Ontology of Folksonomy: A New Modelling Method.
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
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ABSTRACT: Social tagging has become popular around the Internet as well as in research. The main idea behind tagging is to allow users to provide metadata to the web content from their perspective to facilitate categorization and retrieval. There are many factors that influence users' tag choice. Many studies have been conducted to reveal these factors by analysing tagging data. This paper uses two theories to identify these factors, namely the semiotics theory and activity theory. The former treats tags as signs and the latter treats tagging as an activity. The theoretical analysis produced a framework that was used to identify a number of factors. These factors can be considered as categories that can be consulted to influence user tagging choice in order to support particular tagging behaviour, such as cross-lingual tagging.Social Informatics (SocialInformatics), 2012 International Conference on; 01/2012
Conference Paper: Harvesting and Structuring Social Data in Music Information Retrieval[Show abstract] [Hide abstract]
ABSTRACT: An exponentially growing amount of music and sound re-sources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Har-vesting and structuring this information semantically would be very use-ful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extrac-tion and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted informa-tion we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology.ESWC 2014; 05/2014
Conference Paper: LinkedIn Skills: Large-Scale Topic Extraction and Inference[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