In the upcoming Blog-Series, we like to take you on a retrospective tour through 2020, giving you insights into a year with DBpedia. We will highlight our past events and the development around the DBpedia dataset. 

A year with DBpedia and the DBpedia dataset – Retrospective Part 1

DBpedia Workshop colocated with LDAC2020

On June 19, 2020 we organized a DBpedia workshop co-located with the LDAC workshop series to exchange knowledge regarding new technologies and innovations in the fields of Linked Data and Semantic Web. Dimitris Kontokostas (diffbot, US) opened the meeting with his delightful keynote presentation ‘{RDF} Data quality assessment – connecting the pieces’. His presentation focused on defining data quality and identification of data quality issues. Following Dimitri’s keynote many community based presentations were held, enabling an exciting workshop day. 

Most Influential Scholars

DBpedia has become a high-impact, high-visibility project because of our foundation in excellent Knowledge Engineering as the pivot point between scientific methods, innovation and industrial-grade output. The drivers behind DBpedia are 6 out of the TOP 10 Most Influential Scholars in Knowledge Engineering and the C-level executives of our members. Check all details here: https://www.aminer.cn/ai2000/country/Germany 

DBpedia (dataset) and Google Summer of Code 2020

For the 9th year in a row, we were part of this incredible journey of young ambitious developers who joined us as an open source organization to work on a GSoC coding project all summer. With 45 project proposals, this GSoC edition marked a new record for DBpedia. Even though Covid-19 changed a lot in the world, it couldn’t shake GSoC. If you want to have deeper insights in our GSoC student’s work you can find their blogs and repos here: https://blog.dbpedia.org/2020/10/12/gsoc2020-recap/. 

DBpedia Tutorial Series 2020

During this year we organized three amazing tutorials in which more than 120 DBpedians took part. Over the last year, the DBpedia core team has consolidated a great amount of technology around DBpedia. These tutorials are target to developers (in particular of DBpedia Chapters) that wish to learn how to replicate local infrastructure such as loading and hosting an own SPARQL endpoint. A core focus was the new DBpedia Stack, which contains several dockerized applications that are automatically loading data from the DBpedia Databus. We will continue organizing more tutorials in 2021. Looking forward to meeting you online! In case you miss the DBpedia Tutorial series 2020, watch all videos here. 

In our upcoming Blog-Post after the holidays we will give you more insights in past events and technical achievements. We are now looking forward to the year 2021. The DBpedia team plans to have meetings at the Knowledge Graph Conference, the LDK conference in Zaragoza, Spain and the SEMANTiCS conference in Amsterdam, Netherlands. We wish you a merry Christmas and a happy New Year. In the meantime, stay tuned and visit our Twitter channel or subscribe to our DBpedia Newsletter.   

Yours DBpedia Association

The post 2020 – Oh What a Challenging Year appeared first on DBpedia Association.

Hi DBpedians,

During the DBpedia Day in Leipzig, I gave a talk about how to use the facts contained in the DBpedia Knowledge Graph for generating coherent sentences and texts.

We essentially rely on Natural Language Generation (NLG) techniques for accomplishing this task. NLG is the process of generating coherent natural language text from non-linguistic data (Reiter and Dale, 2000). Despite community agreement on the actual text and speech output of these systems, there is far less consensus on what the input should be (Gatt and Krahmer, 2017). A large number of inputs have been taken for NLG systems, including images (Xu et al., 2015), numeric data (Gkatzia et al., 2014), semantic representations (Theune et al., 2001).

Why not generate text from Knowledge graphs? 

The generation of natural language from the Semantic Web has been already introduced some years ago (Ngonga Ngomo et al., 2013; Bouayad-Agha et al., 2014; Staykova, 2014). However, it has gained recently substantial attention and some challenges have been proposed to investigate the quality of automatically generated texts from RDF (Colin et al., 2016). Moreover, RDF has demonstrated a promising ability to support the creation of NLG benchmarks (Gardent et al., 2017). Still, English is the only language which has been widely targeted. Thus, we proposed RDF2NL which can generate texts in other languages than English by relying on different language versions of SimpleNLG.

What is RDF2NL?

