Just recently, DBpedia Association member and hosting specialist, OpenLink released the DBpedia Usage report, a periodic report on the DBpedia SPARQL endpoint and associated Linked Data deployment.
The report not only gives some historical insight into DBpedia’s usage, number of visits and hits per day but especially shows statistics collected between October 2016 and December 2017. The report covers more than a year of logs from the DBpedia web service operated by OpenLink Software at http://dbpedia.org/sparql/.
Before we want to highlight a few aspects of DBpedia’s usage we would like to thank Open Link for the continuous hosting of the DBpedia Endpoint and the creation of this report
Speaking of which, as you can see in the following tables, there has been a massive increase in the number of hits coinciding with the DBpedia 2015–10 release on April 1st, 2016.
This boost can be attributed to an intensive promotion of DBpedia via community meetings, communication with various partners in the Linked Data community and Social media presence among the community, in order to increase backlinks.
Since then, not only the numbers of hits increased but DBpedia also provided for better data quality. We are constantly working on improving accessibility, data quality and stability of the SPARQL endpoint. Kudos to Open Link for maintaining the technical baseline for DBpedia.
… that it has already been eleven years since the first DBpedia dataset was released? Eleven years of development, improvements and growth, and now, 13 billion pieces of information are comprised in our last DBpedia release. We want to take this opportunity to send out a big thank you to all contributors, developers, coders, hosters, funders, believers and DBpedia enthusiasts who made that possible. Thank you for your support.
But, apart from our data sets, there is much more DBpedia has been doing., especially during the past year. Think about the success story of Wouter Maroy, a GSoC 2016 student who got the opportunity to do a six weeks internship at our DBpedia office in Leipzig and who is still contributing to DBpedia’s progress.
All in all, 2017 was highly successful and full of exciting events. Remember our 10th DBpedia Community Meeting in Amsterdam featuring an inspiring keynote by Dr. Chris Welty, one of the developers at IBM computer Watson. Our DBpedia meetings are always a great way to bring the community closer together, and to not only meet our DBpedia audience but also new faces. Therefore, we have already started to plan our community meetings for 2018.
We hope to see you in Poznan, Poland, in spring and to meet you during the SEMANTiCS Conference in Vienna, from 10th – 13th of September 2018. Additionally, if everything goes according to plan, we will be mentoring young DBpedia enthusiasts throughout summer in GSoC 2018 and meet the US DBpedia community in autumn this year. Follow us on Twitter or check our Website for the latest News.
And last but not least, this year we plan something special. DBpedia intends to participate in Coding DaVinci – Germany’s first open cultural hackathon, which happens to take place in Leipzig, right around the corner. Aspiring data enthusiast will develop new creative applications from cultural open data. The kick-off is in early April, followed by 9 weeks of cooperative coding. We are eagerly awaiting the start of this event.
We do hope, we will meet you and some new faces during our events this year. The DBpedia Association want to get to know you because DBpedia is a community effort and would not continue to develop, improve and grow without you. Thank you and see you soon…
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 .
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).
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 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).
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).
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).
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).
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).
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.
Google summer of Code is a global program focused on introducing students to open source software development.
During the 3 months summer break from university, students work on a programming projects with an open source organization, like DBpedia.
We are part of this exciting program for more than 5 years now. Many exciting projectsdeveloped as results of intense coding during hot summers. Presenting you Wouter Maroy, who has been a GSoC student at GSoc 2016 and who is currently a mentor in this years program, we like to give you a glimpse behind the scenes and show you how important the program is to DBpedia.
Success Story: Wouter Maroy
Who are you?
I’m Wouter Maroy, a 23 years old Master’s student in Computer Science Engineering at Ghent University (Belgium). I’m affiliated with IDLab – imec. Linked Data and Big Data technologies are my two favorite fields of interest. Besides my passion for Computer Science, I like to travel, explore and look for adventures. I’m a student who enjoys his student life in Ghent.
What is your main interest in DBpedia and what was your motivation to apply for a DBpedia project at GSoC 2016.
I took courses during my Bachelors with lectures about Linked Data and the Semantic Web which of course included DBpedia; it’s an interesting research field. Before my GSoC 2016 application I did some work on Semantic Web technologies and on a technology (RML) that was required for a GSoC 2016 project that was listed by DBpedia. I wanted to get involved in Open Source and DBpedia, so I applied.
What did you do?
