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Folksonomy as a Complex Network

2017-11-12T00:43:19-00:00

(23 Sep 2005)

Folksonomy is an emerging technology that works to classify the information over WWW through tagging the bookmarks, photos or other web-based contents. It is understood to be organized by every user while not limited to the authors of the contents and the professional editors. This study surveyed the folksonomy as a complex network. The result indicates that the network, which is composed of the tags from the folksonomy, displays both properties of small world and scale-free. However, the statistics only shows a local and static slice of the vast body of folksonomy which is still evolving.
Kaikai Shen, Lide Wu



Adaptive knowledge retrieval using semantically enriched folksonomies

2017-10-13T17:33:39-00:00

(2016), pp. 100-105, doi:10.1109/SMAP.2016.7753392

Organizations invest considerable development effort in personalized systems to reap the benefits of knowledge management. Competition and the need for innovation imply in ever-changing and emerging needs that result in costly effort to redesign such systems. A flexible knowledge management approach should achieve personalization for organizations by adapting to the domain of the organization, meanwhile simplifying the incorporation of future knowledge retrieval needs. We describe such an approach that employs a knowledge management platform at its core, which supports adding and removing multiple folksonomies, constantly enriches them with semantics and provides domain-specific recommendations and semantic search capabilities. This approach to knowledge retrieval has been applied to the domain of homemade explosives and counter-terrorism efforts as part of the HOMER project. Preliminary evaluation from the perspective of the end-users - law enforcement, security and related agencies - is presented. © 2016 IEEE.
D Pappas, I Paraskakis



The Past, Present, and Future of an Identity Theory

2017-01-29T22:26:16-00:00

Social Psychology Quarterly, Vol. 63, No. 4. (2000), pp. 284-297, doi:10.2307/2695840

Among the many traditions of research on "identity," two somewhat different yet strongly related strands of identity theory have developed. The first, reflected in the work of Stryker and colleagues, focuses on the linkages of social structures with identities. The second, reflected in the work of Burke and colleagues, focuses on the internal process of self-verification. In the present paper we review each of these strands and then discuss ways in which the two relate to and complement one another. Each provides a context for the other: the relation of social structures to identities influences the process of self-verification, while the process of self-verification creates and sustains social structures. The paper concludes with examples of potentially useful applications of identity theory to other arenas of social psychology, and with a discussion of challenges that identity theory must meet to provide a clear understanding of the relation between self and society.
Sheldon Stryker, Peter Burke



Social Tagging Systems Recommender Systems for Social Tagging Systems

2016-05-24T11:08:26-00:00

In Recommender Systems for Social Tagging Systems (2012), pp. 3-15, doi:10.1007/978-1-4614-1894-8_1

Social Tagging Systems (STS for short) are web applications where users can upload, tag, and share resources (e. g., websites, videos,photos, etc.) with other users. STS promote decentralization of content control and lead the web to be a more open and democratic environment. As we will see in the course of this book, STS put forward new challenges and opportunities for recommender systems, but before we delve into how to design and deploy efficient recommender systems for STS, in this chapter we formally define social tagging systems and their data structures, elaborate on the different recommendation tasks demanded by STS users, introduce real-world STS that already feature recommendation services, and fix the notation we will use throughout the book. The chapter is based on work published in [9].
Leandro Marinho, Andreas Hotho, Robert Jäschke, Alexandros Nanopoulos, Steffen Rendle, Lars Schmidt-Thieme, Gerd Stumme, Panagiotis Symeonidis



Folksonomy: the New Way to Serendipity

2016-05-24T11:07:17-00:00



Folksonomy expands the collaborative process by allowing contributors to index content. It rests on three powerful properties: the absence of a prior taxonomy, multiindexation and the absence of thesaurus. It concerns a more exploratory search than an entry in a search engine. Its original relationship-based structure (the three-way relationship between users, content and tags) means that folksonomy allows various modalities of curious explorations: a cultural exploration and a social exploration. The paper has two goals. Firstly, it tries to draw a general picture of the various folksonomy websites. Secundly, since labelling lacks any standardisation, folksonomies are often under threat of invasion by noise. This paper consequently tries to explore the different possible ways of regulating the self-generated indexation process. Key words: taxonomy, indexation, innovation and user-created content. n December 17 th O 2006 Time chose you as personality of the year. You, that is, you the internet user in the sense that you contributed to the history of community and collaboration on a greater scale than
Auray Nicolas, Communications Strategies, Nicolas Auray



Harvesting Social Knowledge from Folksonomies

2016-04-12T08:15:20-00:00

In Proceedings of the Seventeenth Conference on Hypertext and Hypermedia (2006), pp. 111-114, doi:10.1145/1149941.1149962

