WordNet: a lexical database for English
Communications of the ACM
Conceptual analysis of lexical taxonomies: the case of WordNet top-level
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
The Semantics of Semantic Annotation
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Usage patterns of collaborative tagging systems
Journal of Information Science
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
What drives content tagging: the case of photos on Flickr
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Image tag clarity: in search of visual-representative tags for social images
WSM '09 Proceedings of the first SIGMM workshop on Social media
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Modeling Human Judgment of Digital Imagery for Multimedia Retrieval
IEEE Transactions on Multimedia
Agent-mediated shared conceptualizations in tagging services
Multimedia Tools and Applications
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Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters.