The Technological Developments of the Dutch Folktale Database (1994–2016)

L’any 1994, la base de dades holandesa de contes populars va començar com una base de dades independent i es va posar en línia el 2004. Des de l’any 2016 i després de dos projectes importants, tots els tipus de metadades es poden afegir de manera automàtica i semisupervisada: idiomes, noms, paraules clau, resums, subgèneres, motius i tipus de contes. Amb aquesta finalitat, la base de dades va analitzar una nova plataforma anomenada Omeka que s’adapta a les necessitats de moltes bases de dades en les humanitats, i que pot gestionar tot tipus de connectors. S’han utilitzat les tècniques següents: n-grames, detecció del llenguatge, reconeixement d’entitats nombrades, extracció de paraules clau, resum, bossa de paraules, aprenentatge automàtic i processament de llenguatge natural. A més de MOMFER, també s’ha afegit un motor de cerca de motius. La interpretació de dades es facilita amb els nous mitjans de visualització: mapes geogràfics, línies de temps, una xarxa de contes similars i núvols de paraules. Com que la base de dades compleix els requisits de Dublin Core, es pot connectar a bases de dades similars o a un recol·lector de dades. Recentment, s’ha creat una aplicació de mineria de dades transatlàntica per construir un recol·lector anomenat ISEBEL: Intelligent Search Engine for Belief Legends (motor de cerca intel·ligent de llegendes de creences). El recol·lector ha de ser capaç de buscar en una  base de dades holandesa, danesa i alemanya simultàniament. Més endavant s'hi poden afegir altres bases de dades

Supporting the Exploration of Online Cultural Heritage Collections:The Case of the Dutch Folktale Database

This paper demonstrates the use of a user-centred design approach for the development of generous interfaces/rich prospect browsers for an online cultural heritage collection, determining its primary user groups and designing different browsing tools to cater to their specific needs. We set out to solve a set of problems faced by many online cultural heritage collections. These problems are lack of accessibility, limited functionalities to explore the collection through browsing, and risk of less known content being overlooked. The object of our study is the Dutch Folktale Database, an online collection of tens of thousands of folktales from the Netherlands. Although this collection was designed as a research commodity for folktale experts, its primary user group consists of casual users from the general public. We present the new interfaces we developed to facilitate browsing and exploration of the collection by both folktale experts and casual users. We focus on the user-centred design approach we adopted to develop interfaces that would fit the users' needs and preferences

Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions

Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar - all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.Comment: 40 pages main paper, 5 pages appendi

Using Crowdsourcing to Investigate Perception of Narrative Similarity

For many applications measuring the similarity between documents is essential. However, little is known about how users perceive similarity between documents. This paper presents the first large-scale empirical study that investigates perception of narrative similarity using crowdsourcing. As a dataset we use a large collection of Dutch folk narratives. We study the perception of narrative similarity by both experts and non-experts by analyzing their similarity ratings and motivations for these ratings. While experts focus mostly on the plot, characters and themes of narratives, non-experts also pay attention to dimensions such as genre and style. Our results show that a more nuanced view is needed of narrative similarity than captured by story types, a concept used by scholars to group similar folk narratives. We also evaluate to what extent unsupervised and supervised models correspond with how humans perceive narrative similarity

Using crowdsourcing to investigate perception of narrative similarity

For many applications measuring the similarity between documents is essential. However, little is known about how users perceive similarity between documents. This paper presents the first large-scale empirical study that investigates perception of narrative similarity using crowdsourcing. As a dataset we use a large collection of Dutch folk narratives. We study the perception of narrative similarity by both experts and non-experts by analyzing their similarity ratings and motivations for these ratings. While experts focus mostly on the plot, characters and themes of narratives, non-experts also pay attention to dimensions such as genre and style. Our results show that a more nuanced view is needed of narrative similarity than captured by story types, a concept used by scholars to group similar folk narratives. We also evaluate to what extent unsupervised and supervised models correspond with how humans perceive narrative similarity