46 research outputs found
Understanding personal data as a space - learning from dataspaces to create linked personal data
In this paper we argue that the space of personal data is a dataspace as defined by Franklin et al. We define a personal dataspace, as the space of all personal data belonging to a user, and we describe the logical components of the dataspace. We describe a Personal Dataspace Support Platform (PDSP) as a set of services to provide a unified view over the user’s data, and to enable new and more complex workflows over it. We show the differences from a DSSP to a PDSP, and how the latter can be realized using Web protocols and Linked APIs.<br/
On the topology of the web of data
The Web of Data consists of the open accessible structured data on the Web. This includes the evolving number of Linked Open Data data sets but also the structured data which is embedded in Web pages. In this paper we address questions related to a unified definition of distinct data sets and factors that influence different network representations of structured Web data. The contributions are (1) an algorithm to generate a data set linking structure of the em- bedded structured data sourcing from (a) the Billion Triples Challenge corpus (b) the Web Data Commons corpus, and (c) the sindice crawl, (2) a discussion on the issue of identifying distinct data sets in a generic fashion, and (3) a high level visual abstraction of the current Web of Data topology
On the topology of the web of data
The Web of Data consists of the open accessible structured data on the Web. This includes the evolving number of Linked Open Data data sets but also the structured data which is embedded in Web pages. In this paper we address questions related to a unified definition of distinct data sets and factors that influence different network representations of structured Web data. The contributions are (1) an algorithm to generate a data set linking structure of the em- bedded structured data sourcing from (a) the Billion Triples Challenge corpus (b) the Web Data Commons corpus, and (c) the sindice crawl, (2) a discussion on the issue of identifying distinct data sets in a generic fashion, and (3) a high level visual abstraction of the current Web of Data topology
a survey
Building ontologies in a collaborative and increasingly community-driven
fashion has become a central paradigm of modern ontology engineering. This
understanding of ontologies and ontology engineering processes is the result
of intensive theoretical and empirical research within the Semantic Web
community, supported by technology developments such as Web 2.0. Over 6 years
after the publication of the first methodology for collaborative ontology
engineering, it is generally acknowledged that, in order to be useful, but
also economically feasible, ontologies should be developed and maintained in a
community-driven manner, with the help of fully-fledged environments providing
dedicated support for collaboration and user participation. Wikis, and similar
communication and collaboration platforms enabling ontology stakeholders to
exchange ideas and discuss modeling decisions are probably the most important
technological components of such environments. In addition, process-driven
methodologies assist the ontology engineering team throughout the ontology
life cycle, and provide empirically grounded best practices and guidelines for
optimizing ontology development results in real-world projects. The goal of
this article is to analyze the state of the art in the field of collaborative
ontology engineering. We will survey several of the most outstanding
methodologies, methods and techniques that have emerged in the last years, and
present the most popular development environments, which can be utilized to
carry out, or facilitate specific activities within the methodologies. A
discussion of the open issues identified concludes the survey and provides a
roadmap for future research and development in this lively and promising
field
Collaborative ontology engineering: a survey
Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising fiel
A-posteriori provenance-enabled linking of publications and datasets via crowdsourcing
This paper aims to share with the digital library community different opportunities to leverage crowdsourcing for a-posteriori capturing of dataset citation graphs. We describe a practical approach, which exploits one possible crowdsourcing technique to collect these graphs from domain experts and proposes their publication as Linked Data using the W3C PROV standard. Based on our findings from a study we ran during the USEWOD 2014 workshop, we propose a semi-automatic approach that generates metadata by leveraging information extraction as an additional step to crowdsourcing, to generate high-quality data citation graphs. Furthermore, we consider the design implications on our crowdsourcing approach when non-expert participants are involved in the process<br/