Francisco Echarte

Pattern Matching Techniques to Identify Syntactic Variations of Tags in Folksonomies

Folksonomies offer an easy method to organize information in the current Web. This fact and their collaborative features have derived in an extensive involvement in many Social Web projects. However they present important drawbacks regarding their limited exploring and searching capabilities, in contrast with other methods as taxonomies, thesauruses and ontologies. One of these drawbacks is an effect of its flexibility for tagging, producing frequently multiple syntactic variations of a same tag. In this paper we study the application of two classical pattern matching techniques, Levenshtein distance for the imperfect string matching and Hamming distance for the perfect string matching, to identify syntactic variations of tags.

A Tag Clustering Method to Deal with Syntactic Variations on Collaborative Social Networks

Folksonomies have emerged as a common way of annotating and categorizing content using a set of tags that are created and managed in a collaborative way. Tags carry the semantic information within a folksonomy, and provide thus the link to ontologies. The appeal of folksonomies comes from the fact that they require a low effort for creation and maintenance since they are community-generated. However they present important drawbacks regarding their limited navigation and searching capabilities, in contrast with other methods as taxonomies, thesauruses and ontologies. One of these drawbacks is an effect of its flexibility for tagging, producing frequently multiple syntactic variations of a same tag. Similarity measures allow the correct identification of tag variations when tag lengths are greater than five symbols. In this paper we propose the use of cosine relatedness measures in order to cluster tags with lengths lower or equal than five symbols. We build a discriminator based on the combination of a fuzzy similarity and a cosine measures and we analyze the results obtained.

Improving folksonomies quality by syntactic tag variations grouping

Folksonomies offer an easy method to organize information in the current Web. This fact and their collaborative features have derived in an extensive involvement in many Social Web projects. However they present important drawbacks regarding their limited exploring and searching capabilities, in contrast with other methods as taxonomies, thesauruses and ontologies. One of these drawbacks is an effect of its flexibility for tagging, producing frequently multiple variations of a same tag. In this paper we propose a method to group syntactic variations of tags using pattern matching techniques. We propose the utilization of a fuzzy similarity measure and we conclude that this technique offers better results than other classic techniques after comparing them on a large real dataset.

ACoAR: a method for the automatic classification of annotated resources

We propose a classification method that automatically classifies annotated resources under the concepts of a classification system represented by an ontology. We use two well known systems used to classify web pages, del.icio.us for the folksonomy information and DMOZ for an existing ontology, to validate the method. Results obtained provide a correct classification rate of resources of 78%, rising to 93% when using an adequate threshold.

A self-adapted method for the categorization of social resources

Social tagging systems have become a popular system to organize information in many web 2.0 sites. They are also being rapidly adopted in enterprises to enhance information sharing, knowledge sharing and emerged as a novel categorization scheme based on the collective knowledge of people. Scalability is an issue of the categorization of the resources of social tagging systems. Scalability has highlighted a critical trade-off between accuracy and complexity. As social tagging systems evolve over time, resource categories can appear or disappear either by grouping new resources or disaggregating existing ones, and this implies the re-assignation of the resources involved to others categories. This makes the methods and/or algorithms that categorize resources of social tagging systems to be non-scalable, and then not efficiently implementable on real social tagging systems. This paper presents a simple method for categorizing resources on social tagging systems which is self-adaptive, scalable and implementable in any real social tagging system.

A self-adapting method for knowledge management in collaborative and social tagging systems

This paper presents an automatic method to group resources of collaborative-social tagging systems in semantic categories. The main goal is to self-adapt the system to represent the current knowledge.

Clustering Method for Social Network Annotations

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.

Evaluation of a Self-adapting Method for Resource Classification in Folksonomies

Nowadays, folksonomies are currently the simplest way to classify information inWeb 2.0. However, such folksonomies increase continuously their amount of information without any centralized control, complicating the knowledge representation. We analyse a method to group resources of collaborative-social tagging systems in semantic categories. It is able to automatically create the classification categories to represent the current knowledge and to self-adapt to the changes of the folksonomies, classifying the resources under categories and creating/deleting them. As opposed to current proposals that require the re-evaluation of the whole folksonomy to maintain updated the categories, our method is an incremental aggregation technique which guarantees its adaptation to highly dynamic systems without requiring a full reassessment of the folksonomy.

A method for the classification of folksonomy resources

The paper presents a method for the automatic classification of the resources of collaborative tagging systems, also called folksonomies. Folksonomies are an easy way of representing knowledge in Web 2.0 because of its simplicity. However, due to their characteristics, the information retrieval in these systems is more difficult than in classical knowledge representation systems. This method has a high degree of precision and recall, and may adjust these values with the use of different modes of classification and thresholds, improving the performances of other resource classification methods.