DBpedia has used a custom mapping language up until now to generate structured data from raw data from Wikipedia infoboxes. A next step was to improve this process to a more modular and generic approach that leads to higher quality linked data generation . This new approach relied on the integration of RML, the RDF Mapping Language and was the goal of the GSoC 2016 project I applied for. Understanding all the necessary details about the GSoC project required some effort and research before I started with coding. I also had to learn a new programming language (Scala). I had good assistance from my mentors so this turned out very well in the end. DBpedia’s Extraction Framework, which is used for extracting structured data from Wikipedia, has a quite large codebase. It was the first project of this size I was involved in. I learned a lot from reading its codebase and from contributing by writing code during these months.
Dimitris Kontokostas and Anastasia Dimou were my two mentors. They guided me well throughout the project. I interacted daily with them through Slack and each week we had a conference call to discuss the project. After many months of research, coding and discussing we managed to deliver a working prototype at the end of the project. The work we did was presented in Leipzig on the DBpedia day during SEMANTICS 16’. Additionally, this work will also be presented at ISWC 2017.
How do you currently contribute to improve DBpedia?
I’m mentoring a GSoC17 project together with Dimitris Kontokostas and Anastasia Dimou as a follow up on the work that was done by our GSoC 2016 project last year. Ismael Rodriguez is the new student who is participating in the project and he already delivered great work! Besides being a mentor for GSoC 2017, I make sure that the integration of RML into DBpedia is going into the right direction in general (managing, coding,…). For this reason, I worked at the KILT/DBpedia office in Leipzig during summer for 6 weeks. Joining and getting to know the team was a great experience.
What did you gain from the project?
Throughout the project I practiced coding, working in a team, … I learned more about DBpedia, RML, Linked Data and other related technologies. I’m glad I had the opportunity to learn this much from the project. I would recommend it to all students who are curious about DBpedia, who are eager to learn and who want to earn a stipend during summer through coding. You’ll learn a lot and you’ll have a good time!
Final words to future GSoC applicants for DBpedia projects.
Give it a shot! Really, it’s a lot of fun! Coding for DBpedia through GSoC is a great, unique experience and one who is enthusiastic about coding and the DBpedia project should definitely go for it.
After our 2nd Community Meeting in the US, we delighted the Irish DBpedia Community with the 9th DBpedia Community Meeting, which was co-located with the Language, Data and Knowledge Conference 2017 in Galway at the premises of the NUI Galway.
First and foremost, we would like to thank John McCrae (Insight Centre for Data Analytics, NUI Galway) and the LDK Conference for co-hosting and support the event.
The focus of this Community Meeting was the Irish DBpedia and Linked Data Community in Ireland. Therefore we invited local data scientists as well as European DBpedia enthusiasts to discuss the state of Irish Linked Data.
The meeting started with two compelling keynotes by Brian Ó Raghallaigh, Dublin City University and Logainm.ie, and Sharon Flynn, NUI Galway and Wikimedia Ireland. Brian presented Logainm.ie, a data use case about placenames in Ireland with a special focus on linked Logainm and machine-readable data.
His insightful presentation was followed by Sharon Flynn talking about Wikimedia in Ireland and the challenges of “this monumental undertaking” with particular reference to the Wikimedia Community in Ireland.
For more details on the content of the presentations, follow the links to the slides.
As a regular part of the DBpedia Community Meeting we have two parallel sessions in the afternoon where DBpedia newbies can learn about what DBpedia is and how to use the DBpedia data sets.
Participants who wanted to learn DBpedia basics joined the DBpedia Tutorial Session byMarkus Freudenberg (DBpedia Release Manager). The DBpedia Association Hour provided a platform for the community to discuss and give feedback.
Additionally, Sebastian Hellmann and Julia Holze, members of the DBpedia Association, updated the participants about the growing number of the DBpedia Association members, the formalized DBpedia language chapters, the established DBpedia Community Committee and they informed about technical developments such as the DBpedia API.
Ontology Engineering and Software Alignment in the ALIGNED Project
The afternoon session started with the DBpedia 2016-10 release update by Markus Freudenberg (DBpedia Release Manager). Following this, Kevin Chekov Feeney, (Trinity College Dublin) presented the software alignment in the ALIGNED project. He talked about “Generating correct-by-construction semantic datasets from unstructured, semi-structured and badly structured data sources”.
At this point, we also like to thank the ALIGNED project for the development ofDBpedia as a project use case and for covering parts of the travel cost.
