• Journal of Information Management
• /
• v.40 no.2
• /
• pp.71-94
• /
• 2009

Indexing images using traditional indexing methods like taxonomy is not always efficient because of its visual content. This study examined how to apply folksonomies to image retrieval. To do this, first, we developed a category model for image tags found in Flickr. The model includes five categories and seventeen subcategories. Second, in order to evaluate the usefulness of the model to represent the various image tags as well as to investigate the end-user tagging behavior, three researchers classified the sampled image tags(141 most popular tags, 105 tags on three individual tag clouds and 3,848 image tags assigned on 156 images) according to the model. Finally, based on the research results, we proposed three methods for efficient image retrieval: extending folksonomies by combining them with ontologies; improving image retrieval efficiency using visual content and folksonomies; and updating taxonomy using folksonomies.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.18 no.2
• /
• pp.141-159
• /
• 2007

The purpose of this study was to try to conceptualize the Folksonomy, so called collaborative taggng or bookmarking and suggest its application on the Web information services. This paper explores how folksonomies could be used in web information services to enable end users to manage personal information spaces, get helped existing controlled vocabularies, and create and share their interests in online communities. Traditional classification system and philosophical issues on Folksonomy were reviewed in this paper in the context of internet based information and its services. The benefits and shortcomings of folksonomies are discussed. Some of the customizable features in existing library catalogue systems are reviewed to suggest other applicable features for web information services.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.10
• /
• pp.149-161
• /
• 2013

In this paper, we propose a novel approach to construction of classification hierarchies for user tags of folksonomies, using WordNet-based Formal Concept Analysis tool, called TagLighter, which is developed on this research. Finally, to give evidence of the usefulness of this approach in practice, we describe some experiments on user tag data of Bibsonomy.org site. The classification hierarchies of user tags constructed by our approach allow us to gain a better and further understanding and insight in tagged data during information retrieval and data analysis on the folksonomy-based systems. We expect that the proposed approach can be used in the fields of web data mining for folksonomy-based web services, social networking systems and semantic web applications.

• Journal of the Korean Society for information Management
• /
• v.27 no.2
• /
• pp.225-239
• /
• 2010

This study aims to answer whether social tagging through user collaboration could be utilized for the creation of digital knowledge of the web, and whether we could verify the quality and efficacy of social tagging to obtain benefits from it. In particular, this paper examines the inter-indexer consistency of social tagging in comparison to professional indexing. It employs two different similarity measures, both of which are based on the Vector Space Model to deal with numerous indexers. It contributes to the utilization of social tagging in the organization of the web, and encourages to adopt social knowledge in developing suitable vocabularies for resources newly generated in the digital library environment. Furthermore, the comparative analysis with two different measures produced more credible results by illustrating a similar pattern of indexing tendency in both measures.

• Journal of Information Science Theory and Practice
• /
• v.7 no.1
• /
• pp.29-38
• /
• 2019

Information retrieval is the challenge of the Web 2.0 world. The experiment of knowledge organisation in the context of abundant information available from various sources proves a major hurdle in obtaining information retrieval with greater precision and recall. The fast-changing landscape of information organisation through social networking sites at a personal level creates a world of opportunities for data scientists and also library professionals to assimilate the social data with expert created data. Thus, folksonomies or social tags play a vital role in information organisation and retrieval. The comparison of these user-created tags with expert-created index terms, author keywords and title words, will throw light on the differentiation between these sets of data. Such comparative studies show revelation of a new set of terms to enhance subject access and reflect the extent of similarity between user-generated tags and other set of terms. The CiteULike tags extracted from 5,150 scholarly journal articles in marine science were compared with corresponding Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title terms. The Jaccard similarity coefficient method was employed to compare the social tags with the above mentioned wordsets, and results proved the presence of user-generated keywords in Aquatic Science and Fisheries Abstracts descriptors, author keywords, and title words. While using information retrieval techniques like stemmer and lemmatization, the results were found to enhance keywords to subject access.

• Science of Emotion and Sensibility
• /
• v.16 no.1
• /
• pp.53-64
• /
• 2013

Folksonomies have potential problems caused by synonyms, tagging level, neologisms and so forth when retrieving music by tags. These problems can be tackled by introducing the mood intensity (Arousal and Valence value) of music as its internal tag. That is, if moods of music pieces and their mood tags are all represented internally by numeric values, A (Arousal) value and V (Valence) value, and they are retrieved by these values, then music pieces having similar mood with the mood tag of a query can be retrieved based on the similarity of their AV values though their tags are not exactly matched with the query. As a prerequisite study, in this paper, we propose the mapping table defining the relation between AV values and folksonomy tags. For analysis of the association between AV values and tags, ANOVA tests are performed on the test data collected from the well known music retrieval site last.fm. The results show that the P values for A values and V values are 0.0, which means the null hypotheses could be rejected and the alternative hypotheses could be adopted. Consequently, it is verified that the distribution of AV values depends on folksonomy tags.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.19-33
• /
• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.