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Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
Developing an Ontology for Enterprise Crowdsourcing
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Developing an Ontology for Enterprise Crowdsourcing

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The improvement of the efficacy of enterprise crowdsourcing activities is heavily dependent on finding, sharing, and integrating the right information for certain use cases. These efforts may include …

The improvement of the efficacy of enterprise crowdsourcing activities is heavily dependent on finding, sharing, and integrating the right information for certain use cases. These efforts may include activities such as recommending a crowdsourcing task to a competent worker or evaluating an ongoing or completed crowdsourcing project. However, to pave the way for intelligent enterprise crowdsourcing platforms, the semantic richness of the data must be improved. Therefore, an ontology including a wide set of classes and properties is proposed in this paper. The ontology development is based on the ontology engineering methodology. A first general assessment of the ontology is given at the end of the paper, which describes how it addresses major crowdsourcing requirements.

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  • 1. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Developing an Ontology for Enterprise Crowdsourcing MKWI 2014, Paderborn, February the 26th, 2014 Lars Hetmank
  • 2. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Agenda 1 Enterprise Crowdsourcing 2 Current Situation & Problem Relevance 3 Anticipated Benefits & Requirements 4 Research Objective & Methodology 5 CSM Ontology 6 Conclusion & Future Work February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 2 | 18
  • 3. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Enterprise Crowdsourcing “ An online, distributed problem-solving and production model that leverages the collective intelligence of online communities to serve specific organizational goals.” (Brabham, 2013) February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 3 | 18
  • 4. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Current Situation & Problem Relevance (source: Amazon Mechanical Turk) February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 4 | 18
  • 5. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Benefits & Requirements CROWDSOURCING SYSTEM (CSS) 2 1 1 1 Task specification Task allocation 3 1 Team building identify propose form define select Requester 4 1 Monitoring 5 1 Crowd Crowdsourcing task evaluate & adjust process Interoperability EXTERNAL BUSINESS APPLICATIONS Search engines CSS Knowledge repository Enterprise SNS HR database ... Key requirements in enterprise crowdsourcing environments (source: own illustration) February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 5 | 18
  • 6. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Research Objective & Methodology  Research Objective:  Development of a lightweight and extensible ontology for capturing, storing, utilizing, and sharing crowdsourcing data that improves the automation and interoperability in enterprise crowdsourcing environments  Semantic Web vocabulary  Methodology:  Design Science  Ontology Engineering February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 6 | 18
  • 7. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Ontology Engineering February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 7 | 18
  • 8. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Literature Review (Source 1) Article Crowdsourcing systems on the World-Wide Web (Doan, Ramakrishnan, & Halevy, 2011) Dimension Semantic Entity Type of target problem Type of action Design of incentive mechanism Reward and incentive mechanism Task complexity Complexity Level Impact Level Approach to combine solutions Type of aggregation Method to evaluate users Evaluation mechanism Degree and distribution of manual effort Type of aggregation, evaluation mech. Role of human users Human requirement Type of architecture Technical requirement … February 26th, 2014 Interaction mode Impact of contribution … Nature of collaboration … Developing an Ontology for Enterprise Crowdsourcing Slide 8 | 18
  • 9. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Literature Review (Preliminary Result 1) February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 9 | 18
  • 10. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management System Analysis (Source 2) microtask open innovation and co-creation design job marketplace crowdfunding software testing & translation February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 10 | 18
  • 11. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management System Analysis II Task properties Platform Task specification Task description Amazon mTurk Atizio crowdSPRING … project name, task title, task description, keywords, task type (categorize, collect data, moderate, get sentiment, survey, tag, transcribe, create content), instructions title, description, image, additional information (text, document), important information, acceptance criteria, thank-you text, visibility project title, project description, external resources ... February 26th, 2014 Task allocation Time and priority duration, expiration, approval time after completion, Reward Evaluation Requester-oriented reward per assignment - qualification type, approval rate, number of approved tasks duration (start and end date/time) amount of (alternative) reward, - - end date amount of payment - … … … ParticipantOriented creation date, task available, reward amount, expiration date, duration Workflow and quality control User properties (requester and participant) number of assignments per task, status (in progress, for review, reviewed) name, login name, contact address information, prepaid balance reward, accepted languages (de, fr, en), duration user activity (ideas, projects, comments, comment evaluation, idea evaluation, time of membership) specialization, country, language product category, activity score, award, time, contributions, status user activity (reputation score, projects, awarded projects) … … … first name, last name, address (street, zip code, city, country), age, about me, website, interests, profession, job status, educational level, languages, references, career/CV, contact list first name, last name, about me, address (city, state, postal code, country), language, time zone, specialization, profile image, email, portfolio items … Developing an Ontology for Enterprise Crowdsourcing Slide 11 | 18
  • 12. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Standards and Semantic Web Vocabularies (Source 3) people, organizations, and information objects events and contextual information social networks and online communities business processes and workflows February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 12 | 18
  • 13. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management CSM Ontology I  A shared crowdsourcing model (CSM) to describe the key conceptual entities: user, project, task, requirement, reward mechanism, evaluation mechanism, and contribution  Includes 24 classes, 22 object properties and 30 datatype properties + several named individuals  Implemented in OWL using Protégé February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 13 | 18
  • 14. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management CSM Ontology II February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 14 | 18
  • 15. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management CSM Ontology Specification http://www.purl.org/csm/ February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 15 | 18
  • 16. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Example: Translate Technical Specification February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 16 | 18
  • 17. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Example SPARQL Query Which type, nature and amount of reward is appropriate for a translation task which lasts approximately 30 minutes? February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 17 | 18
  • 18. Faculty of Business and Economics | Chair of Business Informatics, especially Information Management Conclusion & Future Work  Balancing between simplicity and semantics of the crowdsourcing ontology remains a key challenge  Reuse of existing standards and vocabularies  Further evaluation steps to achieve successive adjustment and improvement  Dissemination in research in practice (standardization process) February 26th, 2014 Developing an Ontology for Enterprise Crowdsourcing Slide 18 | 18

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