Ontology of Folksonomy: A New Modeling Method (original) (raw)

Ontology of Folksonomy: A New Modelling Method

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.

The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies

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.

An Integrated Approach to Drive Ontological Structure from Folksonomie

International Journal of Information Technology and Computer Science, 2014

Web 2.0 is an evolution toward a more social, interactive and collaborative web, where user is at the center of service in terms of publications and reactions. This transforms the user from his old status as a consumer to a new one as a producer. Folksonomies are one of the technologies of Web 2.0 that permit users to annotate resources on the Web. This is done by allowing users to use any keyword or tag that they find relevant. Although folksonomies require a context-independent and inter-subjective definition of meaning, many researchers have proven the existence of an implicit semantics in these unstructured data. In this paper, we propose an improvement of our previous approach to extract ontological structures from folksonomies. The major contributions of this paper are a Normalized Co-occurrences in Distinct Users (NCDU) similarity measure, and a new algorithm to define context of tags and detect ambiguous ones. We compared our similarity measure to a widely used method for identifying similar tags based on the cosine measure. We also compared the new algorithm with the Fuzzy Clustering Algorithm (FCM) used in our original approach. The evaluation shows promising results and emphasizes the advantage of our approach.

Linking Folksonomies and Ontologies for Supporting Knowledge Sharing: a State of the Art

Social tagging systems have recently become very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations: tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This report compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.

An Integrated Approach to Extracting Ontological Structures from 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.

folk2onto: Bridging the gap between social tags and ontologies

Proc. of the 1st …, 2008

Ontologies are a useful and attractive tool for classifying documents and, in general, all types of resources. In fact, if all the documents in the web were classified according to a set of standard ontologies, the job of search engines, automatic document processors, etc. would be much easier. However, ontologies are too complex to be used by the general public and, so far, are used only by specialized users. Nonetheless, a more informal type of classifying resources is becoming increasingly popular amongst the general public: social tagging or folksonomies. Many popular websites (del.icio.us, Flickr, Technorati . . .) allow users to participate by annotating web content using social tags. Although they provide an easy way to collaboratively create knowledge, these tags are difficult to machine-process. In this paper, we propose bridging the gap between folksonomies and ontologies so that that the information in social tagging systems will be made easier to process. To that effect, we present the design and implementation of a software application, folk2onto, that can be trained to map social tags into an ontology.

Bridging ontologies and folksonomies to leverage knowledge sharing on the social Web: A brief survey

2008 23rd IEEE/ACM International Conference on Automated Software Engineering - Workshops, 2008

Social tagging systems have recently became very popular as a means to classify large sets of resources shared among on-line communities over the social Web. However, the folksonomies resulting from the use of these systems revealed limitations : tags are ambiguous and their spelling may vary, and folksonomies are difficult to exploit in order to retrieve or exchange information. This article compares the recent attempts to overcome these limitations and to support the use of folksonomies with formal languages and ontologies from the Semantic Web.

TagSorting: A Tagging Environment for Collaboratively Building Ontologies

Lecture Notes in Computer Science, 2010

Social Tagging Systems (STS) empower users to classify and organize resources and to improve the retrieval performance over the tagged resources. In this paper we argue that the potential of the social process of assigning, finding, and relating symbols in collaborative tagging scenarios is currently underexploited and can be increased by extending the meta-model and using this extension to support the emergence of structured knowledge, e.g. semantic knowledge representations. We propose a model that allows tagging as well as establishing relations between any pair of resources, not just objects and tags. Moreover, we propose to use this extension to enrich and facilitate the process of building semantic knowledge representations. We (1) provide a formal description for our approach, (2) introduce an architecture to facilitate semantic knowledge derivation, and (3) present a preliminary experiment.

Ontology extraction by collaborative tagging with social networking

2008

This paper proposes integration of a social network with collaborative tagging for ontology extraction. Tripartite models of emergent ontologies based on three dimensions (i.e. users, tags, and instances) have been proposed by several researchers, but we integrate another important dimension: user-user relations, such as the friend relation in social networking services and a knows relation in Friend-Of-A-Friend (FOAF) documents. Because a lightweight ontology is a minimum commitment shared within a community, who communicates with whom is an important source of information that can be used to improve the emergent ontology. We also discuss the advanced model in where each concept in each community is considered different (and called p-concept), and show the possibility of using this model to resolve the polysemy/hononymy problem. Two case studies using our algorithms are shown: we analyze tagging and social networking data from an academic conference support system POLYPHONET and also from an advanced social system called Blue Dot. We evaluate the extracted ontologies for information recommendation, and show that our algorithm works better than others.

Emergent Ontologies by Collaborative Tagging for Knowledge Management

2012

INTRODUCTION Throughout human history, knowledge has been stored to cover different needs like education, improvement of scientific knowledge, legal support, and entertainment. However, this trend to store resources turns out a need for systems that are able to recover the information in a rapid and effective way. Currently, according to the statistics presented on the World Wide Web Size (Miniwastts Marketing Group, 2012) about 30% of the world