Session about Irish Linked Data Projects
Chaired by Rob Brennan and Bianca Pereira, the speakers in the last session presented new Irish Linked Data Projects, for example GeoHive, BIOOPENER and the TCD Open Linked Data Engagement Fund Project. The following panel session gave DBpedia and Linked Data enthusiasts a platform for exchange and discussion. Outcome of this session was the creation of a roadmap for the Irish Linked Data with all participants.
Following, you find a list of all presentations of this session:
Closing this session John McCrae announced that the next edition of the Language, Data and Knowledge (LDK) Conference is scheduled for 2019 in Germany. We at the DBpedia Association are now looking forward to welcome the LDK Community in Leipzig!
Social Evening Event
The Community Meeting slowly came to an end with our social evening event, which was held at the PorterShed in Galway. The evening session revolved around the topic How to exploit data commercially? and featured two short impulse talks. Paul Buitelaar started the session by presenting “Kibi”, which is an Open Source platform for Data Intelligence based on the search engine Elasticsearch. Finally, Sebastian Hellmann talked about “Improving the Utility of DBpedia by co-designing a public and commercial DBpedia API” (slides).
Summing up, the 9th DBpedia Community Meeting brought together more than 45 DBpedia enthusiasts from Ireland and Europe who engaged in vital discussions about Linked Data, DBpedia use cases and services.
Special thanks go to LDK 2017 for hosting the meeting.
Thanks Ireland and hello Amsterdam!
We are looking forward to the next DBpedia Community Meeting which will be held in Amsterdam, Netherlands. Co-located with the SEMANTiCS17, the Community will get together on the 14th of September on the DBpedia Day.
Sören Auer and the DBpedia Board members prepared a survey to assess the direction of the DBpedia Association. We wanted to know what the DBpedia Community thinks about DBpedia’s strategic priorities and how the funds of the DBpedia Association are be spent. Between February 2017 and April 2017, a total of 40 members of the DBpedia Community actively participated in the survey and voted as follows:
1. What should be the priorities of the DBpedia Association in the next year?
To overview the various priorities which were mentioned, the following digest illustrates the answers in four different groups. The most frequent answer was: to increase the data quality, followed by the enlargement of the DBpedia Community through broader dissemination.
2. What should be the priorities of the DBpedia Association in the next three years?
In contrast to question one, this one is based on the priorities the DBpedia Association focuses on during the next three years. As well as in the previous overview, the specified priorities are divided into four categories.
3. What is your main interest in DBpedia?
The chart above depicts the several main interests in DBpedia. The majority of participants have an “academic & professional” (45.7%) interest in DBpedia, followed by “professional” (28.6%) and “academic” (20.0%) interests. Only 2.9% of the answers are student-related interests.
4. How should the funds of the association be used?
With respects to “How should the funds of the association be used?”, most attendees chose “service provisioning”. The “development of new DBpedia features” was the second most popular choice. Nevertheless, also “Community building” and “release production” scored many votes.
5. How should the DBpedia Association collaborate with national/language chapters?
Agreeing on strategic goals; making sure that national contributions can be spread to other chapters, thus increasing the overall usability of DBpedia; keeping track of good practices
Facilitating grassroots initiatives – so mainly promote and stimulate national/language initiatives
Local events related to DBpedia tasks
Regular events to share ideas and data
Join other languages members onto DBpedia
As an umbrella organization: support, mediation, and representation
Regular exchange and involvement
Consult, try to figure out common priorities
6. Should DBpedia open itself to contain and curate more data not directly extracted from Wikipedia?As the chart above clearly depicts, more than half of the participants are in favor of DBpedia comprising datasets not directly derived or extracted from Wikipedia. In contrast, 34.3% have the oppositional opinion and appreciate DBpedia focussing solely on data extraction from Wikipedia.
If yes, which other datasets should DBpedia prioritize for fusion to improve its coverage and quality?
7. Which of the following features do you consider most important?
The following diagram gives a review of particular features and their importance from the participants point of view. As the result of question one reveals, data quality is considered the most important issue by the survey participants (23.7%). The second most important features, with 17.2% each, are: the provision of datasets extracted from the Wikipedia article text, substantial collaboration/integration with WikiData and a provision of better search, respectively an exploration of user interfaces.
8. Any other question, feedback, opinion, ideas or suggestion you would like to send to the association.
Increased support of non-RDF publication formats is probably wise as an insurance policy that DBpedia will stay relevant.
In users mailing-list being more open-minded in an easy manner and always signalling provocative postings are welcome. And I fear it is a bit late for this survey, but better late than never, my greetings to all making some thoughts about this stuff.