While the exciting avenue of using deep learning techniques in NLG approaches (Gatt and Krahmer, 2017) is open to this task and deep learning has already shown promising results for RDF data (Sleimi and Gardent, 2016), the morphological richness of some languages led us to develop a rule-based approach. This was to ensure that we could identify the challenges imposed by each language from the SW perspective before applying Machine Learning (ML) algorithms. RDF2NL is able to generate either a single sentence or a summary of a given resource. RDF2NL is based on Ngonga Ngomo et.al LD2NL and it also uses the Brazilian, Spanish, French, German and Italian adaptations of SimpleNLG to the realization task.

An example of RDF2NL application:

We envisioned a promising application by using RDF2PT which aims to support the automatic creation of benchmarking datasets to Named Entity Recognition (NER) and Entity Linking (EL) tasks. In Brazilian Portuguese, there is a lack of gold standards datasets for these tasks, which makes the investigation of these problems difficult for the scientific community. Our aim was to create Brazilian Portuguese silver standard datasets which are able to be uploaded into GERBIL for easy evaluation. To this end, we implemented RDF2PT ( Portuguese version of RDF2NL) in BENGAL , which is an approach for automatically generating NER benchmarks based on RDF triples and Knowledge Graphs. This application has already resulted in promising datasets which we have used to investigate the capability of multilingual entity linking systems for recognizing and disambiguating entities in Brazilian Portuguese texts. Some results you can find below:
NER – http://gerbil.aksw.org/gerbil/experiment?id=201801050043
NED – http://gerbil.aksw.org/gerbil/experiment?id=201801110012

More application scenarios

• Summarize or Explain KBs to non-experts
• Create news automatically (automated journalism)
• Summarize medical records
• Generate technical manuals
• Support the training of other NLP tasks
• Generate product descriptions (Ebay)

Deep Learning into RDF2NL

After devising our rule-based approach, we realized that RD2NL is really good by selecting adequate content from the RDF triples, but the fluency of its generated texts remains a challenge. Therefore, we decided to move forward and work with neural network models to improve the fluency of texts as they have already shown promising results in the generation of translations. Thus, we focused on the generation of referring expressions, which is an essential part while generating texts, it basically decides how the NLG model will present the information about a given entity. For example, the referring expressions of the entity Barack Obama can be “the former president of USA”, “Obama”, “Barack”, “He” and so on. Afterward, we have been working on combining different NLG sub-tasks into single neural models for improving the fluency of our texts.

GSoC on it – Stay tuned!  

Apart from trying to improve the fluency of our models, we relied previously on different language versions of SimpleNLG to the realization task. Nowadays, we have been investigating the generation of multiple languages by using a unique neural model. Our student has been working hard to provide nice results and we are basically at the end of our GSoC project. So stay tuned to know the outcome of this exciting project.

Many thanks to Diego for his contribution. If you want to write a guest post, share your results on the DBpedia Blog, and thus give your work more visibility and outreach, just ping us via dbpedia@infai.org.

Yours

DBpedia Association

The post RDF2NL: Generating Texts from RDF Data appeared first on DBpedia Association.

First and foremost, we would like to thank Jirka Kosek (University of Economics, Prague), Milan Dojchinovski (AKSW/KILT, Czech Technical University in Prague), Tomáš Kliegr (KIZI/University of Economics, Prague) and, the XML Prague conference for co-hosting and support the event.

Opening the DBpedia community meetup

The Czech DBpedia community and the DBpedia Databus were in the focus of this meetup. Therefore, we invited local data scientists as well as DBpedia enthusiasts to discuss the state-of-the-art of the DBpedia databus. Sebastian Hellmann (AKSW/KILT) opened the meeting with an introduction to DBpedia and the DBpedia Databus. Following, Marvin Hofer explained how to use the DBpedia databus in combination with the Docker technology and, Johannes Frey (AKSW/KILT) presented the methods behind the DBpedia’s Data Fusion and Global ID Management.

Showcase Session

Marek Dudáš (KIZI/UEP) started the DBpedia Showcase Session with a presentation on “Concept Maps with the help of DBpedia”, where he showed the audience how to create a “concept map” with the ContextMinds application. Furthermore, Tomáš Kliegr (KIZI/UEP) presented “Explainable Machine Learning and Knowledge Graphs”. He explained his contribution to a rule-based classifier for business use cases. Two other showcases followed: Václav Zeman (KIZI/UEP), who presented “RdfRules: Rule Mining from DBpedia” and Denis Streitmatter (AKSW/KILT), who demonstrated the “DBpedia API”.

Closing this Session, Miroslav Blasko (CTU, Prague) gave a presentation on “Ontology-based Dataset Exploration”. He explained a taxonomy developed for dataset description. Additionally, he presented several use cases that have the main goal of improving content-based descriptors.

Summing up, the DBpedia meetup in Prague brought together more than 50 DBpedia enthusiasts from all over Europe. They engaged in vital discussions about Linked Data, the DBpedia databus, as well as DBpedia use cases and services.

In case you missed the event, all slides and presentations are available on our website. Further insights  feedback, and photos about the event can be found on Twitter via #DBpediaPrague.

We are currently looking forward to the next DBpedia Community Meeting, on May 23rd, 2019 in Leipzig, Germany. This meeting is co-located with the Language, Data and Knowledge (LDK) conference. Stay tuned and check Twitter, Facebook and the website or subscribe to our newsletter for the latest news and updates.

Your DBpedia Association

The post Vítejte v Praze! appeared first on DBpedia Association.

Highlights

– Intro: DBpedia: Global and Unified Access to Knowledge (Graphs)

– DBpedia Databus presentation

– DBpedia Showcase Session

Quick Facts

– Web URL: https://wiki.dbpedia.org/meetings/Prague2019

– When: February 7th, 2019

– Where: University of Economics, nam. W. Churchilla 4, 130 67 Prague 3, Czech Republic

Schedule

– Please check the schedule for the upcoming DBpedia meetup here: https://wiki.dbpedia.org/meetings/Prague2019

Tickets

– Attending the DBpedia Community Meetup costs €40. DBpedia members get free admission, please contact your nearest DBpedia chapter or the DBpedia Association for a promotion code.

– You need to buy a ticket. Please check all details here: http://www.xmlprague.cz/conference-registration/

Sponsors and Acknowledgements

– XML conference Prague (http://www.xmlprague.cz/)

– Institute for Applied Informatics (http://infai.org/en/AboutInfAI)

– OpenLink Software (http://www.openlinksw.com/)

Organisation

-Milan Dojčinovski, AKSW/KILT

– Julia Holze, DBpedia Association

– Sebastian Hellmann, AKSW/KILT, DBpedia Association

– Tomáš Kliegr, KIZI/University of Economics, Prague

Tell us what cool things you do with DBpedia. If you would like to give a talk at the DBpedia meetup, please get in contact with the DBpedia Association.

We are looking forward to meeting you in Prague!

For latest news and updates check Twitter, Facebook and our Website or subscribe to our newsletter.

Your DBpedia Association

The post Call for Participation: DBpedia meetup @ XML Prague appeared first on DBpedia Association.

Come on, let’s do this.

Welcome to Vienna, Austria  – Semantics

More than 110 DBpedia enthusiasts joined our Community Meeting in Vienna, on September 10th, 2018. The event was again co-located with SEMANTiCS, a very successful collaboration. Lucky us, we got hold of two brilliant Keynote speakers, to open our meeting. Javier David Fernández García, Vienna University of Economics, opened the meeting with his keynote Linked Open Data cloud – act now before it’s too late. He reflected on challenges towards arriving at a truly machine-readable and decentralized Web of Data. Javier reviewed the current state of affairs, highlighted key technical and non-technical challenges, and outlined potential solution strategies. The second keynote speaker was Mathieu d’Aquin, Professor of Informatics at the Insight Centre for Data Analytics at NUI Galway. Mathieu, who is specialized in data analytics, completed the meeting with his keynote Dealing with Open-Domain Data.

The 12th edition of the DBpedia Community Meeting also covered a special chapter session, chaired by Enno Meijers, from the Dutch DBpedia Language Chapter. The speakers presented the latest technical or organizational developments of their respective chapter. This session has mainly created an exchange platform for the different DBpedia chapters. For the first time, representatives of the European chapters discussed problems and challenges of DBpedia from their point of view. Furthermore, tools, applications, and projects were presented by each chapter’s representative.

In case you missed the event, a more detailed article can be found here. All slides and presentations are also available on our Website. Further insights, feedback, and photos about the event are available on Twitter via #DBpediaDay.

Welcome to Mountain View  – GSoC mentor summit

GSoC was a vital part of DBpedia’s endeavors in 2018. We had three very talented students that with the help of our great mentors made it to the finish line of the program. You can read about their projects and success story in a dedicated post here.

