Maori Ito presents Enhanced search for life science databases with proposed extension

This week we’ll hear from Maori Ito (National Institute of Biomedical Innovation, Osaka) on extensions for biomedical databases, with an opportunity to discuss these in depth.

Abstract: Lack of unified annotation makes it difficult to find specific information across a set of life science databases. Here, we discuss proposed extensions to to semantically annotate biological databases and their entries using the microdata format. We have applied this to Japanase biomedical data & resources to provide additional fields in our search results. We hope to finalize this proposal and encourage databases to adopt the extension, thereby improving the quality of search results.

W3C Wiki page :
Properties and example of markup are shown in this page.

BioHackathon2012 :
How and why, discussions and useful links are shown in this link.

BH12.12 (Japanese) : Concrete examples of markup and search results, discussions and comments are shown in this link.

Meeting URL

Toll / Intl #: +1 (646) 583-7415
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Attendee PIN #: 47479369

Dublin Core to PROV mapping

The W3C Provenance Working Group has  released a set of documents to define a framework for interchanging and representing provenance on the Web. One of these documents is the Dublin Core to PROV mapping, which describes a partial mapping from Dublin Core Terms to the PROV-O OWL2 ontology.

But why do we need a mapping between Dublin Core and PROV? Dublin Core has been typically used to describe document metadata in the Web. Many of its terms are directly related to provenance, which describe how the document has been modified, who participated in its creation, or when it was created, issued or published. Dublin Core is widely used and it has a strong community of users behind, so the alignment to a W3C specification for provenance is crucial for interoperability in the Web.

How can you use the mapping? If you are currently using Dublin Core terms and you want to derive direct PROV statements from your assertions, take a look at the direct mappings section. If you are interested in obtaining more refined qualified statements from your Dublin Core metadata, we suggest you to explore the Complex Mappings section.

How can you contribute? By sending us comments, suggestions and feedback. We aim to do a final release of the document before the end April and your comments would very appreciated.

— Post by Daniel Garijo

New prefixes added to the RDFa Core Initial Context

The W3C RDFa Working Group has added some new predefined prefixes to the RDFa Core Initial Context:

RDFa 1.1 predefined prefixes, part of the RDFa 1.1 Initial Contexts, is a convenience mechanism that can be used by RDFa 1.1 authors as CURIE prefixes without the necessity to define those explicitly through the @prefix attribute of RDFa 1.1; the RDFa Core Initial Context defines prefixes that are valid for all Host Languages for RDFa 1.1.. Implementations may choose to access the list of these prefixes (also available in Turtle format) dynamically, or hard code them in their distribution. Authors should check whether their particular tool has already been upgraded to include these new prefixes; implementers are encouraged to add them as soon as possible.

Eleven SPARQL 1.1 Specifications are W3C Recommendations

The SPARQL Working Group has completed development of its full-featured system for querying and managing data using the flexible RDF data model. It has now published eleven Recommendations for SPARQL 1.1, detailed in SPARQL 1.1 Overview. SPARQL 1.1 extends the 2008 Recommendation for SPARQL 1.0 by adding features to the query language such as aggregatessubqueriesnegationproperty paths, and an expanded set of functions and operators. Beyond the query language, SPARQL 1.1 adds other features that were widely requested, including updateservice description, a JSON results format, and support for entailment reasoning.

PROV – A Framework for Provenance Interchange

Last week, the W3C Provenance Working group released 13 documents simultaneously that together define a framework for interchanging provenance on the Web. We are really excited about this release as it a complete, full and stable definition of PROV and includes 4 Proposed Recommendations.

While 13 documents is a lot, this is because we have broken down PROV into chunks designed for particular communities and usages. As users of PROV you won’t have to focus on the entire framework just the parts that you need. For an overview of this family of documents and the intended audience check out the PROV Overview.

Here, I wanted to provide you a bit of a guide to the PROV framework and the role of the various documents.

The Core: A Data Model

At the center of PROV is a data model, PROV-DM, that defines a vocabulary for describing provenance. These terms allow for the description of provenance from data, process and agent perspectives. PROV-DM is can be written down in multiple serialization technologies. PROV defines 3 serializations.

  1. PROV-O is a lightweight OWL2 ontology designed for Linked Data and Semantic Web applications.
  2. PROV-N is a compact syntax aimed at human consumption.
  3. PROV-XML is a native xml schema specifically designed for the XML community.

Using these serializations, applications can expose and interchange provenance. PROV-DM and its serializations have specifically been designed with extensibility in mind. We already have several extensions of PROV-O designed for specific communities.