Collaborative tagging systems, or folksonomies, have the potential of becoming technological infrastructure to support knowledge management activities in an organization or a society. There are many challenges, however. This paper presents designs that enhance collaborative tagging systems to meet some key challenges: community identification, ontology generation, user and document recommendation. Design prototypes, evaluation methodology and selected preliminary results are presented.
Harris Wu, Mohammad Zubair, Kurt Maly



Folksonomy as a Complex Network

2015-12-12T17:12:23-00:00

(23 Sep 2005)

Folksonomy is an emerging technology that works to classify the information over WWW through tagging the bookmarks, photos or other web-based contents. It is understood to be organized by every user while not limited to the authors of the contents and the professional editors. This study surveyed the folksonomy as a complex network. The result indicates that the network, which is composed of the tags from the folksonomy, displays both properties of small world and scale-free. However, the statistics only shows a local and static slice of the vast body of folksonomy which is still evolving.
Kaikai Shen, Lide Wu



FAsTA: A Folksonomy-Based Automatic Metadata Generator

2015-12-12T17:11:46-00:00

Creating New Learning Experiences on a Global Scale In Creating New Learning Experiences on a Global Scale, Vol. 4753 (2007), pp. 414-419, doi:10.1007/978-3-540-75195-3_30

Folksonomies provide a free source of keywords describing web resources, however, these keywords are free form and unstructured. In this paper, we describe a novel tool that converts folksonomy tags into semantic metadata, and present a case study consisting of a framework for evaluating the usefulness of this metadata within the context of a particular eLearning application. The evaluation shows the number of ways in which the generated semantic metadata adds value to the raw folksonomy tags.
Hend Al-Khalifa, Hugh Davis



Exploring the potential for social tagging and folksonomy in art museums: Proof of concept

2015-07-03T01:51:04-00:00

New Review of Hypermedia and Multimedia, Vol. 12, No. 1. (June 2006), pp. 83-105, doi:10.1080/13614560600802940

Documentation of art museum collections has been traditionally written by and for art historians. To make art museum collections broadly accessible, and to enable art museums to engage their communities, means of access need to reflect the perspectives of other groups and communities. Social Tagging (the collective assignment of keywords to resources) and its resulting Folksonomy (the assemblage of concepts expressed in such a cooperatively developed system of classification) offer ways for art museums to engage with their communities and to understand what users of online museum collections see as important. Proof of Concept studies at The Metropolitan Museum of Art compared terms assigned by trained cataloguers and untrained cataloguers to existing museum documentation, and explored the potential for social tagging to improve access to museum collections. These preliminary studies, the results of which are reported here, have shown the potential of social tagging and folksonomy to open museum collections to new, more personal meanings. Untrained cataloguers identified content elements not described in formal museum documentation. Results from these teststhe first in the domainprovided validation for exploring social tagging and folksonomy as an access strategy within The Metropolitan Museum, motivation to proceed with a broader inter-institutional collaboration, and input into the development of a multi-institutional collaboration exploring tagging in art museums. Tags assigned by users might help bridge the semantic gap between the professional discourse of the curator and the popular language of the museum visitor. The steve collaboration (http://www.steve.museum) is building on these early studies to develop shared tools and research methods that enable social tagging of art museum collections and explore the utility of folksonomy for providing enhanced access to collections.
J Trant, With Project



Topic sense induction from social tags based on non-negative matrix factorization

2015-07-02T09:43:16-00:00

Information Sciences, Vol. 280 (October 2014), pp. 16-25, doi:10.1016/j.ins.2014.04.048

Social tagging, also noted as collaborative tagging or folksonomy, is an important way for users themselves to describe resources on the Web. The tags that the web users adopt to describe the resources are called social tags, and they have been widely used and studied. However, for the absence of a central controlled vocabulary, the semantics of the social tags are ambiguous due to constant changes of either the users’ interests or the informal definitions, which makes it hard to directly make use of these social tags in the web applications. In this paper, we propose a non-negative matrix factorization (NMF) based method to automatically induce topic senses from social tags, which can then be used for the tag disambiguation. A novel automatic evaluation method is also proposed to evaluate our method. The experiment results show that the proposed topic sense induction method can help to provide precise resources search and recommendation, which is one of the key functionalities in social tagging systems.
Junpeng Chen, Shuai Feng, Juan Liu



Utilizing user tag-based interests in recommender systems for social resource sharing websites

2015-05-25T08:26:58-00:00

Knowledge-Based Systems, Vol. 56 (January 2014), pp. 86-96, doi:10.1016/j.knosys.2013.11.001

