194 research outputs found
Analyzing Tag Semantics Across Collaborative Tagging Systems
The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance
Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy
In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points
Folksonomies and clustering in the collaborative system CiteULike
We analyze CiteULike, an online collaborative tagging system where users
bookmark and annotate scientific papers. Such a system can be naturally
represented as a tripartite graph whose nodes represent papers, users and tags
connected by individual tag assignments. The semantics of tags is studied here,
in order to uncover the hidden relationships between tags. We find that the
clustering coefficient reflects the semantical patterns among tags, providing
useful ideas for the designing of more efficient methods of data classification
and spam detection.Comment: 9 pages, 5 figures, iop style; corrected typo
Participatory Patterns in an International Air Quality Monitoring Initiative
The issue of sustainability is at the top of the political and societal
agenda, being considered of extreme importance and urgency. Human individual
action impacts the environment both locally (e.g., local air/water quality,
noise disturbance) and globally (e.g., climate change, resource use). Urban
environments represent a crucial example, with an increasing realization that
the most effective way of producing a change is involving the citizens
themselves in monitoring campaigns (a citizen science bottom-up approach). This
is possible by developing novel technologies and IT infrastructures enabling
large citizen participation. Here, in the wider framework of one of the first
such projects, we show results from an international competition where citizens
were involved in mobile air pollution monitoring using low cost sensing
devices, combined with a web-based game to monitor perceived levels of
pollution. Measures of shift in perceptions over the course of the campaign are
provided, together with insights into participatory patterns emerging from this
study. Interesting effects related to inertia and to direct involvement in
measurement activities rather than indirect information exposure are also
highlighted, indicating that direct involvement can enhance learning and
environmental awareness. In the future, this could result in better adoption of
policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil
TaxoFolk: a hybrid taxonomy–folksonomy classification for enhanced knowledge navigation
Using Semantic Technologies in Digital Libraries- A Roadmap to Quality Evaluation
Abstract. In digital libraries semantic techniques are often deployed to reduce the expensive manual overhead for indexing documents, maintaining metadata, or caching for future search. However, using such techniques may cause a decrease in a collection’s quality due to their statistical nature. Since data quality is a major concern in digital libraries, it is important to be able to measure the (loss of) quality of metadata automatically generated by semantic techniques. In this paper we present a user study based on a typical semantic technique use
Evaluation of ontology enhancement tools
Mining algorithms can enhance the task of ontology establishment but methods are needed to assess the quality of their findings. Ontology establishment is a long-term interactive process, so it is important to evaluate the contribution of a mining tool at an early phase of this process so that only appropriate tools are used in later phases. We propose a method for the evaluation of such tools on their impact on ontology enhancement. We model impact as quality perceived by the expert and as statistical quality computed by an objective function. We further provide a mechanism that juxtaposes the two forms of quality. We have applied our method on an ontology enhancement tool and gained some interesting insights on the interplay between perceived impact and statistical quality. © 2006 Springer-Verlag
Niche as a determinant of word fate in online groups
Patterns of word use both reflect and influence a myriad of human activities
and interactions. Like other entities that are reproduced and evolve, words
rise or decline depending upon a complex interplay between {their intrinsic
properties and the environments in which they function}. Using Internet
discussion communities as model systems, we define the concept of a word niche
as the relationship between the word and the characteristic features of the
environments in which it is used. We develop a method to quantify two important
aspects of the size of the word niche: the range of individuals using the word
and the range of topics it is used to discuss. Controlling for word frequency,
we show that these aspects of the word niche are strong determinants of changes
in word frequency. Previous studies have already indicated that word frequency
itself is a correlate of word success at historical time scales. Our analysis
of changes in word frequencies over time reveals that the relative sizes of
word niches are far more important than word frequencies in the dynamics of the
entire vocabulary at shorter time scales, as the language adapts to new
concepts and social groupings. We also distinguish endogenous versus exogenous
factors as additional contributors to the fates of words, and demonstrate the
force of this distinction in the rise of novel words. Our results indicate that
short-term nonstationarity in word statistics is strongly driven by individual
proclivities, including inclinations to provide novel information and to
project a distinctive social identity.Comment: Supporting Information is available here:
http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0019009.s00
Semantic contextualisation of social tag-based profiles and item recommendations
Proceedigns of 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23014-1_9We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.This work was supported by the Spanish Ministry of Science
and Innovation (TIN2008-06566-C04-02), and the Community of Madrid (CCG10-
UAM/TIC-5877
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