Supporting Validation

PROV-DM and the associated serializations were purposely designed to allow for flexibility in writing provenance. We wanted to make it as easy to get started as possible and to allow for adaptability as PROV is increasingly used. However, we also realized that some users want a guide as to ensure that their provenance is consistent. Just like there are HTML validators we wanted to provide PROV validators. PROV Constraints defines a set of constraints that can be used to implement validators. PROV-Constraints is backed by a formal semantics defined in PROV Sem.

Data Model extensions

Two use cases for modeling provenance are seen in multiple applications, one is the case of aggregating information into collection/dictionary type structures (e.g. a folder with files) and the other is connecting multiple provenance traces together. PROV-Dictionary and PROV-Links provide define constructs to help model these constructs.

Accessing Provenance

Finally, once you’ve modeled your provenance, you want to be able to easily expose it. PROV-AQ defines how to use already existing web mechanisms, like link headers, to make provenance available. A key part of the design of PROV-AQ was to make it independent of any serialization format, so you can use whatever best fits your needs.

Dublin Core

Dublin Core is one of the most widely published vocabularies and many of its terms are associated with provenance. Working with the DC community, we’ve defined a mapping between Dublin Core and PROV-O ( PROV-DC ). This means that applications who support PROV can easily consume provenance already exposed as Dublin Core


PROV provides a framework for writing down, validating and exchanging provenance information in an interoperable way. Already over 60 implementations support PROV and we expect more in the future. If you have an implementation, there’s still time to register yours using one of our surveys. See the Call for Implementations page for more information. PROV contains both recommendations and notes. The classification was primarily based on the amount of prior work and implementation experience the specification has.

What you can do

We are still looking for feedback on the documents: PROV-Primer, PROV-XML, PROV-DC, PROV-Dictionary, PROV-Links, PROV-Sem. You can also report your implementation. If you have questions or comments, please contact

Finally, if your a W3C member and think that PROV should be a final recommendation of the W3C encourage your AC Representative to vote for the specification.

Two RDF Vocabularies Published for Governmental Use (and more…)

The W3C Government Linked Data Working Group has published two Last Call Working Drafts:

  • The RDF Data Cube Vocabulary. This is an RDF vocabulary for publishing multidimensional data, particularly statistical data. It is compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange), a widely used ISO standard. The Data Cube Vocabulary brings essential SDMX elements to RDF, providing a standard way for governments to publish statistical information as Linked Data. Comments are welcome through 08 April.
  • Data Catalog Vocabulary (DCAT). This is an RDF vocabulary for expressing the contents of data catalogs, such as government data portals. DCAT is for catalogs of all kinds of data (not just RDF data), but uses RDF to support easy aggregation of catalogs and construction of services which can search across many unrelated catalogs. Comments are welcome through 08 April.

PROV Family of Documents Published as Proposed Recommendations

The W3C Provenance Working Group has published four Proposed Recommendation Documents along with corresponding supporting notes. You can find a complete list at the PROV Overview draft. These documents provide a framework for interchanging provenance on the Web. PROV enables one to represent and interchange provenance information using widely available formats such as RDF and XML. In addition, it provides definitions for accessing provenance information, validating it, and mapping to Dublin Core. Comments are welcome through 9 April 2013.

Linked Data Platform 1.0 Draft Published

The W3C Linked Data Platform (LDP) Working Group has published a Working Draft of Linked Data Platform 1.0. This document specifies a set of best practices and simple approach for a read-write Linked Data architecture, based on HTTP access to web resources that describe their state using the RDF data model.

Turtle now a Candidate Recommendation

Turtle, the Terse RDF Triple Language, was published by the RDF Working Group as a Candidate Recommendation. This document is intended to become a W3C Recommendation. Turtle is a concrete syntax for RDF. A Turtle document allows writing down an RDF graph in a compact textual form.

W3C publishes a Candidate Recommendation to indicate that the document is believed to be stable and to encourage implementation by the developer community. This Candidate Recommendation is expected to advance to Proposed Recommendation in the course of 2013.

The RDF Working Group specifically solicits implementations of Turtle and submission of implementation reports. Please send implementation reports and any other comments to (subscribe, archives). The Candidate Recommendation period ends 26 March 2013. All feedback is welcome.

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Turtle is a W3C Candidate Recommendation

The W3C RDF Working Group has published a Candidate Recommendataion of Turtle – A Terse RDF Triple Language. This document defines a textual syntax for RDF called Turtle that allows an RDF graph to be completely written in a compact and natural text form, with abbreviations for common usage patterns and datatypes. Turtle provides levels of compatibility with the existing N-Triples format as well as the triple pattern syntax of the SPARQL W3C Recommendation.