department of Computer and Information Science. Linköping University, The Institute of Technology.Presutti, ValentinaSTLab ISTC-CNR, Italy.Qi, GuilinSoutheast University, China.Sattler, UliUniversity of Manchester, UK.Ding, YingIndiana University Bloomingtom, USA.Ghidini, ChiaraFondazione Bruno Kessler, Italy.
    Knowledge Engineering and Knowledge Management: EKAW 2014 Satellite Events, VISUAL, EKM1, and ARCOE-Logic, Linköping, Sweden, November 24-28, 2014. Revised Selected Papers2015Conference proceedings (editor) (Refereed)
    Abstract [en]

    This volume contains the Satellite Events proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014), held in Linköping, Sweden, during November 24–28, 2014. This was the first EKAW conference in a Nordic country. It was concerned with all aspects of eliciting, acquiring, modeling, and managing knowledge, the construction of knowledge-intensive systems and services for the Semantic Web, knowledge management, e-business, natural language processing, intelligent information integration, personal digital assistance systems, and a variety of other related topics. The special focus of EKAW2014 was Diversity.

  • 20.
    Presutti, Valentina
    et al.
    ISTC-CNR.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Daga, Enrico
    ISTC-CNR.
    Gangemi, Aldo
    ISTC-CNR.
    Pattern-Based Ontology Design2012In: Ontology Engineering in a Networked World / [ed] Mari Carmen Suárez-Figueroa, Asunción Gómez-Pérez, Enrico Motta and Aldo Gangemi, Springer Berlin/Heidelberg, 2012, p. 35-64Chapter in book (Other academic)
    Abstract [en]

    In this chapter, we present ontology design patterns (ODPs), which are reusable modeling solutions that encode modeling best practices. ODPs are the main tool for performing pattern-based design of ontologies, which is an approach to ontology development that emphasizes reuse and promotes the development of a common “language” for sharing knowledge about ontology design best practices. We put specific focus on content ODPs (CPs) and show how they can be used within a particular methodology. CPs are domain-dependent patterns, the requirements of which are expressed by means of competency questions, contextual statements, and reasoning requirements. The eXtreme Design (XD) methodology is an iterative and incremental process, which is characterized by a test-driven and collaborative development approach. In this chapter, we exemplify the XD methodology for the specific case of CP reuse. The XD methodology is also supported by a set of software components named XD Tools, compatible with the NeOn Toolkit, which assist users in the process of pattern-based design.

  • 21.
    Presutti, Valentina
    et al.
    ISTC-CNR.
    Blomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.Troncy, RaphaëlSack, HaraldPapadakis, IoannisTordai, Anna
    The Semantic Web: ESWC 2014 Satellite Events - ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers2014Conference proceedings (editor) (Other academic)
  • 22.
    Rinne, Mikko
    et al.
    Aalto University, Finland.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Keskisärkkä, Robin
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nuutila, Esko
    Aalto University, Finland.
    Event Processing in RDF2013In: Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns co-located with 12th International Semantic Web Conference (ISWC 2013), CEUR-WS , 2013, Vol. 1188Conference paper (Refereed)
    Abstract [en]

    In this study we look at new requirements for event models based on concepts dened for complex event processing. A corresponding model for representing heterogeneous event objects in RDF is dened, building on pre-existing work and focusing on structural aspects, which have not been addressed before, such as composite event objects encapsulating other event objects. SPARQL querying of event objects is also considered, to demonstrate how event objects based on the model can be recognized and processed in a straightforward way with SPARQL 1.1 Query-compliant tools.

  • 23. Sabou, Marta
    et al.
    Blomqvist, EvaLinköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.Noia, Tommaso DiSack, HaraldPellegrini, Tassilo
    I-SEMANTICS 2013 - 9th International Conference on Semantic Systems, ISEM ’13, Graz, Austria, September 4-6, 20132013Conference proceedings (editor) (Other academic)
  • 24.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Gursky, Elin
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, Jim
    Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Umeå University, Sweden.
    Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study using data from Google Flu Trends, telenursing call centres, health service provider web-pages, and mass media coverage2013Conference paper (Other academic)
  • 25.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Gursky, Elin
    National Strategies Support Directorate, ANSER/Analytic Services Inc., Arlington, VA, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Strömgren, Magnus
    Department of Geography and Economic History, Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, James
    Department of Anthropology, Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Department of Geography and Economic History, Umeå University, Sweden.
    Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study2014In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 16, no 4, p. e116-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: There is abundant global interest in using syndromic data from population-wide health information systems--referred to as eHealth resources--to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments.

    OBJECTIVE: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity.

    METHODS: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases.

    RESULTS: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data.

    CONCLUSIONS: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice.

  • 26.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Gursky, Elin
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, Jim
    Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Umeå University, Sweden.
    Predictive value of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden2013Conference paper (Other academic)
  • 27.
    Zhang, Ziqi
    et al.
    University of Sheffield, UK.
    Gentile, Anna Lisa
    University of Sheffield, UK.
    Augenstein, Isabelle
    University of Sheffield, UK.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Ciravegna, Fabio
    University of Sheffield, UK.
    Mining Equivalent Relations from Linked Data2013In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 2: Short Papers, Association for Computational Linguistics, 2013, p. 289-293Conference paper (Refereed)
    Abstract [en]

    Linking heterogeneous resources is a major research challenge in the Semantic Web. This paper studies the task of mining equivalent relations from Linked Data, which was insufficiently addressed before. We introduce an unsupervised method to measure equivalency of relation pairs and cluster equivalent relations. Early experiments have shown encouraging results with an average of 0.75~0.87 precision in predicting relation pair equivalency and 0.78~0.98 precision in relation clustering.

  • 28.
    Zhang, Ziqi
    et al.
    University of Sheffield, England.
    Gentile, Anna Lisa
    University of Sheffield, England.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Augenstein, Isabelle
    University of Sheffield, England.
    Ciravegna, Fabio
    University of Sheffield, England.
    An Unsupervised Data-driven Method to Discover Equivalent Relations in Large Linked Datasets2017In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 8, no 2Article in journal (Refereed)
    Abstract [en]

    This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the well-formedness of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain-and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context.

  • 29.
    Zhang, Ziqi
    et al.
    University of Sheffield, UK.
    Gentile, Anna Lisa
    University of Sheffield, UK.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Augenstein, Isabelle
    University of Sheffield, UK.
    Ciravegna, Fabio
    University of Sheffield, UK.
    Statistical Knowledge Patterns: Identifying Synonymous Relations in Large Linked Datasets2013In: The Semantic Web – ISWC 2013: 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part I, Springer Berlin/Heidelberg, 2013, Vol. 8218, p. 703-719Conference paper (Refereed)
    Abstract [en]

    The Web of Data is a rich common resource with billions of triples available in thousands of datasets and individual Web documents created by both expert and non-expert ontologists. A common problem is the imprecision in the use of vocabularies: annotators can misunderstand the semantics of a class or property or may not be able to find the right objects to annotate with. This decreases the quality of data and may eventually hamper its usability over large scale. This paper describes Statistical Knowledge Patterns (SKP) as a means to address this issue. SKPs encapsulate key information about ontology classes, including synonymous properties in (and across) datasets, and are automatically generated based on statistical data analysis. SKPs can be effectively used to automatically normalise data, and hence increase recall in querying. Both pattern extraction and pattern usage are completely automated. The main benefits of SKPs are that: (1) their structure allows for both accurate query expansion and restriction; (2) they are context dependent, hence they describe the usage and meaning of properties in the context of a particular class; and (3) they can be generated offline, hence the equivalence among relations can be used efficiently at run time.

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