After a successful 3-month mentoring, two of our mentors had the opportunity to attend the annual Google Summer of Code mentor summit. Mariano Rico and Thiago Galery represented DBpedia at the event this year. They engaged in a vital discussion about this years program, about lessons learned, highlights and drawbacks they experienced during the summer. A special focus was put on how to engage potential GSoC students as early as possible to get as much commitment as possible. The ideas the two mentors brought back in their suitcases will help to improve DBpedia’s part of the program for 2019. And apparently, chocolate was a very big thing there ;).

In case you have a project idea for GSoC2019 or want to mentor a DBpedia project next year, just drop us a line via dbpedia@infai.org. Also, as we intend to participate in the upcoming edition, please spread the word amongst students, and especially female students,  that fancy spending their summer coding on a DBpedia project. Thank you.

Welcome to London, England – Connected Data London 2018

In early November, we were invited to Connected Data London again. After 2017 this great event seems to become a regular in our DBpedia schedule.

Executive Director of the DBpedia Association, Sebastian Hellmannparticipated as panel candidate in the discussion around “Building Knowledge Graphs in the Real World”. Together with speakers from Thomson Reuters, Zalando, and Textkernel, he discussed definitions of KG, best practices of how to build and use knowledge graphs as well as the recent hype about it.

Visitors of CNDL2018 had the chance to grab a copy of our brand new flyer and exchange with us about the DBpedia Databus. This event gave us the opportunity to already met early adopters of our databus  – a decentralized data publication, integration, and subscription platform. Thank you very much for that opportunity.

A year went by

2018 has gone by so fast and brought so much for DBpedia. The DBpedia Association got the chance to meet more of DBpedia’s language chapters, we developed the DBpedia Databus to an extent that it can finally be launched in spring 2019. DBpedia is a community project relying on people and with the DBpedia Databus, we create a platform that allows publishing and provides a networked data economy around it. So stay tuned for exciting news coming up next year. Until then we like to thank all DBpedia enthusiasts around the world for their research with DBpedia, and support and contributions to DBpedia. Kudos to you.

All that remains to say is have yourself a very merry Christmas and a dazzling New Year. May 2019 be peaceful, exciting and prosperous.

Yours – being in a cheerful and festive mood –

DBpedia Association

The post A year with DBpedia – Retrospective Part 3 appeared first on DBpedia Association.

Let the travels begin.

Welcome to Thessaloniki, Greece & ESWC

DBpedians from the Portuguese Chapter presented their research results during ESWC 2018 in Thessaloniki, Greece.  the team around Diego Moussalem developed a demo to extend MAG  to support Entity Linking in 40 different languages. A special focus was put on low-resources languages such as Ukrainian, Greek, Hungarian, Croatian, Portuguese, Japanese and Korean. The demo relies on online web services which allow for an easy access to (their) entity linking approaches. Furthermore, it can disambiguate against DBpedia and Wikidata. Currently, MAG is used in diverse projects and has been used largely by the Semantic Web community. Check the demo via http://bit.ly/2RWgQ2M. Further information about the development can be found in a research paper, available here. 

Welcome back to Leipzig Germany

With our new credo “connecting data is about linking people and organizations”, halfway through 2018, we finalized our concept of the DBpedia Databus. This global DBpedia platform aims at sharing the efforts of OKG governance, collaboration, and curation to maximize societal value and develop a linked data economy.

With this new strategy, we wanted to meet some DBpedia enthusiasts of the German DBpedia Community. Fortunately, the LSWT (Leipzig Semantic Web Tag) 2018 hosted in Leipzig, home to the DBpedia Association proofed to be the right opportunity.  It was the perfect platform to exchange with researchers, industry and other organizations about current developments and future application of the DBpedia Databus. Apart from hosting a hands-on DBpedia workshop for newbies we also organized a well-received WebID -Tutorial. Finally,  the event gave us the opportunity to position the new DBpedia Databus as a global open knowledge network that aims at providing unified and global access to knowledge (graphs).

