Small-World File-Sharing Communities

Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a community's access patterns. To this end, we propose a novel perspective on file sharing based on the study of the relationships that form among users based on the files in which they are interested. We propose a new structure that captures common user interests in data--the data-sharing graph-- and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns

The New Ontologies: The Effect of Copyright Protection on Public Scientific Data Sharing Using Semantic Web Ontologies, 10 J. Marshall Rev. Intell. Prop. L. 181 (2010)

The semantic web is going to become an important tool for scientists who need to accurately share data given context through structured relationships. The structure that defines contextual relationships on the semantic web is known as an ontology; which is a hierarchical organization of a knowledge domain that contains entities and their relations. This paper seeks to answer whether semantic web ontologies are protectable by copyright, and regardless of the outcome, what the best practices are for the scientific community. The best practices for the scientific community should include the adoption of a machine readable ontology license which disclaims copyright protection for publication of public scientific data to assure automation of the integration of ontologies and to maximize easy access to public science materials that can be queried. Sharing and information is essential for scientists and failure to address the possibility of ontologies as a possible constraint to public data access could result in data fragmentation and lost scientific opportunities. The ability of the semantic web to annotate and reuse data relies on the social structure of science supporting data sharing as a norm and as an extension of this norm, open licensing of ontologies should be embraced

KNOWLEDGE SHARING IN VIRTUAL COMMUNITIES: THE ROLE OF GROUP IDENTITY

The rapid growth of network access and the development of Web 2.0 have resulted in the popularity of virtual communities (VCs), such as Wikipedia, Facebook, professional forums and social network communities. The impact of VCs increasingly spreads over a broad range of fields, from social and educational to business. The content (i.e., knowledge) that VC members provide is the factor that determines the growth and survival of VCs. Previous studies have investigated the factors that influence knowledge-sharing behavior in the VC environment. Despite the fact that these studies have examined the same factors, their findings have been inconsistent. In this paper, we argue that group identity mediates the relationships between knowledge sharing and these factors. This study adopts social identity theory as a theoretical foundation and collects data from a popular virtual community in Taiwan. The results show that group identity indeed mediates the relationships between VC member knowledge sharing and both organizational commitment and organizational support

Semantic web data warehousing for caGrid

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG® Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges

A multi-layer framework for personalized social tag-based applications

Recent years have seen an increasing diffusion of online communities giving their members the ability of specifying and sharing metadata concerning online resources. Such practice, also known as social or collaborative tagging, has the purpose of collecting and sharing opinions about Web resources and simplifying their retrieval. In this paper, we go one step further and show how tags can have more enhanced applications to be exploited for customizing Web content fruition. More precisely, we propose a multi-layer framework where data collected by social tagging communities are complemented with additional services. Such services provide users the ability of expressing their dis/agreement with existing tags, denoting the members they trust based on their characteristics and relationships, or specifying policies on which "quality" assessment of resources should be returned. Besides providing the formal specification of the proposed framework, we illustrate two case studies we have implemented and the experiments we have carried out in order to verify the feasibility of our approach

Glimpses through the clouds: collocates in a new light

This paper demonstrates a web-based, interactive data visualisation, allowing users to quickly inspect and browse the collocational relationships present in a corpus. The software is inspired by tag clouds, first popularised by on-line photograph sharing website Flickr (www.flickr.com). A paper based on a prototype of this Collocate Cloud visualisation was given at Digital Resources for the Humanities and Arts 2007. The software has since matured, offering new ways of navigating and inspecting the source data. It has also been expanded to analyse additional corpora, such as the British National Corpus (http://www.natcorp.ox.ac.uk/), which will be the focus of this talk

Profile of the Social Network in Photo Sharing Systems

People, who interact, cooperate or share common activities within the photo sharing system can be seen as a multirelational social network. The results of their activities, i.e. tags, comments, references to favourites and others that semantically connect users through multimedia objects, i.e. pictures are the crucial component of the semantic web concept. Every online sharing system provides data that can be used for extraction of different kinds of relations grouped in layers in the multirelational social network. Layers and their profiles were identified and studied on two, spanned in time, snapshots of Flickr population for better understanding of social network structure complexity. Additionally, for each of the identified layers, a separate strength measure was proposed in the paper. The experiments on the Flickr photo sharing system revealed that users are inspired by both the semantic relationships between objects they operate on and social links they have to other users. Moreover, the density and affluence of the social network grows over course of time

Personalization of tagging systems

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative s