New DBpedia Usage Report

Friday, October 30, 2020 - 12:34pm

Our partner Openlink recently published a new DBpedia usage report on the SPARQL endpoint and associated Linked Data deployment

Copyright © 2020 OpenLink Software

Introduction

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 July 2017 and September 2020, spanning more than 3 years of logs from the DBpedia web service operated by our partner OpenLink Software at http://dbpedia.org/sparql/.

Before we want to highlight a few aspects of DBpedia’s usage we would like to thank OpenLink for the continuous hosting of the DBpedia Endpoint and the creation of this report.

DBpedia Usage Report: Historical Overview

The first table shows the average numbers of Visits and Hits per day during the time each DBpedia dataset was live on the http://dbpedia.org/sparql endpoint. Similarly to the hits, we also see a huge increase in visits coinciding with the DBpedia 2015–10 release on April 1st, 2016.

Historic overview
Historical overview of visits and hits per day in the cause of the last 10 years.

This boost was attributed to an intensive promotion of DBpedia via community meetings, and exchange with various partners in the Linked Data community. In addition, our Social Media activity in the community increased backlinks. Since then, not only the numbers of hits rose 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.

The next graph shows the percentage of the total number of hits in a given time period that can be attributed to the /sparql endpoint. If we look at the historical data from 2014–09 onward, we can see the requests to /sparql were about 60.16% of the total number of hits.

DBpedia Usage Report: Current Statistics

If we focus on the last 12 months, we can see a slightly lower average of 48.10%, as shown in the graph below. This means that around 50% of traffic uses Linked Data constructions to view the information available through DBpedia. To put this into perspective, that means that of the average of 7.2 million hits to the endpoint on a given day, 3.6 million hits are Linked Data Deployment hits.

The following table shows the information on visits, sited and hits for
each month between September 2019 and 2020.

Statistical overview of the last year.
Overview of the last 12 months.

For detailed information on the specific usage numbers, please visit the original report by Openlink published here. Also, older reporst are available through their site.

Further Links

For the latest news, subscribe to the DBpedia Newsletter, check our DBpedia Website and follow us on Twitter, Facebook, and LinkedIn .

Thanks for reading and keep using DBpedia!

Yours DBpedia Associaton

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More than 130 knowledge graph enthusiasts joined the KGiA event.

Friday, October 16, 2020 - 2:20pm

Opening the KG in Action event

The SEMANTiCS Onsite Conference 2020 had to be postponed till September 2021. To bridge the gap until 2021, we took this opportunity to organize the Knowledge Graphs in Action (KGiA) online track as a SEMANTiCS satellite event on October 6, 2020. This new online conference is a combination of two existing events: the DBpedia Community Meeting and the annual Spatial Linked Data conference organised by EuroSDR and the Platform Linked Data Netherlands. We combined the best of both and as a bonus we added a track about Geo-information Integration organized by EuroSDR. As special joint sessions we presented four keynote speakers. 

First and foremost, we would like to thank the SEMANTiCS, EuroSDR and Platform Linked Data Netherlands for organizing the KGiA online event and many thanks to all chairs who supported the conference.

Following, we will give you a brief retrospective about the keynote presentations and talks.

Opening & Keynote #1

The Knowledge Graphs in Action conference was opened with a keynote presentation ‘Data Infrastructure for Energy System Models’ by Carsten Hoyer-Klick (German Aerospace Center). He presented LOD GEOSS, a project for the development of a distributed data infrastructure for the analysis of energy systems. The project is about the development of networked database concepts based on the ideas of linked open data and the semantic web for input and output data of energy system models in energy systems analysis. Afterwards the conference chairs offered three parallel sessions in the morning. 

Morning Sessions 

Session 1: Spatial Linked Data Country Update

In this session 7 speakers presented the uptake and latest progress of Spatial Linked Data adoption in European countries, either within national mapping agencies or beyond.

Session 2: VGI country presentations

There is an increasing use of crowdsourced geo-information (CGI) in spatial data applications by National Mapping and Cadastral Agencies (NMCAs). Applications range from using CGI for supporting the actualisation of spatial data to adding extra content, such as land use, building entrances, road barriers, sensors placed in the public space and many more. This session hosted five presentations from NMCAs showing the status of their CGI integration in mapping applications and processes.

Session 3: DBpedia Member presentations

Members of the DBpedia Association presented their latest tools, applications and technical developments in this session. Filipe Mesquita (Diffbot) opened the member session with his talk ‘Beyond Human Curation: How Diffbot Is Building A Knowledge Graph of the Web’. Also ImageSnippets, timbr.ai and GNOSS gave interesting and delightful talks about their technical developments. Vassil Momtchev from Ontotext closed the session by giving insights into the GraphDB 9.4.   

For further details of the presentations follow the links to the slides on the event page.

Afternoon Sessions 

Keynote #2

The afternoon sessions started with an interesting keynote by Peter Mooney (Maynooth University). He talked about the opportunities for a more integrated approach to Geo-information integration. 

Dutch National Graph as a Digital Twin

After the second keynote Sebastian Hellmann, the CEO of the DBpedia Association, presented the development and methodology of the National Knowledge Graph for the Netherlands. In cooperation with Dutch partners, DBpedia invested two months to develop this new knowledge graph. His insightful presentation was followed by Benedicte Bucher (University Gustave Eiffel) talking about ‘Knowledge Graph on spatial digital assets in European’. She also presented the EuroSDR LDG initiative in many details.      

Afternoon Parallel Sessions

Session 4: Transforming Linked Data into a networked data economy – DBpedia Chapter Session

In the DBpedia Chapter Session, members of different European DBpedia chapters gave an overview about the data landscape in their countries. They presented identified business opportunities and important challenges, such as automated clearance of licenses in their countries. Enno Meijers (National Library of the Netherlands) summarized the data landscape in the Netherlands. There were also presentations about the data landscape in Brazil, Spain, Austria and Poland.   

Session 5: EuroSDR VGI data wrangling

This session intends to uncover new combinations and integration of CGI data with data from NMCAs which demonstrate the added value for map creation and map usage. Data wrangling (the process of creating small reproducible data processing workflows) is deployed for this work by using and combining existing geospatial software (desktop, web and mobile). In this session the results of the data wrangling process were presented. 

Session 6: Spatial Session

In this session, two speakers presented how they built knowledge graphs, and in the second part three presenters gave insights into tooling and presented the state of the art on working with Linked Data.

For further details of the presentations follow the links to the slides on the event page.

Keynote #3 and #4

Keynote #3 ‘Spatial Knowledge in Action – Deep semantics, geospatial thinking, and new cartographies’ was given by Marinos Kavouras (National Technical University of Athens). Marinos stated that the power of maps and modern cartographic language proves to have a new role for society at large, as an indispensable communication and cognitive tool. The KG in Action conference ended with the keynote presentation ‘Know, Know Where, KnowWhereGraph’ by Krzysztof Janowicz (University of California). During his live talk from California, Krzysztof provided an overview of ideas and hopes for creating geo-specific knowledge graphs and geo-enrichment services on top of this graph to address some of the aforementioned challenges.

In case you missed the event, all slides and presentations are also available on the DBpeda website. We will upload all recordings on the DBpedia youtube channel. Further insights, feedback and photos about the event are available on Twitter (#KGiA hashtag).

We are now looking forward to 2021. We plan to have meetings at the Knowledge Graph Conference and the SEMANTiCS conference in Amsterdam. Stay safe and check Twitter, LinkedIn and our Website or subscribe to our Newsletter for the latest news and information.

Yours,

DBpedia Association

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GSoC 2020 recap

Monday, October 12, 2020 - 12:45pm

With 45 project proposals, this GSoC edition marked a new record for DBpedia.

GSoc and DBpedia Sticker

Oh, what a year! 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. 