Tag frequency, recency, and duration were combined to model the personalized preference. The social network was utilized to find similar users in the collaborative filtering. The incorporated system was applied to the social resource sharing systems. Recently collaborative tagging, also known as “folksonomy” in Web 2.0, allows users to collaboratively create and manage tags to classify and categorize dynamic content for searching and sharing. A user’s interest in social resources usually changes with time in such a dynamic and information rich environment. Additionally, a social network is one innovative characteristic in social resource sharing websites. The information from a social network provides an inference of a certain user’s interests based on the interests of this user’s network neighbors. To handle the problem of personalized interests changing gradually with time, and to utilize the benefit of the social network, this study models a personalized user interest, incorporating frequency, recency, and duration of tag-based information, and performs collaborative recommendations using the user’s social network in social resource sharing websites. The proposed method includes finding neighbors from the “social friends” network by using collaborative filtering and recommending similar resource items to the users by using content-based filtering. This study examines the proposed system’s performance using an experimental dataset collected from a social bookmarking website. The experimental results show that the hybridization of user’s preferences with frequency, recency, and duration plays an important role, and provides better performances than traditional collaborative recommendation systems. The experimental results also reveal that the friend network information can successfully collaborate, thus improving the collaborative recommendation process.
Cheng-Lung Huang, Po-Han Yeh, Cheng-Wei Lin, Den-Cing Wu



LinkedIn skills: large-scale topic extraction and inference

2015-05-12T04:56:29-00:00

In RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems (2014), pp. 1-8, doi:http://doi.acm.org/10.1145/2645710.2645729
Mathieu Bastian, Matthew Hayes, William Vaughan, Sam Shah, Peter Skomoroch, Hyungjin Kim, Sal Uryasev, Christopher Lloyd



Integrating Folksonomies with the Semantic Web

2015-04-13T16:27:01-00:00

The Semantic Web: Research and Applications In Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007), pp. 624-639, doi:10.1007/978-3-540-72667-8_44

While tags in collaborative tagging systems serve primarily an indexing purpose, facilitating search and navigation of resources, the use of the same tags by more than one individual can yield a collective classification schema. We present an approach for making explicit the semantics behind the tag space in social tagging systems, so that this collaborative organization can emerge in the form of groups of concepts and partial ontologies. This is achieved by using a combination of shallow pre-processing strategies and statistical techniques together with knowledge provided by ontologies available on the semantic web. Preliminary results on the del.icio.us and Flickr tag sets show that the approach is very promising: it generates clusters with highly related tags corresponding to concepts in ontologies and meaningful relationships among subsets of these tags can be identified.
Lucia Specia, Enrico Motta



Studying Social Tagging and Folksonomy: A Review and Framework

2015-04-10T09:45:14-00:00

Journal of Digital Information, pp. @prism.startingPage|virtual.citation.startpage@-@prism.endingPage|virtual.citation.endpage@

This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical framewor
Jennifer Trant



Folksonomy-Based Collaborative Tagging System for Classifying Visualized Information in Design Practice

2015-04-10T09:45:06-00:00

Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design In Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design, Vol. 4557 (2007), pp. 298-306, doi:10.1007/978-3-540-73345-4_34

The aim of this research is to suggest folksonomy-based collaborative tagging system for supporting designers in group who interpret visualized information such as images through grouping, labeling and classifying for design inspiration. We performed field observation and preliminary studies to examine how designers interpret visualized information in group work. We found that traditional classification methods have some problems like lack of surface and time consuming. Based on this research, we developed PC based group work application, named I-VIDI. By implementing I-VIDI based on functional requirements, we have showed how I-VIDI reduces problems found from current image classification methods such as KJ clustering and MDS. In future case study, we plan to conduct extensive user research to evaluate the system further as well as adding more functions which can be usefully applied to collaborative design work.
Hyun-oh Jung, Min-shik Son, Kun-pyo Lee



Ranking in Folksonomy Systems: Can Context Help?

2015-04-10T09:44:53-00:00

In Proceedings of the 17th ACM Conference on Information and Knowledge Management (2008), pp. 1429-1430, doi:10.1145/1458082.1458316

Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.
Fabian Abel, Nicola Henze, Daniel Krause



The Structure of Collaborative Tagging Systems

2015-04-10T09:44:05-00:00



Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
Scott Golder, Bernardo Huberman



Exploring folksonomy for personalized search

2015-04-10T09:35:39-00:00

In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008), pp. 155-162, doi:10.1145/1390334.1390363

As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.
Shengliang Xu, Shenghua Bao, Ben Fei, Zhong Su, Yong Yu



Linking folksonomy to Library of Congress subject headings: an exploratory study

2015-03-17T10:45:12-00:00

Journal of Documentation, Vol. 65, No. 6. (2009), pp. 872-900, doi:10.1108/00220410910998906

Purpose - The purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies. Design/methodology/approach - A selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word-matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree. Findings - The experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two-thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian-pattern. Research limitations/implications - The results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies. Practical implications - The linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases. Originality/value - This is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority-maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.
Kwan Yi, Lois Chan



