Enhancing Navigation on Wikipedia with Social Tags

By Arkaitz Zubiaga. 2012

113 citations recorded by the source corpus.

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 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. As a result, a prototype on applying tags over Wikipedia is proposed in order to evaluate its effectiveness.

Subfields: Artificial Intelligence, Communication, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Information Systems

Citations in the corpus

Memory Efficient Continual Learning with Transformers (2022, methodology): “On bottom, we report BWT and FWT. Figure 2: Comparison of the % increase in the number of parameters of baseline methods and ADA on Arxiv, Reuters and Wiki-30K.”
Sparse Local Embeddings for Extreme Multi-label Classification (2015, methodology): “SLEEC also outperforms the leading tree method, FastXML, by 6% in terms of both P@1 and P@5 on WikiLSHTC and Wiki10 respectively.”
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification (2018, methodology): “We used six most common XMTC benchmark datasets (Table 1): three large-scale datasets (L ranges from 4K to 30K) : EUR-Lex1 [20], Wiki10-31K2 [32], and AmazonCat-13K 2 [19]; and three extreme-scale datasets (L ranges from 500K to 3M): Amazon-670K2 [19], Wiki-500K2 and Amazon-3M2 [19].”
Contextual Memory Trees (2018, unknown): “We compare CMT-augmented OAS to OAA on three multi-label datasets, RCV1-1K (Prabhu & Varma, 2014), AmazonCat-13K (McAuley & Leskovec, 2013), and Wiki-31K (Zubiaga, 2012; Bhatia et al., 2015).”
Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016, methodology): “Datasets: Experiments were carried out on extreme multilabel datasets including Ads-9M, Amazon-670K [6, 29], Wiki1031K [6, 52],WikiLSHTC-325K [33, 34], AmazonCat-13K [29] and EUR-Lex [31].”
Memory Efficient Continual Learning for Neural Text Classification (2022, background): “For Arxiv Papers and Reuters datasets, we created 20 tasks and for Wiki-30K dataset we created 60 tasks and we ﬁxed the number of samples per tasks to 100 for the training set.”
Large-Scale Multi-Label Text Classification on EU Legislation (2019, background): “, 2004), Amazon-13K, (McAuley and Leskovec, 2013), Wiki-30K and Wiki-500K (Zubiaga, 2012), as well as the previous EUR-LEX dataset (Mencia and Fürnkranzand, 2007), reporting that attention-based RNNs produced the best results overall (4 out of 5 datasets).”
Deep Learning for Extreme Multi-label Text Classification (2017, unknown): “As for why SLEEC , the leading target-embedding method, had the strongest performance on EUR-Lex and Wiki-30K in particular, we notice that these two datasets have much longer documents than other datasets (see column of ¯ W in Table 1).”