Tag Archives: results

DBpedia Chapters – Survey Evaluation – Episode One

DBpedia Chapters – Challenge Accepted

The DBpedia community currently comprises more than 20 language chapters, ranging from  Basque, Japanese to Portuguese and Ukrainian. Managing such a variety of chapters is a huge challenge for the DBpedia Association because individual requirements are as diverse as the different languages the chapters represent. There are chapters that started out back in 2012 such as DBpediaNL. Others like the Catalan chapter are brand new and have different haves and needs.

So, in order to optimize chapter development, we aim to formalize an official DBpedia Chapter Consortium. It permits a close dialogue with the chapters in order to address all relevant matters regarding communication, organization as well as technical issues. We want to provide the community with the best basis to set up new chapters and to maintain or develop the existing ones.

Our main targets for this are to: 

  • improve general chapter organization,
  • unite all DBpedia chapters with central DBpedia,
  • promote better communication and understanding and,
  • create synergies for further developments and make easier the access to information about which is done by all DBpedia bodies

As a first step, we needed to collect information about the current state of things.  Hence, we conducted two surveys to collect the necessary information. One was directed at chapter leads and the other one at technical heads. 

In this blog-post, we like to present you the results of the survey conducted with chapter leads.  It addressed matters of communication and organizational relevance. Unfortunately, only nine out of 21 chapters participated, so the respective outcome of the survey speaks only for roughly 42% of all DBpedia chapters.

Chapter-Survey  – Episode One

Most chapters have very little personnel committed to the work done for the chapter, due to different reasons. 66 % of the chapters have only one till four people being involved in the core work. Only one chapter has about ten people working on it.

Overall, the chapters use various marketing channels for promotion, visibility and outreach. The website as well as event participation, Twitter and Facebook are among the most favourite channels they use. 

The following chart shows how chapters currently communicate organizational and communication issues in their respective chapter and to the DBpedia Association.

 

 

The second one explicit that ⅓ of the chapters favour an exchange among chapters and with the DBpedia Association via the discussion mailing list as well as regular chapter calls.

 

The survey results show that 66,6% of the chapters currently do not consider their current mode of communication efficient enough. They think that their communication with the DBpedia Association should improve.

 

As pointed out before, most chapters only have little personnel resources. It is no wonder that most of them need help to improve the work and impact of chapter results. The following chart shows the kind of support chapters require to improve their overall work, organization and communication. Most noteworthy, technical, marketing and organization support are hereby the top three aspects the chapters need help with. 

 

 

The good news is all of the chapters maintain a DBpedia Website. However, the frequency of updates varies among them. See the chart on the right.

 

 

 

Earlier this year, we announced that we like to align all chapter websites with the main DBpedia website. That includes a common structure and a corporate design, similar to the main one.  Above all, this is important for the overall image and recognition factor of DBpedia in the tech community. With respect to that, we inquired whether chapters would like to participate in an alignment of the websites or not.

 

 

 

With respect to marketing support, the chapters require from the Association, more than 50% of the chapters like to be frequently promoted via the main DBpedia twitter channel.

 

 

Good news: just forward us your news or tag us with @dbpedia and we will share ’em.

Almost there.

Finally, we asked about chapters requirements to improve their work and, the impact of their chapters’ results. 

 

Bottom line

All in all, we are very grateful for your contribution. Those data will help us to develop a strategy to work towards the targets mentioned above. We will now use this data to conceptualize a little program to assist chapters in their organization and marketing endeavours. Furthermore, the information given will also help us to tackle the different issues that arose, implement the necessary support and improve chapter development and chapter visibility.

In episode two, we will delve into the results of the technical survey. Sit tight and follow us on Twitter, Facebook, LinkedIn or subscribe to our newsletter.

Finally, one last remark. If you want to promote news of your chapter or otherwise like to increase its visibility, you are always welcome to:

  • forward us the respective information to be promoted via our marketing channels 
  • use your own Twitter channel and tag your post with @dbpedia,  so we can retweet your news. 
  • always use #dbpediachapters

Looking forward to your news.

Yours

DBpedia Association

GSoC 2017 – Recap and Results

We are very pleased to announce that all of this year’s Google Summer of Code students made it successful through the program and passed their projects. All codes have been submitted, merged and are ready to be examined by the rest of the world.

Marco Fossati, Dimitris Kontokostas, Tommaso Soru, Domenico Potena, Emanuele Storti , anastasia Dimiou, Wouter Maroy, Peng Xu, Sandro Coelho and Ricardo Usbeck, members of the DBpedia Community, did a great job in mentoring 7 students from around the world. All of the students enjoyed the experiences made during the program and will hopefully continue to contribute to DBpedia in the future.

“GSoC is the perfect opportunity to learn from experts, get to know new communities, design principles and work flows.” (Ram G Athreya)”

Now, we would like to take that opportunity to  give you a little recap of the projects mentored by DBpedia members during the past months. Just click below for more details .

 

DBpedia Mappings Front-End Administration by Ismael Rodriguez

The goal of the project was to create a front-end application that provides a user-friendly interface so the DBPedia community can easily view, create and administrate DBpedia mapping rules using RML. The developed system includes user administration features, help posts, Github mappings synchronization, and rich RML related features such as syntax highlighting, RML code generation from templates, RML validation, extraction and statistics. Part of these features are possible thanks to the interaction with the DBPedia Extraction Framework. In the end, all the functionalities and goals that were required have been developed, with many functional tests and the approval of the DBpedia community. The system is ready for production deployment. For further information, please visit the project blog.  Mentors: Anastasia Dimou and Wouter Maroy (Ghent University), Dimitris Kontokostas (GeoPhy HQ).