Welcome down under – Melbourne Australia

Further research results that rely on DBpedia were presented during ACL2018, in Melbourne, Australia, July 15th to 20th, 2018. The core of the research was DBpedia data, based on the WebNLG corpus, a challenge where participants automatically converted non-linguistic data from the Semantic Web into a textual format. Later on, the data was used to train a neural network model for generating referring expressions of a given entity. For example, if Jane Doe is a person’s official name, the referring expression of that person would be “Jane”, “Ms Doe”, “J. Doe”, or  “the blonde woman from the USA” etc.

If you want to dig deeper but missed ACL this year, the paper is available here.

Welcome to Lyon, France

In July the DBpedia Association travelled to France. With the organizational support of Thomas Riechert (HTWK, InfAI) and Inria, we finally met the French DBpedia Community in person and presented the DBpedia Databus. Additionally, we got to meet the French DBpedia Chapter, researchers and developers around Oscar Rodríguez Rocha and Catherine Faron Zucker.  They presented current research results revolving around an approach to automate the generation of educational quizzes from DBpedia. They wanted to provide a useful tool to be applied in the French educational system, that:

• helps to test and evaluate the knowledge acquired by learners and…
• supports lifelong learning on various topics or subjects. 

The French DBpedia team followed a 4-step approach:

1. Quizzes are first formalized with Semantic Web standards: questions are represented as SPARQL queries and answers as RDF graphs.
2. Natural language questions, answers and distractors are generated from this formalization.
3. We defined different strategies to extract multiple choice questions, correct answers and distractors from DBpedia.
4. We defined a measure of the information content of the elements of an ontology, and of the set of questions contained in a quiz.

Oscar R. Rocha and Catherine F. Zucker also published a paper explaining the detailed approach to automatically generate quizzes from DBpedia according to official French educational standards. 

Thank you to all DBpedia enthusiasts that we met during our journey. A big thanks to

With this journey from Europe to Australia and back we provided you with insights into research based on DBpedia as well as a glimpse into the French DBpedia Chapter. In our final part of the journey coming up next week, we will take you to Vienna,  San Francisco and London.  In the meantime, stay tuned and visit our Twitter channel or subscribe to our DBpedia Newsletter.

Have a great week.

Yours DBpedia Association

The post A year with DBpedia – Retrospective Part Two appeared first on DBpedia Association.

Who uses DBpedia anyway?…

This question started a research project for Frank Walraven, an Information Sciences Master student at Vrije Universiteit Amsterdam (VUA). The question came up during one of the meetings of the Dutch DBpedia chapter, of which VUA is a member.

If DBpedia users and their usage are better understood, this can lead to better servicing of those Dbpedia users by, for example, prioritizing the enrichment or improvement of specific sections of DBpedia. Characterizing use(r)s of a Linked Open Dataset is an inherently challenging task because in an open web world it is difficult to tell who is accessing your digital resources.

Frank conducted his MSc project research at the Dutch National Library  and used a hybrid approach utilizing both, a data-driven method based on user log analysis and a short survey to get to know the users of the dataset.

 As a scope, Frank selected just the Dutch DBpedia dataset. For the data-driven part of the method, Frank used a complete user log of HTTP requests on the Dutch DBpedia. This log file consisted of over 4.5 Million entries and logged both URI lookups and SPARQL endpoint requests. For this research, he only included a subset of the URI lookups.

Analysis of IP- Addresses od DBpedia Users

As a first analysis step, the requests’ origins IPs were categorized. Five classes can be identified (A-E), with the vast majority of IP addresses being in class “A”: Very large networks and bots. Most of the IP addresses in these lists could be traced back to search engine indexing bots such as those from Yahoo or Google. In classes B-F, Frank manually traced the top 30 most encountered IP-addresses. He concluded that even there 60% of the requests came from bots, 10% definitely not from bots, with 30% remaining unclear.

Step II – Identification of Page Requests

The second analysis step in the data-driven method consisted of identifying what types of pages were most requested. To cluster the thousands of DBpedia URI request, Frank retrieved the ‘categories’ of the pages. These categories are extracted from Wikipedia category links. An example is the “Android_TV” resource, which has two categories: “Google” and “Android_(operating_system)”. Following skos:broader links, a ‘level 2 category’ could also be found to aggregate to an even higher level of abstraction. As not all resources have such categories, this does not give a complete image, but it does provide some ideas on the most popular categories of items requested. After normalizing for categories with large amounts of incoming links, for example, the category “non-endangered animal”, the most popular categories where

• 1. Domestic & International movies,
• 2. Music,
• 3. Sports,
• 4. Dutch & International municipality information and
• 5. Books.