Folksonomy and Information Retrieval

2015-03-17T10:13:18-00:00

Proceedings of the 70th Annual Meeting of the American Society for Information Science and Technology In Annual Meeting of the American Society for Information Science and Technology, Vol. 45 (November 2007)
I Peters, WG Stock



The dynamic features of Delicious, Flickr, and YouTube

2015-03-17T09:22:26-00:00

J. Am. Soc. Inf. Sci., Vol. 63, No. 1. (2012), pp. 139-162, doi:10.1002/asi.21628

This article investigates the dynamic features of social tagging vocabularies in Delicious, Flickr, and YouTube from 2003 to 2008. Three algorithms are designed to study the macro- and micro-tag growth as well as the dynamics of taggers' activities, respectively. Moreover, we propose a Tagger Tag Resource Latent Dirichlet Allocation (TTR-LDA) model to explore the evolution of topics emerging from those social vocabularies. Our results show that (a) at the macro level, tag growth in all the three tagging systems obeys power law distribution with exponents lower than 1; at the micro level, the tag growth of popular resources in all three tagging systems follows a similar power law distribution; (b) the exponents of tag growth vary in different evolving stages of resources; (c) the growth of number of taggers associated with different popular resources presents a feature of convergence over time; (d) the active level of taggers has a positive correlation with the macro-tag growth of different tagging systems; and (e) some topics evolve into several subtopics over time while others experience relatively stable stages in which their contents do not change much, and certain groups of taggers continue their interests in them.
Nan Lin, Daifeng Li, Ying Ding, Bing He, Zheng Qin, Jie Tang, Juanzi Li, Tianxi Dong



Bridging the Gap Between Folksonomies and the Semantic Web: An Experience Report

2015-03-17T09:03:07-00:00

In Workshop: Bridging the Gap between Semantic Web and Web 2.0, European Semantic Web Conference (2007)

Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both. 1
Sofia Angeletou, Marta Sabou, Lucia Specia, Enrico Motta



The social bookmark and publication management system bibsonomy

2015-03-17T07:15:09-00:00

The VLDB Journal, Vol. 19, No. 6. (December 2010), pp. 849-875, doi:10.1007/s00778-010-0208-4

Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.
Dominik Benz, Andreas Hotho, Robert Jäschke, Beate Krause, Folke Mitzlaff, Christoph Schmitz, Gerd Stumme



Ontology is Overrated: Categories, Links, and Tags

2015-03-17T07:05:45-00:00

(2005)

This piece is based on two talks I gave in the spring of 2005 -- one at the O'Reilly ETech conference in March, entitled "Ontology Is Overrated", and one at the IMCExpo in April entitled "Folksonomies & Tags: The rise of user-developed classification." The written version is a heavily edited concatenation of those two talks.
Clay Shirky



Towards the Semantic Web: Collaborative Tag Suggestions

2015-03-11T06:40:27-00:00

In Proceedings of the Collaborative Web Tagging Workshop at the WWW 2006 (2006)

Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.
Zhichen Xu, Yun Fu, Jianchang Mao, Difu Su



Tagging, Folksonomy & Co - Renaissance of Manual Indexing?

2015-03-11T04:36:43-00:00

(26 Jan 2007)

This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.
Jakob Voss



A sophisticated library search strategy using folksonomies and similarity matching

2015-03-10T11:06:44-00:00

Journal of the American Society for Information Science and Technology, Vol. 9999, No. 9999. (2009), NA, doi:10.1002/asi.21072

Libraries, private and public, offer valuable resources to library patrons. As of today, the only way to locate information archived exclusively in libraries is through their catalogs. Library patrons, however, often find it difficult to formulate a proper query, which requires using specific keywords assigned to different fields of desired library catalog records, to obtain relevant results. These improperly formulated queries often yield irrelevant results or no results at all. This negative experience in dealing with existing library systems turns library patrons away from directly querying library catalogs; instead, they rely on Web search engines to perform their searches first, and upon obtaining the initial information (e.g., titles, subject headings, or authors) on the desired library materials, they query library catalogs. This searching strategy is an evidence of failure of today's library systems. In solving this problem, we propose an enhanced library system, which allows partial, similarity matching of (a) tags defined by ordinary users at a folksonomy site that describe the content of books and (b) unrestricted keywords specified by an ordinary library patron in a query to search for relevant library catalog records. The proposed library system allows patrons posting a query Q using commonly used words and ranks the retrieved results according to their degrees of resemblance with Q while maintaining the query processing time comparable with that achieved by current library search engines.
Maria Pera, William Lund, Yiu-Kai Ng



A semantic similarity approach to predicting Library of Congress subject headings for social tags

