by Kathrin Dentler, Triply

Triply and TriplyDB

Triply is an Amsterdam-based company with the mission to (help you to) make linked data the new normal. Every day, we work towards making every step around working with linked data easier, such as converting and publishing it, integrating, querying, exploring and visualising it, and finally sharing and (re-)using it. We believe in the benefits of FAIR (findable, accessible, interoperable and reusable) data and open standards. Our product, TriplyDB, is a user-friendly, performant and stable platform, designed for potentially very large linked data knowledge graphs in practical and large-scale production-ready applications. TriplyDB not only allows you to store and manage your data, but also provides data stories, a great tool for storytelling. 

Data stories 

Data stories are data-driven stories, such as articles, business reports or scientific papers, that incorporate live, interactive visualizations of the underlying data. They are written in markdown, and can be underpinned by an orchestration of powerful visualizations of SPARQL query results. These visualizations can be charts, maps, galleries or timelines, and they always reflect the current state of your data. That data is just one click away: A query in a data story can be tested or even tweaked by its readers. It is possible to verify, reproduce and analyze the results and therefore the narrative, and to download the results or the entire dataset. This makes a data story truly FAIR, understandable, and trustworthy. We believe that a good data story can be worth more than a million words. 

Examples

With a data story, the domain expert is in control and empowered to work with, analyze, and share his or her data as well as interesting research results. There are some great examples that you can check out straight away:

• The fantastic data story on the Spanish Flu, which has been created by history and digital humanities researchers, who usually use R and share their results in scientific papers. 
• Students successfully published data stories in the scope of a course of only 10 weeks. 
• The beautiful data story on the Florentine Catasto of 1427.

DBpedia on triplydb.com

Triplydb.com is our public instance of TriplyDB, where we host many valuable datasets, which currently consist of nearly 100 billion triples. One of our most interesting and frequently used datasets are those by the DBpedia Association: 

• a version from 2017 with 369.205.380 statements 
• the DBpedia Snapshot 2021-06 Release with 845.807.279 statements, and 
• a version of the DBpedia ontology 

We also have several interesting saved queries based on these datasets. 

A data story about DBpedia

To showcase the value of DBpedia and data stories to our users, we published a data story about DBpedia. This data story includes comprehensible and interactive visualizations, such as a timeline and a tree hierarchy, all of which are powered by live SPARQL queries against the DBpedia dataset. 

Let us have a look at the car timeline: DBpedia contains a large amount of content regarding car manufacturers and their products. Based on that data, we constructed a timeline which shows the evolution within the car industry. 

If you navigate from the data story to the query, you can analyze it and try it yourself. You see that the query limits the number of manufacturers so that we are able to look at the full scale of the automotive revolution without cluttering the timeline. You can play around with the query, change the ordering, visualize less or more manufacturers, or change the output format altogether. 

Advanced features

If you wish to use a certain query programmatically, we offer preconfigured code snippets that allow you to run a query from a python or an R script. You can also configure REST APIs in case you want to work with variables. And last but not least, it is possible to embed a data story on any website. Just scroll to the end of the story you want to embed and click the “</> Embed” button for a copy-pasteable code snippet. 

Try it yourself! 

Sounds interesting? We still have a limited number of free user accounts over at triplydb.com. You can conveniently log in with your Google or Github account and start uploading your data. We host your first million open data triples for free! Of course, you can also use public datasets, such as the ones from DBpedia, link your data, work together on queries, save them, and then one day create your own data story to let your data speak for you. We are already looking forward to what your data has to say!

A big thank you to Triply for being a DBpedia member since 2020. Especially Kathrin Dentler for presenting her work at the last DBpedia Day in Amsterdam and for her amazing contribution to DBpedia.

Yours,

DBpedia Association

The post Bringing Linked Data to the Domain Expert with TriplyDB Data Stories appeared first on DBpedia Association.

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

The post SEMANTiCS Interview: Dan Weitzner appeared first on DBpedia Association.