Hypergraph model of social tagging networks

The past few years have witnessed the great success of a new family of paradigms, so-called folksonomy, which allows users to freely associate tags to resources and efficiently manage them. In order to uncover the underlying structures and user behaviors in folksonomy, in this paper, we propose an evolutionary hypergrah model to explain the emerging statistical properties. The present model introduces a novel mechanism that one can not only assign tags to resources, but also retrieve resources via collaborative tags. We then compare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, the present model shows considerable agreement with the empirical data in following aspects: power-law hyperdegree distributions, negtive correlation between clustering coefficients and hyperdegrees, and small average distances. Furthermore, the model indicates that most tagging behaviors are motivated by labeling tags to resources, and tags play a significant role in effectively retrieving interesting resources and making acquaintance with congenial friends. The proposed model may shed some light on the in-depth understanding of the structure and function of folksonomy.Comment: 7 pages,7 figures, 32 reference

Dynamics of T2* and deformation in the placenta and myometrium during pre-labour contractions

Pre-labour uterine contractions, occurring throughout pregnancy, are an important phenomenon involving the placenta in addition to the myometrium. They alter the uterine environment and thus potentially the blood supply to the fetus and may thus provide crucial insights into the processes of labour. Assessment in-vivo is however restricted due to their unpredictability and the inaccessible nature of the utero-placental compartment. While clinical cardiotocography (CTG) only allows global, pressure-based assessment, functional magnetic resonance imaging (MRI) provides an opportunity to study contractile activity and its effects on the placenta and the fetus in-vivo. This study aims to provide both descriptive and quantitative structural and functional MR assessments of pre-labour contractions in the human uterus. A total of 226 MRI scans (18–41 weeks gestation) from ongoing research studies were analysed, focusing on free-breathing dynamic quantitative whole uterus dynamic T2* maps. These provide an indirect measure of tissue properties such as oxygenation. 22 contractile events were noted visually and both descriptive and quantitative analysis of the myometrial and placental changes including volumetric and T2* variations were undertaken. Processing and analysis was successfully performed, qualitative analysis shows distinct and highly dynamic contraction related characteristics including; alterations in the thickness of the low T2* in the placental bed and other myometrial areas, high intensity vessel-like structures in the myometrium, low-intensity vessel structures within the placental parenchyma and close to the chorionic plate. Quantitative evaluation shows a significant negative correlation between T2* in both contractile and not-contractile regions with gestational age (p 0.5). The quantitative and qualitative description of uterine pre-labour contractions including dynamic changes and key characteristics aims to contribute to the sparsely available in-vivo information and to provide an in-vivo tool to study this important phenomenon. Further work is required to analyse the origins of these subclinical contractions, their effects in high-risk pregnancies and their ability to determine the likelihood of a successful labour. Assessing T2* distribution as a marker for placental oxygenation could thus potentially complement clinically used cardiotocography measurements in the future

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them

An approach to building high-quality tag hierarchies from crowdsourced taxonomic tag pairs

Building taxonomies for web content is costly. An alternative is to allow users to create folksonomies, collective social classifications. However, folksonomies lack structure and their use for searching and browsing is limited. Current approaches for acquiring latent hierarchical structures from folksonomies have had limited success. We explore whether asking users for tag pairs, rather than individual tags, can increase the quality of derived tag hierarchies. We measure the usability cost, and in particular cognitive effort required to create tag pairs rather than individual tags. Our results show that when applied to tag pairs a hierarchy creation algorithm (Heymann-Benz) has superior performance than when applied to individual tags, and with little impact on usability. However, the resulting hierarchies lack richness, and could be seen as less expressive than those derived from individual tags. This indicates that expressivity, not usability, is the limiting factor for collective tagging approaches aimed at crowdsourcing taxonomie

A semantic lexicon-based approach for sense disambiguation and its WWW application

This work proposes a basic framework for resolving sense disambiguation through the use of Semantic Lexicon, a machine readable dictionary managing both word senses and lexico-semantic relations. More specifically, polysemous ambiguity characterizing Web documents is discussed. The adopted Semantic Lexicon is WordNet, a lexical knowledge-base of English words widely adopted in many research studies referring to knowledge discovery. The proposed approach extends recent works on knowledge discovery by focusing on the sense disambiguation aspect. By exploiting the structure of WordNet database, lexico-semantic features are used to resolve the inherent sense ambiguity of written text with particular reference to HTML resources. The obtained results may be extended to generic hypertextual repositories as well. Experiments show that polysemy reduction can be used to hint about the meaning of specific senses in given contexts