2015-03-10T08:50:05-00:00

J. Am. Soc. Inf. Sci., Vol. 61, No. 8. (2010), pp. 1658-1672, doi:10.1002/asi.21351

Abstract 10.1002/asi.21351.abs Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf–idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf–idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings.
Kwan Yi



Relating folksonomies with Dublin Core

2015-03-10T05:41:25-00:00

International Journal of Metadata, Semantics and Ontologies, Vol. 5, No. 4. (2010), 285, doi:10.1504/ijmso.2010.035551
Maria Catarino, Ana Baptista



Usage patterns of collaborative tagging systems

2015-03-09T10:50:29-00:00

Journal of Information Science, Vol. 32, No. 2. (01 April 2006), pp. 198-208, doi:10.1177/0165551506062337

Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.
Scott Golder, Bernardo Huberman



An Integrated Approach to Extracting Ontological Structures from Folksonomies

2015-03-09T08:54:07-00:00

The Semantic Web: Research and Applications (2009), pp. 654-668, doi:10.1007/978-3-642-02121-3_48

Collaborative tagging systems have recently emerged as one of the rapidly growing web 2.0 applications. The informal social classification structure in these systems, also known as folksonomy, provides a convenient way to annotate resources by allowing users to use any keyword or tag that they find relevant. In turn, the flat and non-hierarchical structure with unsupervised vocabularies leads to low search precision and poor resource navigation and retrieval. This drawback has created the need for ontological structures which provide shared vocabularies and semantic relations for translating and integrating the different sources. In this paper, we propose an integrated approach for extracting ontological structure from folksonomies that exploits the power of low support association rule mining supplemented by an upper ontology such as WordNet.
Huairen Lin, Joseph Davis, Ying Zhou



The folksonomy tag cloud: when is it useful?

2015-03-07T10:11:34-00:00

J. Inf. Sci. In Journal of Information Science, Vol. 34, No. 1. (01 February 2008), pp. 15-29, doi:10.1177/0165551506078083

The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding information. We conducted an experiment, giving participants the option of using a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means of navigation for a folksonomy-based dataset.
James Sinclair, Michael Hall



Folksonomy as a Complex Network

2015-03-07T10:10:30-00:00

arXiv:cs.IR/0509072, Vol. 1 (September 2005), pp. 1-8

Folksonomy is an emerging technology that works to classify the information over WWW through tagging the bookmarks, photos or other web-based contents. It is understood to be organized by every user while not limited to the authors of the contents and the professional editors. This study surveyed the folksonomy as a complex network. The result indicates that the network, which is composed of the tags from the folksonomy, displays both properties of small world and scale-free. However, the statistics only shows a local and static slice of the vast body of folksonomy which is still evolving.
Kaikai Shen, Lide Wu



FolksAnnotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies

2015-03-07T09:21:12-00:00

Innovations in Information Technology, 2006 In Innovations in Information Technology, 2006 (November 2006), pp. 1-5, doi:10.1109/INNOVATIONS.2006.301927

There are many resources on the Web which are suitable for educational purposes. Unfortunately the task of identifying suitable resources for a particular educational purpose is difficult as they have not typically been annotated with educational metadata. However, many resources have now been annotated in an unstructured manner within contemporary social bookmaking services. This paper describes a novel tool called `FolksAnnotation' that creates annotations with educational semantics from the del.icio.us bookmarking service, guided by appropriate domain ontologies
Hend Al-khalifa, Hugh Davis



Evaluating the usability of a tag-based, multi-faceted knowledge organization system

2015-03-07T06:10:23-00:00

(2012)

Evaluació i desenvolupament d'una interfície per al sistema de tags, amb conceptes de jerarquia i facetes, TACKO (TAg-based Context-dependant Knowledge Organization System) desenvolupat a la Technische Universität München.
Joan Boixados Sanuy



Detecting overlapping communities in folksonomies

2015-03-05T13:49:26-00:00

In Proceedings of the 23rd ACM conference on Hypertext and social media (2012), pp. 213-218, doi:10.1145/2309996.2310032

Folksonomies like Delicious and LastFm are modeled as tripartite (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple topical interests and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of information contained in the original tripartite structure. We propose the first algorithm to detect overlapping communities in folksonomies using the complete hypergraph structure. Our algorithm converts a hypergraph into its corresponding line-graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.
Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly



Deriving ontological structure from a folksonomy

2015-03-05T13:45:35-00:00

In Proceedings of the 47th Annual Southeast Regional Conference (2009), pp. 1-2, doi:10.1145/1566445.1566512

