Therefore we have developed an internship and mentoring program to:

1. qualify a motivated student and beginner level developer. He/she learns about DBpedia, your organization, and data. Collaborative coaching may qualify intern for hiring.
2. move your organization’s data space closer to DBpedia. Internship goals are defined by DBpedia mentors and your organization and focus on concrete needs of your business.
3. gain insight into your needs helping us to shape our strategy for the future.

Springer Nature was the first partner we collaborated with, in of our new program.  We set out on an endeavor to interlink Springer Nature’s SciGraph and DBpedia datasets. 

With Beyza Yaman, who managed to prevail against 7 other international competitors,  we found the perfect partner in crime to tackle this challenge. Read her interview below and find out more about the internship.

Who are you?

My name is Beyza Yaman and I am a Ph.D. student in the Department of Computer Science and Engineering (DIBRIS) at University of Genoa (Italy). I am working on the problem of source selection on Linked Open Data for live queries proposing a context and quality dependent solution. Beside my studies, I like to meet new people, learn their cultures and discover new places, especially by walking/hiking events.

Why DBpedia? What is your main interest in DBpedia and what was your motivation to apply for our collaborative internship?

I have already been using DBpedia datasets for my experiments. Besides from being the core of the Linked Data Cloud, DBpedia is one of the platforms which brings the applied semantic technologies forward and ahead of most other data technologies. Also, collaboration with Springer Nature which is one of the best publishing companies was the cherry on the cake! Springer is an innovative company which applies the latest technologies to their requirements. Thus, being involved in a project with different grounds seemed to be a fruitful experience.  When I saw the announcement of the internship, I thought this is a great opportunity not to be missed!

What have you been responsible for?

My tasks during the internship had been

• Link discovery between SciGraph and DBpedia datasets
• Text mining of DBpedia entities from Springer Nature publication content
• Create useful linkset-files for the resulting links

As the web of data is growing into the interlinked data space, data sources should be connected to discover further insight from the data by creating meaningful relations. Moreover, further information (e.g. quality) about these link sets forms another aspect of the Semantic Web objectives. Thus, we worked on interlinking SciGraph and DBpedia datasets by using the  Link Discovery approach for the structured content and the Named Entity Recognition approach for unstructured text. We were able to integrate SciGraph data with DBpedia resources which improves the identity resolution in the existing resources and to enrich the SciGraph data with additional relations by annotating SciGraph content with DBpedia links which increases the discoverability of the data. One of the challenges we faced was having a huge amount of data and, actually, we have produced even more for the Linked Data users. You can follow our work, use the data and give us feedback from this repository (https://github.com/dbpedia/sci-graph-links).

What did you learn from the project?

It has been a fantastic experience which helped me to expand my theoretical knowledge with a lot of practical aspects. I worked with Markus Freudenberg from DBpedia and Tony Hammond, Michele Pasin and Evangelos Theodoridis from Springer Nature. Working with technically well-equipped researchers and professionals on the subject has been very influential for my research. Especially, working with a team of academics and professionals in collaboration has taught me two different views of looking at the project. I learned more about SciGraph data and DBpedia, as well as, many ways of dealing with huge amount of data, tools used in DBpedia and Linked Data environment, the importance of open source data/codes. Besides the project, I had a chance to witness development phases of DBpedia in the Knowledge Integration and Linked Data Technologies (KILT) group (Leipzig) with a bunch of cool guys and girls who made my stay more enjoyable. I also met a lot of researchers with Semantic Web experience which has extended my point of view widely.

What are your next plans? How do you want to contribute to DBpedia in the future?

I would like to finish my Ph.D. and extend my knowledge by involving new exciting projects like this one. Publishing what we have done and further quality improvements might be a nice follow up for the work and Linked Data community. Besides, I would like to contribute to the development of the Turkish DBpedia Chapter which is unfortunately missing. Thus, in this way, we can promote the usage and development of DBpedia and Linked Data to the Turkish research community and companies as well.

There will also be a report on the collaboration between Springer Nature and DBpedia that will cover the technical details of linking DBpedia and SciGraph datasets. We will keep you informed about news via Twitter and our Website.

We are really happy to have worked with her and we are now looking forward to a Turkish DBpedia Chapter.  If you are a DBpedia enthusiast and want to help to start the Turkish DBpedia chapter, just get in touch with Beyza or contact us.

Did her story inspire you? Do you want to become an intern at DBpedia? Check our Website, Twitter, and Social Media and don’t miss any internship updates.

Last but not least, we like to thank Springer Nature for their cooperation and commitment to the project.

In case you like to collaborate with us in order to find a developer that helps to integrate DBpedia into your business get in touch with us via dbpedia@infai.org.

Yours,

DBpedia Association

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