SLUA: Towards Semantic Linking of Users with Actions in Crowdsourcing
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Edward CurryFollow
Oct. 24, 2013•0 likes•8,249 views
SLUA: Towards Semantic Linking of Users with Actions in Crowdsourcing
Oct. 24, 2013•0 likes•8,249 views
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Recent advances in web technologies allow people to help solve complex problems by performing online tasks in return for money, learning, or fun. At present, human contribution is limited to the tasks defined on individual crowdsourcing platforms. Furthermore, there is a lack of tools and technologies that support matching of tasks with appropriate users, across multiple systems. A more explicit capture of the semantics of crowdsourcing tasks could enable the design and development of matchmaking services between users and tasks. The paper presents the SLUA ontology that aims to model users and tasks in crowdsourcing systems in terms of the relevant actions, capabilities, and rewards. This model describes different types of human tasks that help in solving complex problems using crowds. The paper provides examples of describing users and tasks in some real world systems, with SLUA ontology.
Edward CurryFollow
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SLUA: Towards Semantic Linking of Users with Actions in Crowdsourcing
- 1. SLUA: TOWARDS SEMANTIC LINKING OF USERS WITH ACTIONS IN CROWDSOURCING www.insight-‐centre.org Umair ul Hassan, Sean O’Riain, Edward Curry INSIGHT Centre for Data Analy4cs Na4onal University of Ireland, Galway 1st International Workshop on Crowdsourcing the Semantic Web, CrowdSem’13, Sydney, Australia
- 2. Paper Overview www.insight-‐centre.org • MoJvaJon – MulJple crowdsourcing plaBorms – Lack of tools for finding tasks – Need to query across plaPorms for skills and knowledge of workers • Problem – Enabling interoperability across crowdsourcing plaBorms – Suppor4ng users in their search for tasks – Enable task and user matching services • ContribuJon – An ini4al lightweight ontology for describing crowd sourced tasks and users with regard to human capabili4es, ac4ons, and rewards 2
- 3. Agenda www.insight-‐centre.org • Mo4va4on – Crowdsourcing Landscape – Seman4c Heterogeneity • • • • Challenges SLUA Ontology Examples Summary 3
- 4. Crowdsourcing Landscape www.insight-‐centre.org 4
- 5. Crowdsourcing Landscape www.insight-‐centre.org 5
- 6. Challenges www.insight-‐centre.org • Difficult to interoperate across crowdsourcing systems and plaPorms – e.g. searching for appropriate workers on StackExchange for Wiki edi4ng tasks • VariaJons of data semanJcs across systems and plaPorms – Different APIs used by current marketplaces • ExisJng Taxonomies plaPorm-‐centric – Categorize crowdsourcing plaBorms instead of tasks – LiPkle considera4on human of factors such as ac4ons, capabili4es, mo4va4on 6
- 7. Heterogeneous Crowds www.insight-‐centre.org Tasks Platforms Workers Collaborative Data Curation Cyber Physical Social System • Mul4ple requesters, tasks, workers, plaBorm 7
- 8. PlaBorm Heterogeneity www.insight-‐centre.org Tasks Human AcJons Required CapabiliJes Rewards Wikipedia Create Content Edit Content Moderate Content Write text Include references Highlight mistakes Domain Knowledge Wri4ng Research Social Good Quora Ask Ques4ons Answer Ques4ons Write text Domain Knowledge Reputa4on Amazon Mechanical Turk Micro Tasks Transcribe, Translate, Categorize, etc. Various Capabili4es Money TaskRabbit Physical Tasks Collect Item Deliver Item Shop Item, etc. Various Capabili4es Money Microtask Form Filling Scan Correc4on Data Verifica4on Play games Online gaming Fun 8
- 9. Proposed Solu4on www.insight-‐centre.org • A common model for describing tasks in crowdsourcing • Seman4cally Linked Users and Ac4ons (SLUA) – Lightweight semanJc descripJon of crowdsourcing tasks in terms of human capabiliJes, acJons, and rewards • SLUA – Gaelic word for… …“Crowd” 9
- 10. SLUA Design Medthdology www.insight-‐centre.org 1. Enumerate similar terms on crowdsourcing plaBorms 2. Define the main concept in each group of terms 3. Compare with exis4ng ontologies 4. Define core classes and their rela4onships 5. Extend core classes with subclasses 6. Create example instances 10
