Conference Paper

Ontology of Folksonomy: A New Modelling Method.

Conference: Proceedings of the Semantic Authoring, Annotation and Knowledge Markup Workshop (SAAKM2007) located at the 4th International Conference on Knowledge Capture (KCap 2007), Whistler, British Columbia, Canada, October 28-31, 2007
Source: DBLP

ABSTRACT ABSTRACT 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. Categories and Subject Descriptors H.1.1 [Models and Principles]: Systems and Information Theory – Information theory. General Terms

Full-text

Available from: Francisco Echarte, Jun 12, 2015
0 Followers
 · 
105 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Web 2.0 has brought many collaborative and novel applications which transformed the web as a medium and resulted in its exponential growth. Tagging systems are one of these killer applications. Tags are in free-form but represent the link between objective information and users’ cognitive information. However, tags have ambiguity problem reducing precision. Hence search and retrieval pose a challenge on folksonomy systems which have flat, unstructured, non-hierarchical organization with unsupervised vocabulary. We present a brief survey of different approaches for adding semantics in folksonomies thus bringing structure and precision in search and navigation. We did comparative analysis to estimate the significance of each source of semantics. Then, we have categorized the approaches in a systematic way and summarized the feature set support. Based on the survey we end up with recommendations. Our survey and conclusion will prove to be relevant and beneficial for engineers and designers aiming to design and maintain well structured folksonomy with precise search and navigation results.
    Multimedia Tools and Applications 10/2014; DOI:10.1007/s11042-014-2309-3 · 1.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: "Skills and Expertise" is a data-driven feature on LinkedIn, the world's largest professional online social network, which allows members to tag themselves with topics representing their areas of expertise. In this work, we present our experiences developing this large-scale topic extraction pipeline, which includes constructing a folksonomy of skills and expertise and implementing an inference and recommender system for skills. We also discuss a consequent set of applications, such as Endorsements, which allows members to tag themselves with topics representing their areas of expertise and for their connections to provide social proof, via an "endorse" action, of that member's competence in that topic.
    8th ACM Conference on Recommender Systems; 10/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Folksonomies are a widely used tool of collaboratively creating and managing tags to annotate and categorize Internet resources (Web 2.0). The process of annotation and tag management by users of social networks is extremely easy and simple; however, it involves serious problems of navigation and search unlike what happens with taxonomies, thesauri and ontologies. The use of fuzzy similarity measures allows the correct identification of syntactic variations when tag lengths are greater or equal than five symbols, been inadequate for smaller length tags. This article presents a method that combines both fuzzy similarity and cosine measures in order to provide a proper classification of tags even with smaller tag lengths. This method allows the proper classification of the 95% of the syntactic variations of tags analyzed in the experiments.
    IEEE Latin America Transactions 03/2010; 8(1):88-93. DOI:10.1109/TLA.2010.5453951 · 0.19 Impact Factor