One Billion derived Knowledge Graphs
… by and for Consumers until 2025
One Billion – what a mission! We are proud to announce that the DBpedia Databus website at https://databus.dbpedia.org and the SPARQL API at https://databus.dbpedia.org/(repo/sparql|yasgui) (docu) 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.
But, let’s start from the beginning
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 work power 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 this set-up, data can flow back to previous components, the original sources, or end up being consumed by external components.
One Billion interconnected, quality-controlled Knowledge Graphs until 2025
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.
To a data consumer network
As we are inverting 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 alone has 600k file downloads per year complemented by downloads at over 20 chapters, e.g. 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 DBkWik, LinkedHypernyms are being loaded onto the bus and consolidated. We expect this number to reach over 100 by the end of the year. Companies and organisations who have previously uploaded their backlinks here will be able to migrate to the databus. Other datasets are cleaned and posted. In two of our research projects LOD-GEOSS and PLASS, 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.
A new era for decentralized collaboration on data quality
DBpedia was established around producing a queryable knowledge graph derived from Wikipedia content that’s able to answer questions like “What have Innsbruck and Leipzig in common?” A community and consumer network quickly formed around this highly useful data, resulting in a large, well-structured, open knowledge graph that seeded the Linked Open Data Cloud — which is the largest knowledge graph on earth. The main lesson learned after these 13 years is that current data “copy” or “download” processes are inefficient by a magnitude that can only be grasped from a global perspective. Consumers spend tremendous effort fixing errors on the client-side. If one unparseable line needs 15 minutes to find and fix, we are talking about 104 days of work for 10,000 downloads. Providers – on the other hand – will never have the resources to fix the last error as cost increases exponentially (20/80 rule).
One billion knowledge graphs in mind – the progress so far
Discarding faulty data often means that a substitute source has to be found, which is hours of research and might lead to similar problems. From the dozens of DBpedia Community meetings we held we can summarize that for each clean-up procedure, data transformation, linkset or schema mapping that a consumer creates client-side, dozens of consumers have invested the same effort client-side before him and none of it reaches the source or other consumers with the same problem. Holding the community meetings just showed us the tip of the iceberg.
As a foundation, we implemented a mappings wiki that allowed consumers to improve data quality centrally. A next advancement was the creation of the SHACL standard by our former CTO and board member Dimitris Kontokostas. SHACL allows consumers to specify repeatable tests on graph structures and datatypes, which is an effective way to systematically assess data quality. We established the DBpedia Databus as a central platform to better capture decentrally created, client-side value by consumers.
It is an open system, therefore value that is captured flows right back to everybody.
The full document “DBpedia’s Databus and strategic initiative to facilitate “One Billion derived Knowledge Graphs by and for Consumers” until 2025 is available here.