The concept of folksonomy and its importance has been emerged to information categorization and retrieval domain when the social web sites let users to contribute in the content creation as well as retrieval. By creation of Web 2.0, information users could annotate various data with descriptive tags and semantic relations. To study how humans organize knowledge and tag chunks of data, it is important to investigate how they retrieve the information they want. This essay aims to explore the concept of folksonomy and its relation with the image retrieval behavior since the structure of collective knowledge on images provided by lots of users in a social media is tightly correlated with learning how people think of classification and retrieval of objects.
The research has been done on images as part of information in digital focuses on problems with indexing and retrieval of individual images and there is negligible attention given to categories of images. “In order to inform system design for indexing and retrieval of images from collections across the board (the web, social networking services, digital libraries, etc.), a continued investigation as to what types of features are described through tagging is crucial to designing systems for effective and efficient retrieval of individual images and for browsing groups of those images” (Angus, Thelwall, & Stuart, 2008). From the professional indexing perspective toward image categorization, despite efforts have been done on image classification and indexing; there are gaps between indexers’ image representations and users’ needs. Tags or so-called folksonomy by emerging interests on social tagging can be a valuable resource for comprehending the gap between professional classifiers’ and users’ perspectives.
To take an example, a cognitive approach on image seeking behavior indicates that participants prefer using keywords more than browsing in searching for images. This study also points out the role of user background knowledge that is crucial in their satisfaction with image description and indexing (Matusiak, P. 486). Some findings declare that authority seems less important than in textual information. In image information environments, most users look for an image that represents what they want. Who created the image is not considered. What matters is what an image represents, although in areas such as art or fashion design, creators and producers may play a greater role for relevance. In general, The Relevance study found that the user’s perception of topicality was still the most important factor across the information-seeking stages. (Choi and Rasmussen pt. 6.1). As content based and context based image retrieval are also considerable issues in the user study, there is strong indication that the combined use of both text and image features is more effective in the retrieval process (Goodrum, 66).
some crucial challenges existing the way of image retrieval were pointed out in the relevant researches like:
- The level of computer and internet skills varied among the participants. And to a certain extent, background knowledge of the subject matter in preceding the image retrieval (Matusiak, P. 482).
- The textual representation of images is problematic because images convey information relating to what is actually depicted in the image as well as what the image is about. For example, an image may be of a glass of wine, but be about the Christian mass (Goodrum, P. 64).
- Users do not find it easy to use the tags assigned to the images and find it hard to come up with appropriate tags that could accurately describe these images (Ruiz & Chin, pt. 4).
- Content-based image retrieval systems still have difficulties to bridge the semantic gap between the low-level representation of images and the high level concepts the user is looking for (Gosselin & Cord, pt. 1).
- Image retrieval faces many challenges like the task of describing image content is highly subjective and a picture can mean different things to different people. In other words, there could be a variety of inconsistencies between user textual queries and image annotations or descriptions (Chai, Zhang, & Jin, P. 2).
Users can be classified into different categories depending on the type of search they perform. The query itself depends on several assumptions about the data being searched and the user’s knowledge. The queries also depend on whether user had seen the picture before or not. User may search for a specific item in the collection or look for any image of objects, Scenes, events or even concepts and feelings within a category. (Jaimes pt. 3). Jose, Furner and Harper discuss that searchers often have a well-defined mental image of a potentially satisfying picture in mind, that they are happy to express this need in visual terms (P. 232).
The advent of dynamic user participation in Web 2.0 has revolutionized the social interaction and collaboration in creating the Web content and authorship of the Web which led to the invention of social networking applications. One of the main features of the Web 2.0 applications which have taken into consideration by web users is tagging. A lot of image sharing applications have used user-generated tagging systems. By means of tagging, users can categorize the shared pictures on internet that play the same role as metadata librarians or information professional apply to retrieve the information. Thus, tagging adds “information about information” or labels to digital objects in order to enable their discovery.
Folksonomy which is the combination of “folk” and “taxonomy” (qtd. in Mathes, 2004), is comprised of terms in a flat namespace, there is no hierarchy between the terms. Unlike controlled vocabulary, folksonomies are set of terms that a group of users tagged content with and they are not predetermined as classification terms. There are some drawbacks toward using folksonomies for instance, ambiguity is a feature that can lead to either application of the same tag in different ways or different tags being used for the same concept. Due to the fact that there are no explicit systematic guidelines and no scope notes in tagging systems, also lack of letter case sensitivity, there is a high chance to collapse distinct ideas into a single tag, particularly in acronyms. Furthermore, there is no standard for synonyms for instance, plural vs. singular names are often problematic since both forms can be seen in the tag list (Mathes, 2004).
