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[Please forward to interested colleagues]
We are proud to announce that the DBpedia Databus website
at<https://databus.dbpedia.org/>_https://databus.dbpedia.org_
<https://databus.dbpedia.org/> and the SPARQL API
at<https://databus.dbpedia.org/(repo/sparql|yasgui)>_https://databus.dbpedia.org/(repo/sparql|yasgui)_
(_docu_ <http://dev.dbpedia.org/Download_Data>) are in public beta now.
The system is usable (eat-your-own-dog-food tested) following a “working
software over comprehensive documentation” approach. Due to its many
components (website, sparql endpoints, keycloak, mods, upload client,
download client, and data debugging), we estimate approximately six
months in beta to fix bugs, implement all features and improve the
details. If you have any feedback or questions, please use
the<https://forum.dbpedia.org/>_DBpedia Forum_
<https://forum.dbpedia.org/>, the “report issues” button, or
_dbpedia@infai.org_.
The full document is available at:
_https://databus.dbpedia.org/dbpedia/publication/strategy/2019.09.09/strategy_databus_initiative.pdf_
We are looking forward to the feedback and discussion at the_14th
DBpedia Community Meeting at SEMANTiCS 2019 in Karlsruhe_
<https://wiki.dbpedia.org/events/14th-dbpedia-community-meeting-karlsruhe>
on September 12th or online.
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# Excerpt
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DBpedia Databus
The DBpedia Databus is a platform to capture invested effort by data
consumers who needed better data quality (fitness for use) in order to
use the data and give improvements back to the data source and other
consumers. DBpedia Databus enables anybody to build an automated
DBpedia-style extraction, mapping and testing for any data they need.
Databus incorporates features from DNS, Git, RSS, online forums and
Maven to harness the full workpower of data consumers.
Vision
Professional consumers of data worldwide have already built stable
cleaning and refinement chains for all available datasets, but their
efforts are invisible and not reusable. Deep, cleaned data silos exist
beyond the reach of publishers and other consumers trapped locally in
pipelines.
*Data is not oil that flows out of inflexible pipelines*. Databus breaks
existing pipelines into individual components that together form a
decentralized, but centrally coordinated data network in which data can
flow back to previous components, the original sources, or end up being
consumed by external components,
The Databus provides a platform for re-publishing these files with very
little effort (leaving file traffic as only cost factor) while offering
the full benefits of built-in system features such as automated
publication, structured querying, automatic ingestion, as well as
pluggable automated analysis, data testing via continuous integration,
and automated application deployment *(software with data)*. The impact
is highly synergistic, just a few thousand professional consumers and
research projects can expose millions of cleaned datasets, which are on
par with what has long existed in deep silos and pipelines.
1 Billion interconnected, quality-controlled Knowledge Graphs until 2025
As we are inversing the paradigm form a publisher-centric view to a data
consumer network, we will open the download valve to enable discovery
and access to massive amounts of cleaner data than published by the
original source. The main DBpedia Knowledge Graph - cleaned data from
Wikipedia in all languages and Wikidata - alone has 600k file downloads
per year complemented by downloads at over 20 chapter,
e.g.<http://es.dbpedia.org/>_http://es.dbpedia.org_
<http://es.dbpedia.org/> as well as over 8 million daily hits on the
main Virtuoso endpoint. Community extension from the alpha phase such
as<https://databus.dbpedia.org/sven-h/dbkwik/dbkwik/2019.09.02>_DBkWik_
<https://databus.dbpedia.org/sven-h/dbkwik/dbkwik/2019.09.02>,<https://databus.dbpedia.org/propan/lhd/linked-hypernyms>_LinkedHypernyms_
<https://databus.dbpedia.org/propan/lhd/linked-hypernyms> are being
loaded onto the bus and consolidated and we expect this number to reach
over 100 by the end of the year. Companies and organisations who
have<https://github.com/dbpedia/links>_previously uploaded their
backlinks here_ <https://github.com/dbpedia/links> will be able to
migrate to the databus. Other datasets are cleaned and posted. In two of
our research projects_LOD-GEOSS_
<https://www.enargus.de/pub/bscw.cgi/?op=enargus.eps2&s=14&q=BASF%20SE&v=10&m=2&id=1216225&p=1>
and<http://plass.io/>_PLASS_ <http://plass.io/>, we will re-publish open
datasets, clean them and create collections, which will result in
DBpedia-style knowledge graphs for energy systems and supply-chain
management.
The *full document* is available at:
_https://databus.dbpedia.org/dbpedia/publication/strategy/2019.09.09/strategy_databus_initiative.pdf_
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