DBpedia Spotlight should return Wikidata URIs by default, for stability
Use a richer ontology without contradictions, e.g. Book-Physical vs. Book-Conceptual Work
Thank you for your input and your participation! Your priorities and opinions are of vital importance for the success of DBpedia in the future. We will discuss the implementation of your answers during our next DBpedia Board Meetings in order to find a reasonable strategic direction of the DBpedia Association for the next years.
This release took us longer than expected. We had to deal with multiple issues and included new data. Most notable is the addition of the NIF annotation datasets for each language, recording the whole wiki text, its basic structure (sections, titles, paragraphs, etc.) and the included text links. We hope that researchers and developers, working on NLP-related tasks, will find this addition most rewarding. The DBpedia Open Text Extraction Challenge (next deadline Mon 17 July for SEMANTiCS 2017) was introduced to instigate new fact extraction based on these datasets.
We want to thank anyone who has contributed to this release, by adding mappings, new datasets, extractors or issue reports, helping us to increase coverage and correctness of the released data. The European Commission and the ALIGNED H2020 project for funding and general support.
You want to read more about the New Release? Click below for further details.
Altogether the DBpedia 2016-10 release consists of 13 billion (2016-04: 11.5 billion) pieces of information (RDF triples) out of which 1.7 billion (2016-04: 1.6 billion) were extracted from the English edition of Wikipedia, 6.6 billion (2016-04: 6 billion) were extracted from other language editions and 4.8 billion (2016-04: 4 billion) from Wikipedia Commons and Wikidata.
In addition, adding the large NIF datasets for each language edition (see details below) increased the number of triples further by over 9 billion, bringing the overall count up to 23 billion triples.
The NLP Interchange Format (NIF) aims to achieve interoperability between Natural Language Processing (NLP) tools, language resources and annotations. To extend the versatility of DBpedia, furthering many NLP-related tasks, we decided to extract the complete human- readable text of any Wikipedia page (‘nif_context’), annotated with NIF tags. For this first iteration, we restricted the extent of the annotations to the structural text elements directly inferable by the HTML (‘nif_page_structure’). In addition, all contained text links are recorded in a dedicated dataset (‘nif_text_links’).
The DBpedia Association started the Open Extraction Challenge on the basis of these datasets. We aim to spur knowledge extraction from Wikipedia article texts in order to dramatically broaden and deepen the amount of structured DBpedia/Wikipedia data and provide a platform for benchmarking various extraction tools with this effort.
If you want to participate with your own NLP extraction engine, the next deadline for the SEMANTICS 2017 is July 17th.
We included an example of these structures in section five of the download-page of this release.
A considerable amount of work has been done to streamline the extraction process of DBpedia, converting many of the extraction tasks into an ETL setting (using SPARK). We are working in concert with the Semantic Web Company to further enhance these results by introducing a workflow management environment to increase the frequency of our releases.
In case you missed it, what we changed in the previous release (2016-04)
We added a new extractor for citation data that provides two files:
citation links: linking resources to citations
citation data: trying to get additional data from citations. This is a quite interesting dataset but we need help to clean it up
In addition to normalised datasets to English DBpedia (en-uris), we additionally provide normalised datasets based on the DBpedia Wikidata (DBw) datasets (wkd-uris). These sorted datasets will be the foundation for the upcoming fusion process with wikidata. The DBw-based uris will be the only ones provided from the following releases on.
We now filter out triples from the Raw Infobox Extractor that are already mapped. E.g. no more “<x> dbo:birthPlace <z>” and “<x> dbp:birthPlace|dbp:placeOfBirth|… <z>” in the same resource. These triples are now moved to the “infobox-properties-mapped” datasets and not loaded on the main endpoint. See issue 22 for more details.
Major improvements in our citation extraction. See here for more details.
We incorporated the statistical distribution approach of Heiko Paulheim in creating type statements automatically and providing them as additional datasets (instance_types_sdtyped_dbo).
DBpedia Fusion: We finally started working again on fusing DBpedia language editions. Johannes Frey is taking the lead in this project. The next release will feature intermediate results.
Id Management: Closely pertaining to the DBpedia Fusion project is our effort to introduce our own Id/IRI management, to become independent of Wikimedia created IRIs. This will not entail changing out domain or entity naming regime, but providing the possibility of adding entities of any source or scope.
RML Integration: Wouter Maroy did already provide the necessary groundwork for switching the mappings wiki to an RML based approach on Github. Wouter started working exclusively on implementing the Git based wiki and the conversion of existing mappings last week. We are looking forward to the consequent results of this process.