Lancaster (2002) has acknowledged that uncontrolled vocabularies have some advantages, such as “allowing great specificity in retrieval.” He argues that “it seems clear that natural language will be the norm on information retrieval and the use of conventional controlled vocabularies will decrease. There are many reasons for this, such as the high costs of human intellectual process, the rapidly declining costs of storage systems, the increasing volume of text that is accessible by computer (including email and full text of magazines and newspapers) and the gradual reduction of dependence on intermediaries skilled in online searching.” (qtd. in Gakindi et al., 2009). In addition to the advantages of free indexing, Rodriguez states that “the terminology used in social tagging is very versatile because it can refer to the description of the contents but also subjective aspects, attributes or elements of the context.” (qtd. in Gakindi et al., 2009).
Moreiro (2004) points out the following with regard to folksonomies:
- There is no investment required to build documental languages.
- They are evolving languages.
- They offer a wealth of vocabulary.
- Satisfactory results are obtained when combining them with the proper terms of a specific scientific and technical environment. (qtd. in Gakindi et al., 2009).
However, tagging is not without drawbacks. Although, users are more free and comfortable to assign a wide variety of keywords than in the traditional metadata schemas used by librarians and other information professionals to classify digital objects, it is very easy to chose keywords or tags without any structure which is exceptionally disorganized (Pharo, 2008).
The current disadvantages of social tagging (folksonomies) are still a motivation for many settings to use controlled vocabularies. Apparently for classification of small assets, applying control vocabularies by information professionals would be carried out quite well as well as the information retrieval would be more systematic and convenient. In the contrary, regarding the bulky information assets like social networking applications such as Delicious and Flickr, applying the controlled vocabulary is less likely possible. It seems that when a large group of people are involved in content sharing, folksonomies can be considered as a more appropriate way of organizing information since, the total cost of time and effort for such complex system would be cheaper in this way.
Steel (2008) explores the advantages and disadvantages of folksonomies as compared to controlled vocabularies. He states “Of course, as with most new technologies, there are critics of tagging. Although some of the tension is caused by placing metadata creation in the hands of the masses, the professionals have more concerns than just loss of control of their records. Is tagging here to stay or just a fad? Will the masses be willing to continue to tag if it becomes the main source of cataloging?” (Steel, 2008, p. 71)
From the distinction between folksonomy and controlled vocabulary, one can comprehend the role of folksonomy to understand the users’ image retrieval behavior. Since individuals take an active role in providing comprehensive information about pictures they tag. In this way, it would be more clear what chunks of data is important for users and how they assign labels to images. These days, more and more organizations attempt to use social assets in their systems as far as providing opportunity for users to participate and engage with content that creates useful information and experiences toward content; such as posting comments about the images and even discussions over related topic would add values to the content. For example, the National Archive in the United Kingdom planned to add its digital image collections to Flickr in order to increase users’ interest and encourage interaction with historically significant content. (Payne, 2008)
References:
Angus, E., Thelwall, M., and Stuart, D. (2008). General patterns of tag usage among university groups in Flickr. Online Information Review, 32 (1), 89-101.
Chai, Joyce Y., Chen Zhang, and Rong Jin. (2007). “An empirical investigation of user term feedback in text-based targeted image search.” ACM Transactions on Information Systems (TOIS) 25.1 (2007).
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Gakindi, M., Grimm, S., Mastromatteo, J. D., Vahdat, M., & Zanni, A. (2009). “Advantages and Disadvantages of Implementing LibraryThing for Libraries: a Literature Review”
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Mathes, A. (2004). Folksonomies – Cooperative Classification and Communication Through Shared Metadata.
Matusiak, Krystyna K. (2006). “Information Seeking Behavior in Digital Image Collections: A Cognitive Approach.” The Journal of Academic Librarianship 32.5 (2006): 479-88. ScienceDirect. 25 July 2006.
Payne, A. (2008). “Unlocking the Archives – Using Technology to Widen Schools’ Access to The National Archives Collection in the UK.” ABM-utvikling Conference. Grand Hotel, Oslo,
Pharo, Nils. (2008). “Web Documents and Genre.” Digital Documents Lecture. Høgskolen i Oslo, Oslo, Norway. 3 Nov. 2008.
Ruiz, Miguel E., and Pok Chin. (2009). “Users’ Image Seeking Behavior in a Multilingual Tag Environment.” Cross language evaluasion forum. TrebleCLEF Coordination Action, 2009.
Steel, T. (2008) The new cooperative cataloging. Library Hi Tech, 27(1), p.68-77.
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