مشخصات مقاله:
عنوان فارسی مقاله:
توانمندسازی مهارتهای مردم و توافق برای نشانه گذاری منابع وب مشترک
عنوان انگلیسی مقاله:
Leveraging crowd skills and consensus for collaborative web-resource labeling
کلمات کلیدی مقاله:
جمع سپاری، نشانه گذاری منابع وب مبتنی بر توافق، طراحی وظیفه
مناسب برای رشته های دانشگاهی زیر:
مهندسی کامپیوتر – مهندسی فناوری اطلاعات
مناسب برای گرایش های دانشگاهی زیر:
الگوریتم و محاسبات – اینترنت و شبکه های گسترده
وضعیت مقاله انگلیسی و ترجمه:
مقاله انگلیسی را میتوانید به صورت رایگان با فرمت PDF با کلیک بر روی دکمه آبی، دانلود نمایید. برای ثبت سفارش ترجمه نیز روی دکلمه قرمز رنگ کلیک نمایید. سفارش ترجمه نیازمند زمان بوده و ترجمه این مقاله آماده نمیباشد و پس از اتمام ترجمه، فایل ورد تایپ شده قابل دانلود خواهد بود.
فهرست مطالب:
Outline
Highlights
Abstract
Keywords
۱٫ Introduction
۲٫ Related work
۳٫ Collaborative web-resource labeling
۴٫ The crowdsourcing framework for CLabel task design
۵٫ Enforcing collaborative web-resource labeling
۶٫ Setting up Argo for CLabel enforcement
۷٫ Experimental results
۸٫ Concluding remarks
References
قسمتی از مقاله انگلیسی:
Abstract
In this paper, we propose a three-stage approach called CLabel for enforcing collaborative web-resource labeling in form of a crowdsourcing process. In CLabel, the results of both crowdsourcing and automated tasks are combined into a coherent process flow. CLabel leverages on crowd preferences and consensus, for capturing the different interpretations that can be associated with a considered web resource in form of different candidate labels and for selecting the most agreed candidate(s) as the final result. CLabel succeeds to be particularly appropriate for application to labeling problems and scenarios where human feelings and preferences are decisive to select the answers (i.e., labels) supported by the majority of the crowd. Moreover, CLabel succeeds in providing label variety when multiple labels are required for a suitable resource annotation, thus avoiding duplicate or repetitive labels. A real case-study of collective web-resource labeling in the music domain is presented, where we discuss the task/consensus configuration and obtained labels as well as the results of two specific tests, respectively devoted to the analysis of label variety, and to the comparison of CLabel results against a reference classification system, where music resources are labeled using predefined categories based on a mix of social-based and expert-based recommendations.
1. Introduction
In the recent years, crowdsourcing has gained a growing popularity in many application domains concerned with resource labeling, like for example item classification [1], argument discovery [2], and entity linking [3], where the use of automatic tools/procedures is not completely effective nor possible or satisfactory, and the contribution of human workers becomes decisive for improving the quality of final results. According to a widely-accepted definition proposed in [4], crowdsourcing is defined as ‘‘a type of participative on-line activity in which an individual, [. . . ], proposes to a group of workers of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task’’