Have a look at it!

Share/Save

The dataset, including both articles’ content and social tags, can be downloaded from here.

Share/Save

Share/Save

English

Spanish

Share/Save

Abstract

Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by the documents’ content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval.

Share/Save

Abstract

User-generated annotations on social bookmarking sites can provide interesting and promising metadata for web document management tasks like web page classification. These user-generated annotations include diverse types of information, such as tags and comments. Nonetheless, each kind of annotation has a different nature and popularity level. In this work, we analyze and evaluate the usefulness of each of these social annotations to classify web pages over a taxonomy like that proposed by the Open Directory Project. We compare them separately to the content-based classification, and also combine the different types of data to augment performance. Our experiments show encouraging results with the use of social annotations for this purpose, and we found that combining these metadata with web page content improves even more the classifier’s performance.

Share/Save

Abstract

Las anotaciones generadas por usuarios en sistemas de marcadores sociales pueden proveer metadatos interesantes y muy útiles para la clasificación de páginas web. Estas anotaciones incluyen diversos tipos de información, como etiquetas y comentarios. No obstante, cada tipo de anotación tiene una naturaleza y un nivel de popularidad diferente. En este trabajo, analizamos y evaluamos la utilidad de cada una de estas anotaciones sociales para clasificar páginas web sobre una taxonomía como la del Open Directory Project. Las comparamos por separado a la clasificación basada en contenido, y también las combinamos. Nuestros experimentos muestran resultados prometedores con la utilización de anotaciones sociales para este propósito. Y además indican que su combinación con el contenido textual mejora el rendimiento de la clasificación.

Share/Save

Abstract

Erabiltzaileek aurrez anotatutako datuak berreskuratzeko baliabide interesgarria bilakatu dira markatzaile sozialak. Mota honetako webguneek etiketarik erabilienek osatutako etiketa-lainoa erakusten dute nabigazio aukera gisa. Etiketa-laino hauek, ordea, ez dute etiketen arteko antzekotasuna ez eta edukia kontuan izaten. Lan honetan SOM mapak erabiliz etiketen arteko erlazioak definitzeko metodoa aurkezten dugu. Erlazio hauek definitzeko, web dokumentuen edukietan oinarritu gara. Emaitza bezala lortutako mapa, ondorioz, etiketa-multzo ezberdinez osatzen da, multzo horietako bakoitzean termino esanguratsuenak erakutsiz nabigazioa eta ikuskera hobetzen direlarik. Azkenik, metodologia honek izan dezakeen aplikazio erreala aztertzen dugu.

Share/Save

Abstract

Social tagging systems are becoming an interesting way to retrieve web information from previously annotated data. These sites present a tag cloud made up by the most popular tags, where neither tag grouping nor their corresponding content is considered. We present a methodology to obtain and visualize a cloud of related tags based on the use of self-organizing maps, and where the relations among tags are established taking into account the textual content of tagged documents. Each map unit can be represented by the most relevant terms of the tags it contains, so that it is possible to study and analyze the groups as well as to visualize and navigate through the relevant terms and tags.

Share/Save

Abstract

Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperforms the former, proving the suitability of unlabeled data for the learning phase in this kind of tasks. However, the suitability of unlabeled data for multiclass tasks using SVM has never been tested before. In this work, we present a study on whether unlabeled data could improve results for multiclass web page classification tasks using Support Vector Machines. As a conclusion, we encourage to rely only on labeled data, both for improving (or at least equaling) performance and for reducing the computational cost.

Share/Save