Semantic Web and Human Computation: The status of an emerging field

@article{Sabou2018SemanticWA,
  title={Semantic Web and Human Computation: The status of an emerging field},
  author={Marta Sabou and Lora Aroyo and Kalina Bontcheva and Alessandro Bozzon and Rehab Kamal Qarout},
  journal={Semantic Web},
  year={2018},
  volume={9},
  pages={291-302}
}
This editorial paper introduces a special issue that solicited papers at the intersection of Semantic Web and Human Computation research. Research in that inter-disciplinary space dates back a decade, and has been acknowledged as a research line of its own by a seminal research manifesto published in 2015. But where do we stand in 2018? How did this research line evolve during the last decade? How do the papers in this special issue align with the main lines of work of the community? In this… 

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References

SHOWING 1-10 OF 54 REFERENCES
Crowdsourcing and the Semantic Web: A Research Manifesto
TLDR
A roadmap to guide the evolution of the new research field that is emerging at the intersection between crowdsourcing and the Semantic Web is defined, and a list of successful or promising scenarios for both perspectives is described.
Using microtasks to crowdsource DBpedia entity classification: A study in workflow design
TLDR
This work proposes a hierarchical tree-based approach to categorize DBpedia entities according to the DBpedia ontology using human computation and paid microtasks and discusses the findings and their potential implications for the design of effective crowdsourced entity classification in DBpedia and beyond.
Towards Hybrid NER: A Study of Content and Crowdsourcing-Related Performance Factors
TLDR
The findings show that crowd workers are adept at recognizing people, locations, and implicitly identified entities within shorter microposts, which are expected to lead to the design of more advanced NER pipelines, informing the way in which tweets are chosen to be outsourced or processed by automatic tools.
Automated Semantic Validation of Crowdsourced Local Information - The Case of the Web Application "Climate Twins"
TLDR
The authors show how the domain ontology of Climate Twins can be used to semantically validate the coherence of new entries in order to prevent incorrect links.
Semantic Annotation of Data Processing Pipelines in Scientific Publications
TLDR
This paper describes a method designed to classify sentences according to the nature of the contained information, and to extract relevant named entities, which is then semantically annotated and published as linked data in open knowledge repositoriesaccording to the DMS ontology for data processing metadata.
Detecting Linked Data quality issues via crowdsourcing: A DBpedia study
TLDR
The results show that a combination of the two styles of crowdsourcing is likely to achieve more efficient results than each of them used in isolation, and that human computation is a promising and affordable way to enhance the quality of Linked Data.
A Controlled Crowdsourcing Approach for Practical Ontology Extensions and Metadata Annotations
TLDR
A new approach to ontology development and data annotation enabling users to add new metadata properties on the fly as they describe their datasets, creating terms that can be immediately adopted by others and eventually become standardized.
Choosing the right crowd: expert finding in social networks
TLDR
This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users, and defines models and methods for evaluating people's expertise by considering their profiles and by tracing their activities in social networks.
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