 Survey

Additionally, Frank set up a user survey to corroborate this evidence. The survey contained questions about the how and why of the respondents use of the Dutch DBpedia, including the categories they were most interested in.

The survey was distributed using the Dutch DBpedia website and via Twitter. However, the endeavour only attracted 5 respondents. This illustrates the difficulty of the problem that users of the DBpedia resource are not necessarily easily reachable through communication channels. The five respondents were all quite closely related to the chapter but the results were interesting nonetheless. Most of the DBpedia users used the DBpedia SPARQL endpoint. The full results of the survey can be found through Frank’s thesis, but in terms of corroboration, the survey revealed that four out of the five categories found in the data-driven method were also identified in the top five results from the survey. The fifth one identified in the survey was ‘geography’, which could be matched to the fifth from the data-driven method.

Conclusion

Frank’s research shows that it remains a challenging problem, using a combination of data-driven and user-driven method. Yet,  it is indeed possible to get an indication into the most-used categories on DBpedia. Within the Dutch DBpedia Chapter, we are currently considering follow-up research questions based on Frank’s research. For further information about the work of the Dutch DBpedia chapter, please visit their website. 

A big thanks to the Dutch DBpedia Chapter for supervising this research and providing insights via this post.

Yours

DBpedia Association

The post Who are these DBpedia users ? …(and why ? ) appeared first on DBpedia Association.

Survey Evaluation – Episode Two

The second survey focused on technical matters. We asked the chapters about the usage of DBpedia services and tools, technical problems and challenges and potential reasons to overcome them.  Have a look below.

Again, only nine out of 21 DBpedia chapters participated in this survey. And again, that means, the results only represent roughly 42% of the DBpedia chapter population

The good news is, all chapters maintain a local DBpedia endpoint. Yay! More than 55 % of the chapters perform their own extraction. The rest of them apply a hybrid approach reusing some datasets from DBpedia releases and additionally, extract some on their own.

Datasets, Services and Applications

In terms of frequency of dataset updates, the situation is as follows:  44,4 % of the chapters update them once a year. The answers of the remaining ones differ in equal shares, depending on various factors. See the graph below. 

When it comes to the maintenance of links to local datasets, most of the chapters do not have additional ones. However, some do maintain links to, for example, Greek WordNet, the National Library of Greece Authority record, Geonames.jp and the Japanese WordNet. Furthermore, some of the chapters even host other datasets of local interest, but mostly in a separate endpoint, so they keep separate graphs.

Apart from hosting their own endpoint, most chapters maintain one or the other additional service such as Spotlight, LodLive or LodView.

Moreover,  the chapters have additional applications they developed on top of DBpedia data and services.

Besides, they also gave us some reasons why they were not able to deploy DBpedia related services. See their replies below.

DBpedia Chapter set-up

Lastly, we asked the technical heads of the chapters what the hardest task for setting up their chapter had been.  The answers, again, vary as the starting position of each chapter differed. Read a few of their replies below.

The hardest technical task for setting up the chapter was:

• to keep virtuoso up to date
• the chapter specific setup of DBpedia plugin in Virtuoso
• the Extraction Framework
• configuring Virtuoso for serving data using server’s FQDN and Nginx proxying
• setting up the Extraction Framework, especially for abstracts
• correctly setting up the extraction process and the DBpedia facet browser
• fixing internationalization issues, and updating the endpoint
• keeping the extraction framework working and up to date
• updating the server to the specific requirements for further compilation – we work on Debian

Final  words

With all the data and results we gathered, we will get together with our chapter coordinator to develop a strategy of how to improve technical as well as organizational issues the surveys revealed. By that, we hope to facilitate a better exchange between the chapters and with us, the DBpedia Association. Moreover, we intend to minimize barriers for setting up and maintaining a DBpedia chapter so that our chapter community may thrive and prosper.

In the meantime, spread your work and share it with the community. Do not forget to follow and tag us on Twitter ( @dbpedia ). You may also want to subscribe to our newsletter.

We will keep you posted about any updates and news.

Yours

DBpedia Association

The post DBpedia Chapters – Survey Evaluation – Episode Two appeared first on DBpedia Association.

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

The post DBpedia Chapters – Survey Evaluation – Episode One appeared first on DBpedia Association.