Emergent Semantics from Folksonomies: A Quantitative Study

Abstract Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances (resources) or are based on the idea that title words are responsible for generating tags for resources.

2007

Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging systems like folksonomies using ontologies. In our proposal, structured information (ontologies) can be extracted from knowledge built in a simple and collaborative way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology.

2007

Folksonomies are Web 2.0 platforms where users share resources with each other. Furthermore, they can assign keywords (called tags) to the resources for categorizing and organizing the resources. Numerous types of resources like websites (Delicious), images (Flickr), and videos (YouTube) are supported by different folksonomies. The folksonomies are easy to use and thus attract the attention of millions of users. Together with the ease they offer, there are also some problems. This thesis addresses different problems of folksonomies and proposes solutions for these problems. The first problem occurs when users search for relevant resources in folksonomies. Often, the users are not able to find all relevant resources because they don't know which tags are relevant. The second problem is assigning tags to resources. Although many folksonomies (like Delicious) recommend tags for the resources, other folksonomies (like Flickr) do not recommend any tags. Tag recommendation helps the users to easily tag their resources. The third problem is that tags and resources are lacking semantics. This leads for example to ambiguous tags. The tags are lacking semantics because they are freely chosen keywords. The automatic identification of the semantics of tags and resources helps in reducing problems that arise from this freedom of the users in choosing the tags. This thesis proposes methods which exploit semantics to address the problems of search, tag recommendation, and the identification of tag semantics. The semantics are discovered from a variety of sources. In this thesis, we exploit web search engines, online social communities and the co-occurrences of tags as sources of semantics. Using different sources for discovering semantics reduces the efforts to build systems which solve the problems mentioned earlier. This thesis evaluates the proposed methods on a large scale data set. The evaluation results suggest that it is possible to exploit the semantics for improving search, recommendation of tags, and automatic

. Bridging the gap between folksonomies and the semantic web: an experience report. 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.

Knowledge Management Research & Practice, 2010

A growing number of tagging applications have begun to provide users the ability to socialise their own keywords. Tagging, which assigns a set of keywords to resources, has become a powerful way for organising, browsing, and publicly sharing personal collections of resources on the Web. It is called folksonomies. These systems on current social websites, however, have deficiencies in defining tag's meaning, and are often blocked to users in order to reuse, share, and exchange the tags across heterogeneous websites. In this paper, we describe a semantic model for expressing folksonomies in social websites. This model, called Social Semantic Cloud of Tags, aims to provide a consistent format of representing folksonomies and some features in terms of tagging activities. We describe core concepts and relevant properties such as a popularity and usage of tags, along with deduced relationships between tags. We will discuss how this model helps to reduce drawbacks regarding tag sharing between users, applications, or folksonomies.

Lecture Notes in Computer Science, 2009

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.

Advancing Information Management through Semantic Web Concepts and Ontologies

The massive use of Internet and social networks leads us to a new dynamic environment with huge amounts of unstructured and unclassified information resources in continuous evolution. New classification, compilation, and recommendation systems based on the use of folksonomies and ontologies have appeared to deal with the requirements of data management in this environment. Nevertheless, using ontologies alone has some weaknesses due to the need of being statically modeled by a set of experts in a specific domain. On the other hand, folksonomies show a lack of formality because of their implicit ambiguity and flexibility by definition. The main objective of this chapter is to outline and evaluate a new way to exploit Web information resources and tags for bridging the gap between ontology modeling and folksonomies.

Modeling ontology of folksonomy provides a way of learning light weight ontology's which is a hot topic investigated recently. Previous approaches for modeling ontology of folksonomy either ignores semantics (synonymy, hyponymy or polysemy) or do not simultaneously consider relationships between actors (users), concepts (tags) and instances (resources) or are based on the idea that title words are responsible for generating tags for resources. Latent semantics and user-tag dependencies instead of user-word dependencies however are extremely important. In this paper we address these problems by introducing a latent topic layer into the traditional tripartite Actor-Concept-Instance graph. We thus propose an Actor-Concept-Instance-Topic (ACIT) approach to model ontology from folksonomy in a unified way by directly using tags and users of resources. We illustrate on Bibsonomy dataset that our proposed approach ACIT outperforms title words based approaches Tag-Topic (TT) and (User-Word-Topic) UWT for modeling the ontology of folksonomy.

2008

There is a growing interest on how we represent and share tagging data for the purpose of collaborative tagging systems. Conventional tags are not naturally suited for collaborative processes. Being free-text keywords, they are exposed to linguistic variations like case (upper vs lower), grammatical number (singular vs. plural) as well as human typing errors. Additionally, tags depend on the personal views of the world by individual users, and are not normalized for synonymy, morphology or any other mapping. The bottom line of the problem is that tags have no semantics whatsoever. Moreover, even if a user gives some semantics to a tag while using or viewing it, this meaning is not automatically shared with computers since it's not defined in a machine-readable way. With tagging systems increasing in popularity each day, the evolution of this technology is hindered by this problem, since tagging metadata is not readily generated and shared. In this paper we discuss approaches to represent collaborative tagging activities at a semantic level, and present conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.

Natural language processing and information systems, 2006

Folksonomies are classication schemes that emerge from the collective actions of users who tag resources with an unrestricted set of key terms. There has been a urry of activity in this domain recently with a number of high prole 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 ndings for the relationship between folksonomy and ontology are discussed.