Further development of SPARK Integration and workflow-based DBpedia extraction, to increase the release frequency.
New languages extracted fromWikipedia:
South Azerbaijani (azb), Upper Sorbian (hsb), Limburgan (li), Minangkabau (min), Western Mari (mrj), Oriya (or), Ossetian (os)
SDTypes: We extended the coverage of the automatically created type statements (instance_types_sdtyped_dbo) to English, German and Dutch.
Extensions: In the extension folder (2016-10/ext) we provide two new datasets (both are to be considered in an experimental state:
DBpedia World Facts: This dataset is authored by the DBpedia Association itself. It lists all countries, all currencies in use and (most) languages spoken in the world as well as how these concepts relate to each other (spoken in, primary language etc.) and useful properties like iso codes (ontology diagram). This Dataset extends the very useful LEXVO dataset with facts from DBpedia and the CIA Factbook. Please report any error or suggestions in regard to this dataset to Markus.
JRC-Alternative-Names: This resource is a link based complementary repository of spelling variants for person and organisation names. The data is multilingual and contains up to hundreds of variations entity. It was extracted from the analysis of news reports by the Europe Media Monitor (EMM) as available on JRC-Names.
The DBpedia community added new classes and properties to the DBpedia ontology via the mappings wiki. The DBpedia 2016-04 ontology encompasses:
1,105 object properties
1,622 datatype properties
132 specialised datatype properties
414 owl:equivalentClass and 220 owl:equivalentProperty mappings external vocabularies
The editor community of the mappings wiki also defined many new mappings from Wikipedia templates to DBpedia classes. For the DBpedia 2016-10 extraction, we used a total of 5887 template mappings (DBpedia 2015-10: 5800 mappings). The top language, gauged by the number of mappings, is Dutch (648 mappings), followed by the English community (606 mappings).
Markus Freudenberg (University of Leipzig / DBpedia Association) for taking over the whole release process and creating the revamped download & statistics pages.
Dimitris Kontokostas (University of Leipzig / DBpedia Association) for conveying his considerable knowledge of the extraction and release process.
All editors that contributed to the DBpedia ontology mappings via the Mappings Wiki.
The whole DBpedia Internationalization Committee for pushing the DBpedia internationalization forward.
Václav Zeman and the whole LHD team (University of Prague) for their contribution of additional DBpedia types
Alan Meehan (TCD) for performing a big external link cleanup
Aldo Gangemi (LIPN University, France & ISTC-CNR, Italy) for providing the links from DOLCE to DBpedia ontology.
SpringerNature for offering a co-internship to a bright student and developing a closer relation to DBpedia on multiple issues, as well as Links to their SciGraph subjects.
Kingsley Idehen, Patrick van Kleef, and Mitko Iliev (all OpenLink Software) for loading the new data set into the Virtuoso instance that provides 5-Star Linked Open Data publication and SPARQL Query Services.
OpenLink Software (http://www.openlinksw.com/) collectively for providing the SPARQL Query Services and Linked Open Data publishing infrastructure for DBpedia in addition to their continuous infrastructure support.
Ruben Verborgh from Ghent University – imec for publishing the dataset as Triple Pattern Fragments, and imec for sponsoring DBpedia’s Triple Pattern Fragments server.
Ali Ismayilov (University of Bonn) for extending and cleaning of the DBpedia Wikidata dataset.
All the GSoC students and mentors which have directly or indirectly worked on the DBpedia release
We are very excited to announce this year’s final students for our projects at the Google Summer of Code program (GSoC).
Google Summer of Code is a global program focused on bringing more student developers into open source software development. Stipends are awarded to students to work on a specific DBpedia related project together with a set of dedicated mentors during summer 2017 for the duration of three months.
For the past 5 years DBpedia has been a vital part of the GSoC program. Since the very first time many Dbpedia projects have been successfully completed.
In this years GSoC edition, DBpedia received more than 20 submissions for selected DBpedia projects. Our mentors read many promising proposals, evaluated them and now the crême de la crême of students snatched a spot for this summer. In the end 7 students from around the world were selected and will jointly work together with their assigned mentors on their projects. DBpedia developers and mentors are really excited about this 7 promising student projects.
You want to read more about their specific projects? Just click below… or check GSoC pages for details.
