Following, we will give you a brief retrospective about the presentations.

Opening Session

Katja Hose – “Querying the web of data”

….on the search for the killer App.

The concept of Linked Open Data and the promise of the Web of Data have been around for over a decade now. Yet, the great potential of free access to a broad range of data that these technologies offer has not yet been fully exploited. This talk will, therefore review the current state of the art, highlight the main challenges from a query processing perspective, and sketch potential ways on how to solve them. Slides are available here.

Dan Weitzner – “timbr-DBpedia – Exploration and Query of DBpedia in SQL”

The timbr SQL Semantic Knowledge Platform enables the creation of virtual knowledge graphs in SQL. The DBpedia version of timbr supports query of DBpedia in SQL and seamless integration of DBpedia data into data warehouses and data lakes. We already published a detailed blogpost about timbr where you can find all relevant information about this amazing new DBpedia Service.

Showcase Session

Maribel Acosta – “A closer look at the changing dynamics of DBpedia mappings”

Her presentation looked at the mappings wiki and how different language chapters use and edit it. Slides are available here.

Mariano Rico – “Polishing a diamond: techniques and results to enhance the quality of DBpedia data”

DBpedia is more than a source for creating papers. It is also being used by companies as a remarkable data source. This talk is focused on how we can detect errors and how to improve the data, from the perspective of academic researchers and but also on private companies. We show the case for the Spanish DBpedia (the second DBpedia in size after the English chapter) through a set of techniques, paying attention to results and further work. Slides are available here.

Guillermo Vega-Gorgojo – “Clover Quiz: exploiting DBpedia to create a mobile trivia game”

Clover Quiz is a turn-based multiplayer trivia game for Android devices with more than 200K multiple choice questions (in English and Spanish) about different domains generated out of DBpedia. Questions are created off-line through a data extraction pipeline and a versatile template-based mechanism. A back-end server manages the question set and the associated images, while a mobile app has been developed and released in Google Play. The game is available free of charge and has been downloaded by +10K users, answering more than 1M questions. Therefore, Clover Quiz demonstrates the advantages of semantic technologies for collecting data and automating the generation of multiple-choice questions in a scalable way. Slides are available here.

Fabian Hoppe and Tabea Tiez – “The Return of German DBpedia”

Fabian and Tabea will present the latest news on the German DBpedia chapter as it returns to the language chapter family after an extended offline period. They will talk about the data set, discuss a few challenges along the way and give insights into future perspectives of the German chapter. Slides are available here.

Wlodzimierz Lewoniewski and Krzysztof Węcel  – “References extraction from Wikipedia infoboxes”

In Wikipedia’s infoboxes, some facts have references, which can be useful for checking the reliability of the provided data. We present challenges and methods connected with the metadata extraction of Wikipedia’s sources. We used DBpedia Extraction Framework along with own extensions in Python to provide statistics about citations in 10 language versions. Provided methods can be used to verify and synchronize facts depending on the quality assessment of sources. Slides are available here.

Wlodzimierz Lewoniewski – “References extraction from Wikipedia infoboxes” … He gave insight into the process of extracting references for Wikipedia infoboxes, which we will use in our GFS project.

Afternoon Session

Sebastian Hellmann, Johannes Frey, Marvin Hofer – “The DBpedia Databus – How to build a DBpedia for each of your Use Cases”

The DBpedia Databus is a platform that is intended for data consumers. It will enable users to build an automated DBpedia-style Knowledge Graph for any data they need. The big benefit is that users not only have access to data, but are also encouraged to apply improvements and, therefore, will enhance the data source and benefit other consumers. We want to use this session to officially introduce the Databus, which is currently in beta and demonstrate its power as a central platform that captures decentrally created client-side value by consumers.  

We will give insight on how the new monthly DBpedia releases are built and validated to copy and adapt for your use cases. Slides are available here.

Interactive session, moderator: Sebastian Hellmann – “DBpedia Connect & DBpedia Commerce – Discussing the new Strategy of DBpedia”

In order to keep growing and improving, DBpedia has been undergoing a growth hack for the last couple of months. As part of this process, we developed two new subdivisions of DBpedia: DBpedia Connect and DBpedia Commerce. The former is a low-code platform to interconnect your public or private databus data with the unified, global DBpedia graph and export the interconnected and enriched knowledge graph into your infrastructure. DBpedia Commerce is an access and payment platform to transform Linked Data into a networked data economy. It will allow DBpedia to offer any data, mod, application or service on the market. During this session, we will provide more insight into these as well as an overview of how DBpedia users can best utilize them. Slides are available here.

In case you missed the event, all slides and presentations are also available on our Website. Further insights, feedback and photos about the event are available on Twitter via #DBpediaDay

We are now looking forward to more DBpedia meetings next year. So, stay tuned and check Twitter, Facebook and the Website or subscribe to our Newsletter for the latest news and information.

If you want to organize a DBpedia Community meeting yourself, just get in touch with us via dbpedia@infai.org regarding program and organization.

Yours

DBpedia Association

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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.

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.

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.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.

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.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.

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

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In part three of DBpedia’s growth hack blog series, we feature timbr, the latest development at DBpedia in collaboration with WPSemantix. Read on to find out how it works.

timbr – DBpedia SQL Semantic Knowledge Platform

Tel Aviv, Israel and Leipzig, Germany – July 18, 2019 – WP-Semantix (WPS) – the “SQL Knowledge Graph Company” and DBpedia Association – Institut für Angewandte Informatik e.V., announced today the launch of the timbr-DBpedia SQL Semantic Knowledge Platform, a unique version of WPS’ timbr SQL Semantic Knowledge Graph that integrates timbr-DBpedia ontology, timbr’s ontology explorer/visualizer and timbr’s SQL query service, to provide for the first time semantic access to DBpedia knowledge in SQL and to thus facilitate DBpedia knowledge integration into standard data warehouses and data lakes.

