Crowdsourced Knowledge Acquisition: Towards Hybrid-Genre Workflows

Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced knowledge acquisition processes. Climate Quiz uses this paradigm for acquiring environmental domain knowledge from non-experts. The game's usage statistics and the quality of the produced data show that Climate Quiz has managed to attract a large number of players but noisy input data and task complexity led to low player engagement and suboptimal task throughput and data quality. To address these limitations, the authors propose embedding the game into a hybrid-genre workflow, which supplements the game with a set of tasks outsourced to micro-workers, thus leveraging the complementary nature of games with a purpose and mechanised labour platforms. Experimental evaluations suggest that such workflows are feasible and have positive effects on the game's enjoyment level and the quality of its output.

[1]  Aniket Kittur,et al.  CrowdForge: crowdsourcing complex work , 2011, UIST.

[2]  Arno Scharl,et al.  Games with a purpose for social networking platforms , 2009, HT '09.

[3]  Marta Sabou,et al.  Climate quiz: a web application for eliciting and validating knowledge from social networks , 2012, WebMedia.

[4]  Eduard H. Hovy,et al.  Annotation , 1935, Glasgow Medical Journal.

[5]  Heiner Stuckenschmidt,et al.  Crowdsourcing the assembly of concept hierarchies , 2010, JCDL '10.

[6]  Kalina BontchevaHamish,et al.  Universities of Leeds, Sheffield and York , 2022 .

[7]  Dominik Benz,et al.  Evaluation of Folksonomy Induction Algorithms , 2012, TIST.

[8]  Elena Paslaru Bontas Simperl,et al.  SpotTheLink: playful alignment of ontologies , 2011, SAC '11.

[9]  Hinrich Schütze,et al.  Active Learning with Amazon Mechanical Turk , 2011, EMNLP.

[10]  Edward A. Felgenbaum The art of artificial intelligence: themes and case studies of knowledge engineering , 1977, IJCAI 1977.

[11]  Harald Sack,et al.  RISQ! Renowned Individuals Semantic Quiz: a Jeopardy like quiz game for ranking facts , 2011, I-Semantics '11.

[12]  H. Lieberman Common Consensus : a web-based game for collecting commonsense goals , 2007 .

[13]  Leah Hoffmann,et al.  Crowd control , 2009, CACM.

[14]  Johanna Völker,et al.  GuessWhat?! Human Intelligence for Mining Linked Data , 2010 .

[15]  Aniket Kittur,et al.  Crowdsourcing user studies with Mechanical Turk , 2008, CHI.

[16]  Elena Paslaru Bontas Simperl,et al.  An Experiment in Comparing Human-Computation Techniques , 2012, IEEE Internet Computing.

[17]  Csongor Nyulas,et al.  WebProtégé: A collaborative ontology editor and knowledge acquisition tool for the Web , 2013, Semantic Web.

[18]  Sriram Subramanian,et al.  Talking about tactile experiences , 2013, CHI.

[19]  Iryna Gurevych,et al.  The People's Web Meets NLP, Collaboratively Constructed Language Resources , 2013, Theory and Applications of Natural Language Processing.

[20]  Derek Greene,et al.  Using Crowdsourcing and Active Learning to Track Sentiment in Online Media , 2010, ECAI.

[21]  Manuel Blum,et al.  Verbosity: a game for collecting common-sense facts , 2006, CHI.

[22]  Martin Hepp,et al.  Games with a Purpose for the Semantic Web , 2008, IEEE Intelligent Systems.

[23]  Aniket Kittur,et al.  CrowdForge: crowdsourcing complex work , 2011, CHI Extended Abstracts.

[24]  Elizabeth Chang,et al.  Semi-Automatic Ontology Extension Using Spreading Activation , 2005 .

[25]  Brendan T. O'Connor,et al.  Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks , 2008, EMNLP.

[26]  Harald Sack,et al.  WhoKnows? Evaluating linked data heuristics with a quiz that cleans up DBpedia , 2011, Interact. Technol. Smart Educ..