Chatbot for DBpedia by Ram G Athreya

DBpedia Chatbot is a conversational chatbot for DBpedia which is accessible through the following platforms: a Web Interface, Slack and Facebook Messenger.

The bot is capable of responding to users in the form of simple short text messages or through more elaborate interactive messages. Users can communicate or respond to the bot through text and also through interactions (such as clicking on buttons/links). The bot tries to answer text based questions of the following types: natural language questions, location information, service checks, language chapters, templates and banter. For more information, please follow the link to the project site. Mentor: Ricardo Usbeck (AKSW).

Knowledge Base Embeddings for DBpedia by Nausheen Fatma

Knowledge base embeddings has been an active area of research. In recent years a lot of research work such as TransE, TransR, RESCAL, SSP, etc. has been done to get knowledge base embeddings. However none of these approaches have used DBpedia to validate their approach. In this project, I want to achieve the following tasks: i) Run the existing techniques for KB embeddings for standard datasets. ii) Create an equivalent standard dataset from DBpedia for evaluations. iii) Evaluate across domains. iv) Compare and Analyse the performance and consistency of various approaches for DBpedia dataset along with other standard datasets. v) Report any challenges that may come across implementing the approaches for DBpedia. For more information, please follow the links to her project blog and GitHub-repository. Mentors: Tommaso Soru (AKSW) and  Sandro Coelho (KILT).

Knowledge Base Embeddings for DBpedia by Akshay Jagatap

The project defined embeddings to represent classes, instances and properties by implementing Random Vector Accumulators with additional features in order to better encode the semantic information held by the Wikipedia corpus and DBpedia graphs. To test the quality of embeddings generated by the RVA, lexical memory vectors of locations were generated and tested on a modified subset of the Google Analogies Test Set. Check out further information via Akshay’s GitHub-repo. Mentors: Tommaso Soru (AKSW) and Xu Peng (University of Alberta).

The Table Extractor by Luca Vergili

Wikipedia is full of data hidden in tables. The aim of this project was to explore the possibilities of exploiting all the data represented with the appearance of tables in Wiki pages, in order to populate the different chapters of DBpedia through new data of interest. The Table Extractor has to be the engine of this data “revolution”: it would achieve the final purpose of extracting the semi structured data from all those tables now scattered in most of the Wiki pages. In this page you can observe dataset (english and italian) extracted using table extractor . Furthermore you can read log file created in order to see all operations made up for creating RDF triples. I recommend to also see this page, that contains the idea behind the project and an example of result extracted from log files and .ttl dataset. For more details see Luca’s Git-Hub repository. Mentors: Domenico Potena and Emanuele Storti (Università Politecnica delle Marche).

 

Unsupervised Learning of DBpedia Taxonomy by Shashank Motepalli

Wikipedia represents a comprehensive cross-domain source of knowledge with millions of contributors. The DBpedia project tries to extract structured information from Wikipedia and transform it into RDF.

The main classification system of DBpedia depends on human curation, which causes it to lack coverage, resulting in a large amount of untyped resources. DBTax provides an unsupervised approach that automatically learns a taxonomy from the Wikipedia category system and extensively assigns types to DBpedia entities, through the combination of several NLP and interdisciplinary techniques. It provides a robust backbone for DBpedia knowledge and has the benefit of being easy to understand for end users. details about his work and his code can e found on the projects site. Mentors: Marco Fossati (Università degli Studi di Trento) and Dimitris Kontokostas (GeoPhy HQ). 

The  Wikipedia List-Extractor by Krishanu Konar

This project aimed to augment upon the already existing list-extractor project by Federica in GSoC 2016. The project focused on the extraction of relevant but hidden data which lies inside lists in Wikipedia pages. Wikipedia, being the world’s largest encyclopedia, has humongous amount of information present in form of text. While key facts and figures are encapsulated in the resource’s infobox, and some detailed statistics are present in the form of tables, but there’s also a lot of data present in form of lists which are quite unstructured and hence its difficult to form into a semantic relationship. The main objective of the project was to create a tool that can extract information from Wikipedia lists and form appropriate RDF triplets that can be inserted in the DBpedia dataset. Fore details on the code and about the project check Krishanu’s blog and GitHub-repository. Mentors: Marco Fossati (Università degli Studi di Trento), Domenico Potena and Emanuele Storti (Università Politecnica delle Marche). 

Read more

We are regularly growing our community through GSoC and can deliver more and more opportunities to you. Ideas and applications for the next edition of GSoC are very much welcome. Just contact us via email or check our website for details.

Again, DBpedia is planning to be a vital part of the GSoC Mentor Summit, from October 13th -15th, at the Google Campus in Sunnyvale California. This summit is a way to say thank you to the mentors for the great job they did during the program. Moreover it is a platform to discuss what can be done to improve GSoC and how to keep students involved in their communities post-GSoC.

And there is more good news to tell.  DBpedia wants to meet up with the US community during the 11th DBpedia Community Meeting in California.  We are currently working on the program and keep you posted as soon as registration is open.

So, stay tuned and check  Twitter, Facebook and the Website or subscribe to our Newsletter for latest news and updates.

See you soon!

Yours,

DBpedia Association