In this paper we describe our investigation of tagging systems and the derivation of ontological structure in the form of a folksonomy from the set of tags. Tagging systems are becoming popular, because the amount of information available on some websites is becoming too large for humans to browse manually and the types of information (multimedia data) is unsuitable for the indexers used by conventional search engines to organize. However, tag-based search is very inaccurate and incomplete (low precision and recall), because the semantics of the tags is both weak and ambiguous. The basic problem is that tags are treated like keywords by search engines, which consider individual tags in isolation. However, there is additional semantics implicit in a collection of tagged data. In this paper, we innovate and investigate techniques to make the implicit semantics explicit, so that search can be improved in both precision and recall and additional utility can be derived from the tags that people associate with multimedia items (pictures, blogs, videos, etc.). Our approach is to propose hypotheses about the ontological structure inherent in a collection of tags and then attempt to verify the hypotheses statistically. We conducted more than one hundred experimental searches on Flickr with different tags and discovered by statistical analysis information about how tags are assigned by users and what ontological knowledge is implicit in these tags that can be made explicit, and ultimately, exploited.
Saurabh Lalwani, Michael Huhns



Constructing folksonomies from user-specified relations on flickr

2015-03-05T13:17:13-00:00

In Proceedings of the 18th international conference on World wide web (2009), pp. 781-790, doi:10.1145/1526709.1526814

Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies.
Anon Plangprasopchok, Kristina Lerman



Taxo Folk: A hybrid taxonomy-folksonomy classification for enhanced knowledge navigation

2014-10-13T06:50:30-00:00

Knowledge Management Research and Practice, Vol. 8, No. 1. (2010), pp. 24-32, doi:10.1057/kmrp.2009.33

Taxonomy is widely used in many of the website and directory navigation schemes for content/knowledge retrieval. However, information or content navigation support through taxonomy is often constrained due to its inability to take into account the full nomenclature and cultural nuances of knowledge seekers. The emergence and increasing adoption of collaborative tagging (social bookmarking) tools have provided lightweight and informal conceptual structures called folksonomies for knowledge retrieval. As for folksonomies, they reflect the vocabulary of the users. Hence, integrating folksonomies into a taxonomy combines the best of the two schemes as the resultant structure enhances taxonomy navigation with personsalisation for knowledge search and retrieval. This paper presents TaxoFolk, an algorithm for deriving hybrid taxonomy-folksonomy classification for enhanced knowledge navigation. The algorithm integrates folksonomy with a taxonomy through several unsupervised data mining techniques with augmented heuristics. © 2010 Operational Research Society. All rights reserved.
CC Kiu, E Tsui



Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web

2014-09-04T09:43:48-00:00

Information Processing & Management, Vol. 44, No. 4. (July 2008), pp. 1562-1579, doi:10.1016/j.ipm.2007.12.010

Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.
P Jasonmorrison



FOLKSONOMIES AND TAGGING: New developments in social bookmarking

2014-09-04T08:27:09-00:00

In ARK GROUP CONFERENCE: DEVELOPING AND IMPROVING CLASSIFICATION SCHEMES

What is the role of controlled vocabulary in a Web 2.0 world? Can we have the best of both worlds: balancing folksonomies and controlled vocabularies to help communities of users find and share information and resources most relevant to them? education.au develops and manages Australian online services for education and training. Its goal is to bring people, learning and technology together. education.au projects are increasingly involved in exploring the use of Web 2.0 developments building on user ideas, knowledge and experience, and how these might be integrated with existing information management systems. This paper presents work being undertaken in this area, particularly in relation to controlled vocabularies, and discusses the challenges faced. Education Network Australia (edna), managed by education.au, is a leading online resource collection and collaborative network for education, with an extensive repository of selected educational resources with metadata created by educators and information specialists. It uses controlled vocabularies for metadata creation and
Sarah Hayman



HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to Read

2014-09-03T17:54:08-00:00

In Proceedings of the Seventeenth Conference on Hypertext and Hypermedia (2006), pp. 31-40, doi:10.1145/1149941.1149949

In recent years, tagging systems have become increasingly popular. These systems enable users to add keywords (i.e., "tags") to Internet resources (e.g., web pages, images, videos) without relying on a controlled vocabulary. Tagging systems have the potential to improve search, spam detection, reputation systems, and personal organization while introducing new modalities of social communication and opportunities for data mining. This potential is largely due to the social structure that underlies many of the current systems.Despite the rapid expansion of applications that support tagging of resources, tagging systems are still not well studied or understood. In this paper, we provide a short description of the academic related work to date. We offer a model of tagging systems, specifically in the context of web-based systems, to help us illustrate the possible benefits of these tools. Since many such systems already exist, we provide a taxonomy of tagging systems to help inform their analysis and design, and thus enable researchers to frame and compare evidence for the sustainability of such systems. We also provide a simple taxonomy of incentives and contribution models to inform potential evaluative frameworks. While this work does not present comprehensive empirical results, we present a preliminary study of the photo-sharing and tagging system Flickr to demonstrate our model and explore some of the issues in one sample system. This analysis helps us outline and motivate possible future directions of research in tagging systems.
Cameron Marlow, Mor Naaman, Danah Boyd, Marc Davis



Folksonomies: Tidying up Tags?

