Search (36024 results, page 1802 of 1802)

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Abstract

Purpose - The purpose of this paper is to produce figures showing the carbon footprint of the knowledge industry - from creation to distribution and use of knowledge, and to provide comparative figures for digital distribution and access. Design/methodology/approach - An extensive literature search and environmental scan was conducted to produce data relating to the CO2 emissions from various industries and activities such as book and journal production, photocopying activities, information technology and the internet. Other sources such as the International Energy Agency (IEA), Carbon Monitoring for Action (CARMA ), Copyright Licensing Agency, UK (CLA), Copyright Agency Limited, Australia (CAL), etc., have been used to generate emission figures for production and distribution of print knowledge products versus digital distribution and access. Findings - The current practices for production and distribution of printed knowledge products generate an enormous amount of CO2. It is estimated that the book industry in the UK and USA alone produces about 1.8 million tonnes and about 11.27 million tonnes of CO2 respectively. CO2 emission for the worldwide journal publishing industry is estimated to be about 12 million tonnes. It is shown that the production and distribution costs of digital knowledge products are negligible compared to the environmental costs of production and distribution of printed knowledge products. Practical implications - Given the astounding emission figures for production and distribution of printed knowledge products, and the associated activities for access and distribution of these products, for example, emissions from photocopying activities permitted within the provisions of statutory licenses provided by agencies like CLA, CAL, etc., it is proposed that a digital distribution and access model is the way forward, and that such a system will be environmentally sustainable. Originality/value - It is expected that the findings of this study will pave the way for further research and this paper will be extremely helpful for design and development of the future knowledge distribution and access systems.

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Abstract

The Web of Linked Data can be seen as a global database, where resources are identified through URIs, are self-described (by means of the URI dereferencing mechanism), and are globally connected through RDF links. According to the Linked Data perspective, research attention is progressively shifting from data organization and representation to linkage and composition of the huge amount of data available on the Web. For example, at the time of this writing, the DBpedia knowledge base describes more than 3.5 million things, conceptualized through 672 million RDF triples, with 6.5 million external links into other RDF datasets. Useful applications have been provided for enabling people to browse this wealth of data, like Tabulator. Other systems have been implemented to collect, index, and provide advanced searching facilities over the Web of Linked Data, such as Watson and Sindice. Besides these applications, domain-specific systems to gather and mash up Linked Data have been proposed, like DBpedia Mobile and Revyu . corn. DBpedia Mobile is a location-aware client for the semantic Web that can be used on an iPhone and other mobile devices. Based on the current GPS position of a mobile device, DBpedia Mobile renders a map indicating nearby locations from the DBpedia dataset. Starting from this map, the user can explore background information about his or her surroundings. Revyu . corn is a Web site where you can review and rate whatever is possible to identify (through a URI) on the Web. Nevertheless, the potential advantages implicit in the Web of Linked Data are far from being fully exploited. Current applications hardly go beyond presenting together data gathered from different sources. Recently, research on the Web of Linked Data has been devoted to the study of models and languages to add functionalities to the Web of Linked Data by means of Linked Data services.

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Abstract

Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact-the effect of real-world activities on online actions-has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi-experimental design study that examined the presence of causal relations between real-world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check-ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi-experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions.

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Content

# How does Archivo work? Each week Archivo runs several discovery algorithms to scan for new ontologies. Once discovered Archivo checks them every 8 hours. When changes are detected, Archivo downloads and rates and archives the latest snapshot persistently on the DBpedia Databus. # Archivo's mission Archivo's mission is to improve FAIRness (findability, accessibility, interoperability, and reusability) of all available ontologies on the Semantic Web. Archivo is not a guideline, it is fully automated, machine-readable and enforces interoperability with its star rating. - Ontology developers can implement against Archivo until they reach more stars. The stars and tests are designed to guarantee the interoperability and fitness of the ontology. - Ontology users can better find, access and re-use ontologies. Snapshots are persisted in case the original is not reachable anymore adding a layer of reliability to the decentral web of ontologies.