Re: Special Issue of Journal of Web Semantics on Data Linking

Hi Michael,

>>  these are at the essence of one aspect of systems biology, to which i
would add significance testing, i.e. most significant differentially
expressed genes.

Yes, that's a great idea - and a great venue to target the work of the
sysBio group; in the next sessions I hope to focus on hands-on, bio-hacking
using our lists of differentially expressed genes  :)

On Mon, Apr 23, 2012 at 3:11 PM, Michael Miller <
Michael.Miller@systemsbiology.org> wrote:

> hi all,
>
> very interesting, this concerns one of the areas i'm curious about the
> viability of semantic web technologies
>
> > • data similarity measures
> > • similarity spreading measures
> > • schema-based similarity measures
> > • candidate dataset selection and datasets similarity measures
> > • statistical analysis techniques
> > • semi-supervised, learning-based data linking methods
> > • optimization methods for computing similarity
>
> these are at the essence of one aspect of systems biology, to which i
> would add significance testing, i.e. most significant differentially
> expressed genes.
>
> can our bioRDF paper be extended to show how gene sets can be created and
> compared using semantic web technologies from raw gene expression data?
>
> cheers,
> michael
>
> Michael Miller
> Software Engineer
> Institute for Systems Biology
>
> > -----Original Message-----
> > From: M. Scott Marshall [mailto:mscottmarshall@gmail.com]
> > Sent: Monday, April 23, 2012 2:54 AM
> > To: HCLS
> > Subject: Fwd: Special Issue of Journal of Web Semantics on Data Linking
> >
> > FYI -Scott
> > ---------- Forwarded message ----------
> > From: François Scharffe <francois.scharffe@lirmm.fr>
> > Date: Mon, Apr 23, 2012 at 11:05 AM
> > Subject: Special Issue of Journal of Web Semantics on Data Linking
> > To: "public-lod@w3.org" <public-lod@w3.org>
> >
> >
> > * Apologies for cross-posting *
> >
> > This special issue of the Journal of Web Semantics focuses on the
> > problem of finding links between datasets published as linked data.
> >
> > Today the web of data has become a reality. The ever increasing number
> > of datasets published as RDF according to the linked data principles,
> > the support of major search engines, e-commerce sites and social
> > networks give no doubt that the early scenarios of the semantic Web
> > vision will soon become a reality.
> >
> > The power of the web lies in its networked structure, in the
> > connections between the resources it contains. Similarly, linked data
> > enable the interlinking of data resources so that databases become
> > interconnected and the information they contain become part of a huge
> > distributed database. The transformation of the Web from a “Web of
> > documents” into a “Web of data”, as well as the availability of large
> > collections of sensor generated data (“internet of things”), is
> > leading to a new generation of Web applications based on the
> > integration of both data and services. At the same time, new data are
> > published every day out of user generated contents and public Web
> > sites.
> >
> > This emergence of the Web of data raises many challenges, such as the
> > need of comparing and matching data with the goal of resolving the
> > multiplicity of data references to the same real-world objects and of
> > finding useful and relevant similarities and correspondences among
> > data. The Web needs techniques and tools for the discovery of data
> > links, and a suitable theory for the understanding and definition of
> > the data links meaning.
> >
> > About data links, one of the most important goals is to provide means
> > to ensure that the interconnection between data is effective. The
> > design of algorithms, methodologies, languages and tools that provide
> > more efficient and automated ways to link data is essential for the
> > growth of the Web of data rather than a set of disjoint data islands.
> >
> > While the problems of entity resolution have been studied in the
> > database community for a long time, the Web of data environment
> > presents new important challenges at different levels. Large volumes
> > of data and the variety of repositories which have to be processed
> > rise the need for scalable linking techniques which require minimal
> > user involvement. On the other hand, in cases where user configuration
> > effort is required, there is a need for tools to be usable by
> > non-experts in the domain.
> >
> > Given that published data links can be used by automatic reasoning
> > tools, it is important to capture the meaning of links in a precise
> > way. Since quality of automatically generated links can vary, their
> > provenance and reliability have to be modelled in an explicit way.
> > Finally, to capture and compare the reliability of different tools and
> > techniques, there is a need for evaluation methods for automatic data
> > linking approaches.
> >
> > Challenges
> >
> > • Automating the process of finding links between Web datasets
> > • Scaling data linking algorithms
> > • Representation and interpretation of links
> > • Providing efficient user interfaces and interaction methods
> > • Modeling and reasoning on links trust and provenance information
> >
> > Topics of Interest
> >
> > The topics of interest for this special issue include but are not
> > limited to the following.
> >
> > • data linking tools and frameworks
> > • techniques for automated data linking
> > • data similarity measures
> > • similarity spreading measures
> > • schema-based similarity measures
> > • candidate dataset selection and datasets similarity measures
> > • statistical analysis techniques
> > • semi-supervised, learning-based data linking methods
> > • optimization methods for computing similarity
> > • web data sampling techniques
> > • identity representation and semantics
> > • reasoning on links, link propagation
> > • user interaction for link elicitation and validation
> > • provenance and trust models on links
> > • methods for link quality assessment
> > • innovative applications using links
> > • evaluation of data linking techniques and tools
> >
> > Important Dates
> >
> > We will review papers on a rolling basis as they are submitted and
> > explicitly encourage submissions well before the final deadline.
> >
> > • 1 June: submission deadline
> > • 1 September: initial decisions and notifications
> > • 1 October: major/minor revisions due
> > • 1 November: final minor revisions due
> > • 1 December: final decisions and notifications
> > • 1 January: preprints available publication in 2013
> >
> >
> > Instructions for submission
> >
> > Please see the author guidelines for detailed instructions before you
> > submit. Submissions should be conducted through Elsevier’s Electronic
> > Submission System. More details on the Journal of Web Semantics can be
> > found on its homepage. See the JWS Guide for Authors for details on
> > the submission process.
> >
> >
> > Editors
> >
> > • Alfio Ferrara (Università degli Studi di Milano)
> > • Andriy Nikolov (Open University)
> > • François Scharffe (LIRMM, Université de Montpellier 2)
>
>


-- 
Helena F. Deus
Post-Doctoral Researcher at DERI/NUIG
http://lenadeus.info/