Ismael Rodriguez– Project Description: Although the DBPedia Extraction Framework was adapted to support RML mappings thanks to a project of last year GSoC, the user interface to create mappings is still done by a MediaWiki installation, not supporting RML mappings and needing expertise on Semantic Web. The goal of the project is 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. Moreover, it should also facilitate data transformations and overall DBPedia dataset generation. Mentors: Anastasia Dimou, Dimitris Kontokostas, Wouter Maroy
Ram Ganesan Athreya – Project Description:The requirement of the project is to build a conversational Chatbot for DBpedia which would be deployed in at least two social networks.There are three main challenges in this task. First is understanding the query presented by the user, second is fetching relevant information based on the query through DBpedia and finally tailoring the responses based on the standards of each platform and developing subsequent user interactions with the Chatbot.Based on my understanding, the process of understanding the query would be undertaken by one of the mentioned QA Systems (HAWK, QANARY, openQA). Based on the response from these systems we need to query the DBpedia dataset using SPARQL and present the data back to the user in a meaningful way. Ideally, both the presentation and interaction flow needs to be tailored for the individual social network.I would like to stress that although the primary medium of interaction is text, platforms such as Facebook insist that a proper mix between chat and interactive elements such as images, buttons etc would lead to better user engagement. So I would like to incorporate these elements as part of my proposal.
Mentor: Ricardo Usbeck
Nausheen Fatma – Project discription: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. Along the way, I would also try my best to come up with any new research approach for the problem.
Mentors: Sandro Athaide Coelho, Tommaso Soru
Akshay Jagatap – Project Description: The project aims at defining embeddings to represent classes, instances and properties. Such a model tries to quantify semantic similarity as a measure of distance in the vector space of the embeddings. I believe this can be done by implementing Random Vector Accumulators with additional features in order to better encode the semantic information held by the Wikipedia corpus and DBpedia graphs.
Mentors: Pablo Mendes, Sandro Athaide Coelho, Tommaso Soru
Luca Virgili – Project Description: In Wikipedia a lot of data are hidden in tables. What we want to do is to read correctly all tables in a page. First of all, we need a tool that can allow us to capture the tables represented in a Wikipedia page. After that, we have to understand what we read previously. Both these operations seem easy to make, but there are many problems that could arise. The main issue that we have to solve is due to how people build table. Everyone has a particular style for representing information, so in some table we can read something that doesn’t appear in another structure. In this paper I propose to improve the last year’s project and to create a general way for reading data from Wikipedia tables. I want to review the parser for Wikipedia pages for trying to understand more types of tables possible. Furthermore, I’d like to build an algorithm that can compare the column’s elements (that have been read previously by the parser) to an ontology so it could realize how the user wrote the information. In this way we can define only few mapping rules, and we can make a more generalized software.
Mentors: Emanuele Storti, Domenico Potena
Shashank Motepalli – Project Description: DBpedia tries to extract structured information from Wikipedia and make information available on the Web. In this way, the DBpedia project develops a gigantic source of knowledge. However, the current system for building DBpedia Ontology relies on Infobox extraction. Infoboxes, being human curated, limit the coverage of DBpedia. This occurs either due to lack of Infoboxes in some pages or over-specific or very general taxonomies. These factors have motivated the need for DBTax.DBTax follows an unsupervised approach to learning taxonomy from the Wikipedia category system. It applies several inter-disciplinary NLP techniques to assign types to DBpedia entities. The primary goal of the project is to streamline and improve the approach which was proposed. As a result, making it easy to run on a new DBpedia release. In addition to this, also to work on learning taxonomy of DBTax to other Wikipedia languages.
Mentors: Marco Fossati, Dimitris Kontokostas
Krishanu Konar – Project Description: 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 project focuses on the extraction of relevant but hidden data which lies inside lists in Wikipedia pages. The main objective of the project would be to create a tool that can extract information from wikipedia lists, form appropriate RDF triplets that can be inserted in the DBpedia dataset.
Mentor: Marco Fossati
Congrats to all selected students! We will keep our fingers crossed now and patiently wait until early September, when final project results are published.
An encouraging note to the less successful students.
The competition for GSoC slots is always on a very high level and DBpedia only has a limited amount of slots available for students. In case you weren’t among the selected, do not give up on DBpedia just yet. There are plenty of opportunities to prove your abilities and be part of the DBpedia experience. You, above all, know DBpedia by heart. Hence, contributing to our support system is not only a great way to be part of the DBpedia community but also an opportunity to be vital to DBpedia’s development. Above all, it is a chance for current DBpedia mentors to get to know you better. It will give your future mentors a chance to support you and help you to develop your ideas from the very beginning.
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