[27]  Edward A. Feigenbaum,et al.  The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering , 1977, IJCAI.

[28]  Emanuele Della Valle,et al.  Linking Smart Cities Datasets with Human Computation - The Case of UrbanMatch , 2012, SEMWEB.

[29]  Marta Sabou,et al.  Dynamic Integration of Multiple Evidence Sources for Ontology Learning , 2012, J. Inf. Data Manag..

[30]  Elena Paslaru Bontas Simperl,et al.  CrowdMap: Crowdsourcing Ontology Alignment with Microtasks , 2012, SEMWEB.

[31]  Elena Paslaru Bontas Simperl,et al.  SpotTheLink: A Game for Ontology Alignment , 2011, Wissensmanagement.

[32]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[33]  Udo Kruschwitz,et al.  Phrase detectives: Utilizing collective intelligence for internet-scale language resource creation , 2013, TIIS.

[34]  Andreas Dengel,et al.  BetterRelations: Using a Game to Rate Linked Data Triples , 2011, KI.

[35]  Benjamin B. Bederson,et al.  Human computation: a survey and taxonomy of a growing field , 2011, CHI.

[36]  Tim Berners-Lee,et al.  Linked data , 2020, Semantic Web for the Working Ontologist.

[37]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[38]  Lydia B. Chilton,et al.  TurKit: human computation algorithms on mechanical turk , 2010, UIST.

[39]  Jisup Hong,et al.  How Good is the Crowd at "real" WSD? , 2011, Linguistic Annotation Workshop.

[40]  David Vickrey,et al.  Online Word Games for Semantic Data Collection , 2008, EMNLP.

[41]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[42]  Arno Scharl,et al.  Refining non-taxonomic relation labels with external structured data to support ontology learning , 2010, Data Knowl. Eng..

[43]  G Stix,et al.  The mice that warred. , 2001, Scientific American.

[44]  Leah Hoffmann Content control , 2009, CACM.

[45]  Alon Y. Halevy,et al.  Crowdsourcing systems on the World-Wide Web , 2011, Commun. ACM.

[46]  Gianluca Demartini,et al.  ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking , 2012, WWW.

[47]  Laura A. Dabbish,et al.  Designing games with a purpose , 2008, CACM.

[48]  Udo Kruschwitz,et al.  Using Games to Create Language Resources: Successes and Limitations of the Approach , 2013, The People's Web Meets NLP.

[49]  Min-Yen Kan,et al.  Perspectives on crowdsourcing annotations for natural language processing , 2012, Language Resources and Evaluation.

[50]  Michael S. Bernstein,et al.  Soylent: a word processor with a crowd inside , 2010, UIST.

[51]  K. Bretonnel Cohen,et al.  Last Words: Amazon Mechanical Turk: Gold Mine or Coal Mine? , 2011, CL.

[52]  Tom Heath,et al.  Linked Data: Evolving the Web into a Global Data Space , 2011, Linked Data.

[53]  Kevin Crowston,et al.  From Conservation to Crowdsourcing: A Typology of Citizen Science , 2011, 2011 44th Hawaii International Conference on System Sciences.

[54]  Matteo Negri,et al.  Divide and Conquer: Crowdsourcing the Creation of Cross-Lingual Textual Entailment Corpora , 2011, EMNLP.

[55]  Peng Dai,et al.  Decision-Theoretic Control of Crowd-Sourced Workflows , 2010, AAAI.

[56]  Timothy Chklovski,et al.  Collecting paraphrase corpora from volunteer contributors , 2005, K-CAP '05.

[57]  Ted S. Sindlinger,et al.  Crowdsourcing: Why the Power of the Crowd is Driving the Future of Business , 2010 .

[58]  M. Blanchette,et al.  Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment , 2012, PloS one.

[59]  Arno Scharl,et al.  An Automated Approach to Investigating the Online Media Coverage of U.S. Presidential Elections , 2008 .