2014-09-02T14:33:20-00:00

D-Lib Magazine, Vol. 12, No. 1. (January 2006), doi:10.1045/january2006-guy
Marieke Guy, Emma Tonkin



Social tagging is no substitute for controlled indexing: A comparison of Medical Subject Headings and CiteULike tags assigned to 231,388 papers

2014-09-02T07:22:57-00:00

Journal of the American Society for Information Science and Technology, Vol. 63, No. 9. (September 2012), pp. 1747-1757, doi:10.1002/asi.22653

Social tagging and controlled indexing both facilitate access to information resources. Given the increasing popularity of social tagging and the limitations of controlled indexing (primarily cost and scalability), it is reasonable to investigate to what degree social tagging could substitute for controlled indexing. In this study, we compared CiteULike tags to Medical Subject Headings (MeSH) terms for 231,388 citations indexed in MEDLINE. In addition to descriptive analyses of the data sets, we present a paper-by-paper analysis of tags and MeSH terms: the number of common annotations, Jaccard similarity, and coverage ratio. In the analysis, we apply three increasingly progressive levels of text processing, ranging from normalization to stemming, to reduce the impact of lexical differences. Annotations of our corpus consisted of over 76,968 distinct tags and 21,129 distinct MeSH terms. The top 20 tags/MeSH terms showed little direct overlap. On a paper-by-paper basis, the number of common annotations ranged from 0.29 to 0.5 and the Jaccard similarity from 2.12% to 3.3% using increased levels of text processing. At most, 77,834 citations (33.6%) shared at least one annotation. Our results show that CiteULike tags and MeSH terms are quite distinct lexically, reflecting different viewpoints/processes between social tagging and controlled indexing.
Danielle Lee, Titus Schleyer



On improving aggregate recommendation diversity and novelty in folksonomy-based social systems

2014-09-01T08:20:18-00:00

In Personal and Ubiquitous Computing, Vol. 18, No. 8. (2014), pp. 1855-1869, doi:10.1007/s00779-014-0785-0

Benefit from technical advances in the Internet of Things, many social media applications relative to folksonomy have become ubiquitous. The size and complexity of folksonomy-based systems can unfortunately lead to information overload and reduced utility for users. Consequentially, the increasing need for recommender services from users has arisen. Many efforts have been made to address recommendation accuracy as well as other issues with respect to personalized recommendation in such systems. A key challenge facing these systems is that the most useful individual recommendations are to be found among diverse niche resources while increasing diversity most often compromises accuracy. In this paper, we introduce a simple yet elegant method—Diversity-aware Personalized PageRank (DaPPR)—to address this challenge from the aggregate perspective. DaPPR exploits a balance factor to adjust the influence of a personalized ranking vector and a unified non-personalized ranking vector based on PageRank. By this, it can reduce the impact of resource popularity on recommendations and then generate more diverse and novel recommendations to users. A hybrid DaPPR model that combines two ranking processes on the user–resource and the resource–tag bipartite graphs is specifically designed to meet the requirements in folksonomy-based systems. According to solid experiments, our proposed method yields better results balancing both aggregate accuracy and aggregate diversity (novelty). Improvements of all performance metrics are also obtained compared with the existing algorithms.
Hao Wu, Xiaohui Cui, Jun He, Bo Li, Yijian Pei



Correlating user profiles from multiple folksonomies

2014-07-21T04:21:40-00:00

In HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia (2008), pp. 33-42, doi:10.1145/1379092.1379103

As the popularity of the web increases, particularly the use of social networking sites and style sharing platforms, users are becoming increasingly connected, sharing more and more information, resources, and opinions. This vast array of information presents unique opportunities to harvest knowledge about user activities and interests through the exploitation of large-scale, complex systems. Communal tagging sites, and their respective folksonomies, are one example of such a complex system, providing huge amounts of information about users, spanning multiple domains of interest. However, the current Web infrastructure provides no mechanism for users to consolidate and exploit this information since it is spread over many desperate and unconnected resources. In this paper we compare user tag-clouds from multiple folksonomies to: (a) show how they tend to overlap, regardless of the focus of the folksonomy (b) demonstrate how this comparison helps finding and aligning the user's separate identities, and (c) show that cross-linking distributed user tag-clouds enriches users profiles. During this process, we find that significant user interests are often reflected in multiple Web2.0 profiles, even though they may operate over different domains. However, due to the free-form nature of tagging, some correlations are lost, a problem we address through the implementation and evaluation of a user tag filtering architecture.
Martin Szomszor, Iván Cantador, Harith Alani