- 11. Crowdsourcing Terminology www.insight-‐centre.org Amazon Mechanical Turk Mobileworks ShorXask CrowdFlower Task HIT Task ShortTask Microtask User Worker Requester Worker Developer Solver Seeker Contributor Customer Capability Qualifica4on Filter Reward Payment Payment Reward Payment • Terms used for similar concepts in crowdsourcing plaBorms 11
- 12. Conceptual Requirements www.insight-‐centre.org • Task: A unit of work to be performed by people in the crowd • AcJon: The cogni4ve or psychomotor ac4vity that leads towards the comple4on of a task • User: The human par4cipant, commonly described as “worker” in crowdsourcing marketplaces. • Capability: The human ability, knowledge, or skill that allows a user to perform the necessary ac4ons for task comple4on. • Reward: A core concept to the mo4va4on of people in the crowd 12
- 13. Exis4ng Ontologies www.insight-‐centre.org Concept PIMO TMO HRM-‐O FOAF SIOC Task Task Task AcJon User Person Job Seeker Person UserAccount Reward Compensa4on Reputa4on Money Salary Fun Altruism Learning Capability Loca4on Loca4on Skill Skill Knowledge Ability Ability Availability Interval Personal Information Management Ontology (PIMO) Task Management Ontology (TMO) Human Resource Management Ontology (HRM-O) Friend of a Friend (FOAF) Semantically Interlinked Online Communities (SIOC) 13
- 14. SLUA Core Classes and Proper4es www.insight-‐centre.org earns User Reward performs possesses Action Capability offers includes Task requires 14
- 15. SLUA Sub-‐classes www.insight-‐centre.org • Capability – The ability of people to do things -‐ both the capacity and the opportuni4es to do things. – Main capabili4es in literature • Knowledge, Skill, Ability, and Others (e.g. Loca4on, Availability) • Reward – The benefit generated from the use of capability in both labour market and non-‐labour market ac4vi4es. – Main rewards in literature • Reputa4on, Money, Fun, Learning, Altruism or Social Good 15
- 16. SLUA Ontology www.insight-‐centre.org Reputation Money Fun Altruism Learning subClassOf earns User Reward performs possesses Action Capability offers includes Task requires subClassOf Location Skill Knowledge 16 Ability Availability
- 17. Example Task www.insight-‐centre.org Please consider adding full cita4ons to the Wikipedia ar4cle slua:Task a slua:Loca4on a hPp://www.wikipedia.org/wiki/ A3_road/tasks/1 slua:requires slua:Reward :loc1 hPp://live.dbpedia.org/ resource/London slua:Knowledge rdfs:label slua:requires slua:includes :ac1 a a :knw1 17 a :rw1 rdfs:label hPp://live.dbpedia.org/ resource/London slua:Ac4on slua:offers Wiki page edit a slua:Reputa4on
- 18. SLUA in Ac4on www.insight-‐centre.org Tasks SLUA Mediated Task Routing Task Modelling Task Routing Worker Profiling Matching Collaborative Data Curation Capability Capability Models Task Model Model Cold Start Worker Worker Profiles Worker Profiles Profiless Ordering SLUA Mediated Infrastructure Services Cyber Physical Social System Application Interface, User Interface, Identity Management, Notification Services 18 Workers
- 19. Collabora4ve Data Cura4on www.insight-‐centre.org Programmers Managers Web of Data DQ Rules & Algorithms Human Computation Entity Linking Data Fusion Relation Extraction Databases External Crowd - High Availability - Large Scale - Expertise Variety Relevance Judgment Data Verification Disambiguation Textual Content Clean Data 19 Internal Community - Domain Knowledge - High Quality Responses - Trustable
- 20. Cyber-‐Physical Social Systems www.insight-‐centre.org Sensor Data Collection Temperature of Room 202e Energy Decision Models Human Actuation Close Window Room 202e Monitored Physical Environment Located near Room 202e 20
- 21. Summary & Future Work www.insight-‐centre.org • SLUA is an ini4al step towards defining a light-‐ weight ontology for describing tasks, acJons, users, rewards, and capabiliJes in crowdsourcing plaBorms • Future Plans – Enumerate design with addi4onal crowdsourcing plaBorms – Prototype SLUA-‐based for cross plaBorm query – Task rou4ng system used match between tasks and users with SLUA descriptors 21
- 22. Further Reading www.insight-‐centre.org International Workshop on “Crowdsourcing the Semantic Web” Sydney, 21 October 2013 Ontology Available at: hXp://vocab.deri.ie/slua U. Ul Hassan, S. O’Riain, and E. Curry, “SLUA: Towards SemanJc Linking of Users with AcJons in Crowdsourcing,” in 1st InternaJonal Workshop on Crowdsourcing the SemanJc Web, 2013. hXp://deri.ie/users/umair-‐ul-‐hassan 22