6,387 research outputs found
Creating ontologies for content representation - the OntoSeed suite
Full text linkBontas Simperl EP, Schlangen D. Creating ontologies for content representation - the OntoSeed suite. In: Spaccapietra S, ed. Journal on Data Semantics 9 (u.a.: 4th International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE 2005). Lecture Notes in Computer Science, 4601. Berlin u.a.: Springer; 2007: 141-166
A-posteriori provenance-enabled linking of publications and datasets via crowdsourcing
No full textThis paper aims to share with the digital library community different opportunities to leverage crowdsourcing for a-posteriori capturing of dataset citation graphs. We describe a practical approach, which exploits one possible crowdsourcing technique to collect these graphs from domain experts and proposes their publication as Linked Data using the W3C PROV standard. Based on our findings from a study we ran during the USEWOD 2014 workshop, we propose a semi-automatic approach that generates metadata by leveraging information extraction as an additional step to crowdsourcing, to generate high-quality data citation graphs. Furthermore, we consider the design implications on our crowdsourcing approach when non-expert participants are involved in the process<br/
Who models the world?: Collaborative ontology creation and user roles in Wikidata
No full textWikidata is a collaborative knowledge graph which is central to many academic and industry IT projects. Its users are responsible for maintaining the schema that organises this knowledge into classes, properties, and attributes, which together form the Wikidata ‘ontology’. In this paper, we study the relationship between different Wikidata user roles and the quality of the Wikidata ontology. To do so we first propose a framework to evaluate the ontology as it evolves. We then cluster editing activities to identify user roles in monthly time frames. Finally, we explore how each role impacts the ontology. Our analysis shows that the Wikidata ontology has uneven breadth and depth. We identified two user roles: contributors and leaders. The second category is positively associated to ontology depth, with no significant effect on other features. Further work should investigate other dimensions to define user profiles and their influence on the knowledge graph
Please Stay vs Let’s Play: Social Pressure Incentives in Paid Collaborative Crowdsourcing
No full textCrowdsourcing via paid microtasks has traditionally been approached as an individual activity with units of work created and completed independently. Other forms of crowdsourcing have however, embraced a mixed model that further allows for interaction and collaboration. In this paper, we expand the model of collaborative crowdsourcing to explore the role of social pressure and social flow generated by partners, as sources of incentives for improved output. We designed experiments wherein a worker could request their partner to collaboratively complete more tasks than required, either not to be abandoned and lose money (social pressure), or for fun (social flow). Our experiments reveal that these socially motivated incentives can act as furtherance mechanisms improving output by over 30 % and accuracy by about 5 %
Open Data and entrepreneurship
No full textThat there is potential for entrepreneurial development of innovative, economically beneficial products and services from Open Data makes logical sense, as data is increasingly available for the creation of new insights and activities. Although innovation with open data takes place across all sizes and ages of organisations, entrepreneurs and start-up businesses are important players. This paper considers the barriers to entrepreneurship with open data, as well as the sustainability, supportive policies and impact
Social incentives in paid collaborative crowdsourcing
No full textPaid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by the members of the crowd. Other forms of crowdsourcing have, however, embraced more varied models, which allow for a greater level of participant interaction and collaboration. This article studies the feasibility and uptake of such an approach in the context of paid microtasks. Specifically, we compare engagement, task output, and task accuracy in a paired-worker model with the traditional, single-worker version. Our experiments indicate that collaboration leads to better accuracy and more output, which, in turn, translates into lower costs. We then explore the role of the social flow and social pressure generated by collaborating partners as sources of incentives for improved performance. We utilise a Bayesian method in conjunction with interface interaction behaviours to detect when one of the workers in a pair tries to exit the task. Upon this realisation, the other worker is presented with the opportunity to contact the exiting partner to stay: either for personal financial reasons (i.e., they have not completed enough tasks to qualify for a payment) or for fun (i.e., they are enjoying the task). The findings reveal that: (1) these socially motivated incentives can act as furtherance mechanisms to help workers attain and exceed their task requirements and produce better results than baseline collaborations; (2) microtask crowd workers are empathic (as opposed to selfish) agents, willing to go the extra mile to help their partners get paid; and, (3) social furtherance incentives create a win-win scenario for the requester and for the workers by helping more workers get paid by re-engaging them before they drop out
Volunteer engagement in short-term virtual citizen science projects
No full textVirtual citizen science (VCS) projects have proven to be a highly effective method to analyse large quantities of data for scientific research purposes. Yet if these projects are to achieve their goals, they must attract and maintain the interest of sufficient numbers of active, dedicated volunteers. Although CSCW and HCI research has typically focussed on designing platforms to support long-term engagement, in recent years a new project format has been trialled -- using short-term crowdsourcing activities lasting as little as 48 hours. In this paper, we explore two short-term projects to understand how they influence participant engagement in the task and discussion elements of VCS. We calculate descriptive statistics to characterise project participants. Additionally, using calculation of correlation coefficients and hypothesis testing, we identify factors influencing volunteer task engagement and the effect this has on project outcomes. Our findings contribute to the understanding of volunteer engagement in VCS
Trusts, co-ops and crowd workers: could we include crowd data workers as stakeholders in data trust design?
No full textData trusts have been proposed as mechanism through which data can be more readily exploited for a variety of aims, including economic development and social-benefit goals such as medical research or policy-making. Data Trusts, and similar data governance mechanisms such as Data Co-Ops, aim to facilitate the use and reuse of datasets across organisational boundaries and, in the process, to protect the interests of stakeholders such as data subjects. However, current discourse on Data Trusts does not acknowledge another common stakeholder in the data value chain – the crowd workers who are employed to collect, validate, curate and transform data. In this paper, we report on a preliminary qualitative investigation into how crowd data workers themselves feel datasets should be used and governed. We find that while overall remuneration is important to those workers, they also value public-benefit data use, but have reservations about delayed remuneration and the trustworthiness of both administrative processes and the crowd itself. We discuss the implications of our findings for how data trusts could be designed, and how data trusts could be used to give crowd workers a more enduring stake in the product of their work
Web science challenges in researching bug bounties
No full textThe act of searching for security flaws (vulnerabilities) in a piece of software was previously considered to be the preserve of malicious actors, or at least actors who wished to cause chaos. Increasingly, however, companies are recognising the value of running a bug bounty program, where they will pay "white hat" hackers to locate and disclose security flaws in their applications in order that they can fix it. This is known as a "bug bounty" or a "vulnerability reward program", and at present has seen comparatively little research. This paper introduces two existing research on bug bounties in two areas: as a means of regulating the sale of vulnerabilities; and as a form of crowdsourcing. We argue that the nature of bug bounties makes Web science particularly suitable to drive forward research. We identify gaps in the current literature, and propose areas which we consider to be particularly promising for future research
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