Ontologies and tag-statistics

2014-04-17T16:59:07-00:00

(5 Jan 2012)

Due to the increasing popularity of collaborative tagging systems, the research on tagged networks, hypergraphs, ontologies, folksonomies and other related concepts is becoming an important interdisciplinary topic with great actuality and relevance for practical applications. In most collaborative tagging systems the tagging by the users is completely "flat", while in some cases they are allowed to define a shallow hierarchy for their own tags. However, usually no overall hierarchical organisation of the tags is given, and one of the interesting challenges of this area is to provide an algorithm generating the ontology of the tags from the available data. In contrast, there are also other type of tagged networks available for research, where the tags are already organised into a directed acyclic graph (DAG), encapsulating the "is a sub-category of" type of hierarchy between each other. In this paper we study how this DAG affects the statistical distribution of tags on the nodes marked by the tags in various real networks. We analyse the relation between the tag-frequency and the position of the tag in the DAG in two large sub-networks of the English Wikipedia and a protein-protein interaction network. We also study the tag co-occurrence statistics by introducing a 2d tag-distance distribution preserving both the difference in the levels and the absolute distance in the DAG for the co-occurring pairs of tags. Our most interesting finding is that the local relevance of tags in the DAG, (i.e., their rank or significance as characterised by, e.g., the length of the branches starting from them) is much more important than their global distance from the root. Furthermore, we also introduce a simple tagging model based on random walks on the DAG, capable of reproducing the main statistical features of tag co-occurrence.
Gergely Tibely, Peter Pollner, Tamas Vicsek, Gergely Palla



Hypergraph topological quantities for tagged social networks

2014-03-04T21:43:06-00:00

Physical Review E, Vol. 80, No. 3. (7 September 2009), 036118, doi:10.1103/physreve.80.036118

Recent years have witnessed the emergence of a new class of social networks, that require us to move beyond previously employed representations of complex graph structures. A notable example is that of the folksonomy, an online process where users collaboratively employ tags to resources to impart structure to an otherwise undifferentiated database. In a recent paper[1] we proposed a mathematical model that represents these structures as tripartite hypergraphs and defined basic topological quantities of interest. In this paper we extend our model by defining additional quantities such as edge distributions, vertex similarity and correlations as well as clustering. We then empirically measure these quantities on two real life folksonomies, the popular online photo sharing site Flickr and the bookmarking site CiteULike. We find that these systems share similar qualitative features with the majority of complex networks that have been previously studied. We propose that the quantities and methodology described here can be used as a standard tool in measuring the structure of tagged networks.
Vinko Zlatić, Gourab Ghoshal, Guido Caldarelli



Random hypergraphs and their applications

2014-03-04T21:42:14-00:00

Phys. Rev. E, Vol. 79, No. 6. (Jun 2009), 066118, doi:10.1103/physreve.79.066118

In the last few years we have witnessed the emergence, primarily in online communities, of new types of social networks that require for their representation more complex graph structures than have been employed in the past. One example is the folksonomy, a tripartite structure of users, resources, and tags\char22labels collaboratively applied by the users to the resources in order to impart meaningful structure on an otherwise undifferentiated database. Here we propose a mathematical model of such tripartite structures that represents them as random hypergraphs. We show that it is possible to calculate many properties of this model exactly in the limit of large network size and we compare the results against observations of a real folksonomy, that of the online photography website Flickr. We show that in some cases the model matches the properties of the observed network well, while in others there are significant differences, which we find to be attributable to the practice of multiple tagging, i.e., the application by a single user of many tags to one resource or one tag to many resources.
Gourab Ghoshal, Vinko Zlatiifmmode acutecelse ćfi, Guido Caldarelli, MEJ Newman



The Language of Folksonomies: What Tags Reveal About User Classification

2014-03-03T17:41:44-00:00

Natural Language Processing and Information Systems In Natural Language Processing and Information Systems, Vol. 3999 (2006), pp. 58-69, doi:10.1007/11765448_6

Folksonomies are classification schemes that emerge from the collective actions of users who tag resources with an unrestricted set of key terms. There has been a flurry of activity in this domain recently with a number of high profile web sites and search engines adopting the practice. They have sparked a great deal of excitement and debate in the popular and technical literature, accompanied by a number of analyses of the statistical properties of tagging behavior. However, none has addressed the deep nature of folksonomies. What is the nature of a tag? Where does it come from? How is it related to a resource? In this paper we present a study in which the linguistic properties of folksonomies reveal them to contain, on the one hand, tags that are similar to standard categories in taxonomies. But on the other hand, they contain additional tags to describe class properties. The implications of the findings for the relationship between folksonomy and ontology are discussed.
Csaba Veres