DBpedia

DBpedia is the crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects and publish these as files on the Databus and via online databases. This structured information resembles an open knowledge graph which has been available for everyone on the Web for over a decade. Knowledge graphs are a new kind of databases developed to store knowledge in a machine-readable form, organized as connected, relationship-rich data. After the publication of DBpedia (in parallel to Freebase) 12 years ago, knowledge graphs have become very successful and Google uses a similar approach to create the knowledge cards displayed in search results.

Query the world’s knowledge in standard SQL

Amit Weitzner, founder and CEO at WPS commented: “Knowledge graphs use specialized languages, require resource-intensive, dedicated infrastructure and require costly ETL operations. That is, they did until timbr came along. timbr employs SQL – the most widely known database language, to eliminate the technological barriers to entry for using knowledge graphs and to implement Semantic Web principles to provide knowledge graph functionality in SQL. timbr enables modelling of data as connected, context-enriched concepts with inference and graph traversal capabilities while being queryable in standard SQL, to represent knowledge in data warehouses and data lakes. timbr-DBpedia is our first vertical application and we are very excited by the prospects of our cooperation with the DBpedia team to enable the largest user base to query the world’s knowledge in standard SQL.”

Sebastian Hellmann, executive director of the DBpedia Association commented that:

timbr will help to explore the power of semantic technologies

Prof. James Hendler, pioneer and a world-leading authority in Semantic Web technologies and WPS’ advisory board member commented “timbr can be a game-changing solution by enabling the semantic inference capabilities needed in many modelling applications to be done in SQL. This approach will enable many users to get the advantages of semantic AI technologies and data integration without the learning curve of many current systems. By giving more people access to the semantic version of Wikipedia, timbr-DBpedia will definitely contribute to allowing the majority of the market to explore the power of semantic technologies.”

timbr-DBpedia is available as a query service or licensed for use as SaaS or on-premises. See the DBpedia website: wiki.dbpedia.org/timbr.

About WPSemantix

WP-Semantix Ltd. (wpsemantix.com) is the developer of the timbr SQL semantic knowledge platform, a dynamic abstraction layer over relational and non-relational data, facilitating declaration and powerful exploration of semantically rich ontologies using a standard SQL query interface. timbr is natively accessible in Apache Spark, Python, R and SQL to empower data scientists to perform complex analytics and generate sophisticated ML algorithms.  Its JDBC interface provides seamless integration with the most popular business intelligence solutions to make complex analytics accessible to analysts and domain experts across the organization.

WP-Semantix, timbr, “SQL Knowledge Graph”, “SQL Semantic Knowledge Graph” and associated marks and trademarks are registered trademarks of WP Semantix Ltd.

DBpedia is looking forward to this cooperation. Follow us on Twitter for the latest information and stay tuned for part four of our growth hack series. The next post features the GlobalFactSyncRe. Curious? You have to be a little more patient and wait till Thursday, July 25th.

Yours DBpedia Association

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With part  II of our growth hack series, we would like to introduce you to the latest feature of our development – the new DBpedia Forum.

Why a new forum?

DBpedia has an inclusionist model and DBpedia is huge. At the core, there is data extracted from Wikipedia and Wikidata. Around this, there are derived datasets like the fusion/enrichment and also LHD. Additionally, we offer services such as DBpedia Spotlight, DBpedia Lookup, SameAs, and not to forget the main endpoint http://dbpedia.org/sparql as well as our DBpedia Chapters. All of this is surrounded by 25k academic papers and a vivid business network.

Since we have this inclusionist model, we believe that access to data and knowledge should be global and unified (and free where possible). That is exactly why we established the DBpedia Forum –  to further this mission. 

Welcome!

The DBpedia Forum is a shared community resource — a place to share skills, knowledge, and interests through an ongoing conversation about DBpedia and related topics. It is meant (among others) to replace our old support page for assistance with DBpedia. In the long run, we will shut down our (former) support page, as it is not serving our growing needs anymore. 

Where are all the DBpedians?

We figured, most of you are already actively involved in exchange about DBpedia. However, the majority of that is scattered all over the web which makes it hard for us and others to keep track of. With the new forum, we offer you a playground for vivid exchange, and to meet and greet fellow DBpedians – a platform for everyone’s benefit. 

The DBpedia Forum simplifies communication

Make this a great place for discussion by contributing yourself. It is super easy. Just visit https://forum.dbpedia.org/, browse the topics, and find the info that helps you or add your own. If you want to contribute just register and off you go. Improve the discussion by discovering ones that are already happening. Help us influence the future of the DBpedia community by engaging in discussions that make this forum an interesting place to be. 

Transparency is all

To assist with maintaining an appropriate code of conduct the forum utilizes little discourse tools that enable the community to collectively identify the best (and worst) contributions. The forum tracks bookmarks, likes, flags, replies, edits, and many more. That is similar to the ranking in the old support system but much more transparent and much more fun.

For the hunter-gatherers among you, you can also earn batches for various activities  – as long as you are active.  And if you feel very passionate about a certain topic, we would gladly make you a moderator – just let us know.  

Now is the time

Since you are already talking about DBpedia somewhere on the WWW, why not do it here and now for everyone else to follow? Your knowledge and skills are key, not only for individuals in this forum but also for the whole DBpedia community. 

Happy posting and stay tuned for part III in the growth hack series. The next post will feature timbr – DBpedia SQL Semantic Knowledge Platform.

Yours,

DBpedia Association

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