Tag Archives: datanetworks

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

Our 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.  

If you have any feedback or questions, please use the DBpedia Forum, the “report issues” button, or dbpedia@infai.org.

Yours,

DBpedia Association

SEMANTiCS Interview: Dan Weitzner

As the upcoming 14th DBpedia Community Meeting, co-located with SEMANTiCS 2019 in Karlsruhe, Sep 9-12, is drawing nearer, we like to take that opportunity to introduce you to our DBpedia keynote speakers.

Today’s post features an interview with Dan Weitzner from WPSemantix who talks about timbr-DBpedia, which we blogged about recently, as well as future trends and challenges of linked data and the semantic web.

Dan Weitzner is co-founder and Vice President of Research and Development of WPSemantix. He obtained his Bachelor of Science in Computer Science from Florida Atlantic University. In collaboration with DBpedia, he and his colleagues at WPSemantix launched timbr, the first SQL Semantic Knowledge Graph that integrates Wikipedia and Wikidata Knowledge into SQL engines.

Dan Weitzner

1. Can you tell us something about your research focus?

WPSemantix bridges the worlds of standard databases and the Semantic Web by creating ontologies accessible in standard SQL. 

Our platform – timbr is a virtual knowledge graph that maps existing data-sources to abstract concepts, accessible directly in all the popular Business Intelligence (BI) tools and also natively integrated into Apache Spark, R, Python, Java and Scala. 

timbr enables reasoning and inference for complex analytics without the need for costly Extract-Transform-Load (ETL) processes to graph databases.

2. How do you personally contribute to the advancement of semantic technologies?

We believe we have lowered the fundamental barriers to adoption of semantic technologies for large organizations who want to benefit from knowledge graph capabilities without firstly requiring fundamental changes in their database infrastructure and secondly, without requiring expensive organizational changes or significant personnel retraining.  

Additionally, we implemented the W3C Semantic Web principles to enable inference and inheritance between concepts in SQL, and to allow seamless integration of existing ontologies from OWL. Subsequently, users across organizations can do complex analytics using the same tools that they currently use to access and query their databases, and in addition, to facilitate the sophisticated query of big data without requiring highly technical expertise.  
timbr-DBpedia is one example of what can be achieved with our technology. This joint effort with the DBpedia Association allows semantic SQL query of the DBpedia knowledge graph, and the semantic integration of the DBpedia knowledge into data warehouses and data lakes. Finally, timbr-DBpedia allows organizations to benefit from enriching their data with DBpedia knowledge, combining it with machine learning and/or accessing it directly from their favourite BI tools.

3. Which trends and challenges do you see for linked data and the semantic web?

Currently, the use of semantic technologies for data exploration and data integration is a significant trend followed by data-driven communities. It allows companies to leverage the relationship-rich data to find meaningful insights into their data. 

One of the big difficulties for the average developer and business intelligence analyst is the challenge to learn semantic technologies. Another one is to create ontologies that are flexible and easily maintained. We aim to solve both challenges with timbr.

4. Which application areas for semantic technologies do you perceive as most promising?

I think semantic technologies will bloom in applications that require data integration and contextualization for machine learning models.

Ontology-based integration seems very promising by enabling accurate interpretation of data from multiple sources through the explicit definition of terms and relationships – particularly in big data systems,  where ontologies could bring consistency, expressivity and abstraction capabilities to the massive volumes of data.

5. As artificial intelligence becomes more and more important, what is your vision of AI?

I envision knowledge-based business intelligence and contextualized machine learning models. This will be the bedrock of cognitive computing as any analysis will be semantically enriched with human knowledge and statistical models.

This will bring analysts and data scientists to the next level of AI.

6. What are your expectations about Semantics 2019 in Karlsruhe?

I want to share our vision with the semantic community and I would also like to learn about the challenges, vision and expectations of companies and organizations dealing with semantic technologies. I will present “timbr-DBpedia – Exploration and Query of DBpedia in SQL”

The End

Visit SEMANTiCS 2019 in Karlsruhe, Sep 9-12 and find out more about timbr-DBpedia and all the other new developments at DBpedia. Get your tickets for our community meeting here. We are looking forward to meeting you during DBpedia Day.

Yours DBpedia Association