Each year has brought us new project ideas, many amazing students and mostly great project results that shaped the future of DBpedia. 

Even though Covid-19 changed a lot in the world, it couldn’t shake GSoC much. The program, designed to mentor youngsters from afar is almost too perfect for the current world situation. One of the advantages of Google Summer of Code is, especially in times like these, the chance to work on projects remotely, but still obtain a first deep dive into Open Source projects like us – DBpedia. 

Meet the students and their projects

This year, we had notably more applications than in the previous ones. With 45 project proposals, this GSoC edition marked a new record for DBpedia. Throughout the summer program, our seven finalists worked intensely on their challenging DBpedia projects with great outcomes to show to the public. Projects ranged from extending our DBpedia extraction framework to a DBpedia Database project as well as to an online tool to generate RDF from DBpedia abstracts. If you want to have deeper insights into our GSoC student’s work you can find their blogs and repos in the following list. Check them out! 

Thanks to all our mentors around the world for joining us in this endeavour, for mentoring with kindness and technical expertise. A huge shout out to those who have been by our side for so many years in a row. Many thanks to Tommaso Soru, Beyza Yaman, Diego Moussalem, Edgard Marx, Mariano Rico, Thiago Castro Ferreira, Luca Virgili as well as Sebastian Hellmann, Stuart Chan, Amandeep Srivastava, Julio Hernandez and Jan Forberg. 

Mentor Summit

During the previous years you might have noticed that we always organized a little lottery to decide which mentor or organization admin can join the annual GSoC mentor summit. As this year’s event will be held online, space is not limited to 300 something mentors but is open to all organization admins and mentors alike. The GSoC Virtual Mentor Summit takes place October 15- 16, 2020 and this year we hope all our mentors will find the time to join and exchange with fellow mentors from around dozens of open source projects. 

After GSoC is before the next GSoC

We can not wait for the 2021 edition. Likewise, if you are an ambitious student who is interested in open source development and working with DBpedia you are more than welcome to either contribute your own project idea or apply for project ideas we offer starting in early 2021.

In case you like to mentor a project do not hesitate to also get in touch with us via dbpedia@infai.org

Stay tuned, frequently check Twitter, LinkedIn or the DBpedia Forum to stay in touch and don’t miss your chance of becoming a crucial force in this endeavour as well as a vital member of the DBpedia community.

See you soon,

yours

DBpedia Association

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Call for Participants: DBpedia Autumn Hackathon

Friday, September 11, 2020 - 2:19pm

Dear DBpedians, Linked Data savvies and Ontologists,

We would like to invite you to join the DBpedia Autumn Hackathon 2020 as a new format to contribute to DBpedia, gain fame, win small prizes and experience the latest technology provided by DBpedia Association members. 
The hackathon is part of the Knowledge Graphs in Action conference on October 6, 2020. 

Timeline 

  • Registration of participants – main communication channel will be the #hackathon channel in DBpedia Slack (sign up, then add yourself to the channel). If you wish to receive a reminder email on Sep 21st, 2020 you can leave your email address in this form.
  • Until September 14th – preparation phase, participating organizations prepare details and track formation. Additional tracks can be proposed, please contact dbpedia-events@infai.org.
  • Announcement of details for each track, including prizes, participating data, demos as well as tools and tasks. Please check updates on the Hackathon website. – September 21st, 2020
  • Hacking period, coordinated via DBpedia slack September 21st to October 1st, 2020
  • Submission of hacking result (3 min video and 2-3 paragraph summary with links, if not stated otherwise in the track) – October 1st, 2020 at 23:59 Hawaii Time
  • Final Event, Each track chair will present a short recap of the track and announces prizes or summarizes the result of hacking. – October 5th, 2020 at 16:00 CEST
  • Knowledge Graphs in Action Event (see program) – October 6th, 2020 at 9:50 – 15:30 CEST
  • Results and videos are documented on the DBpedia Website and the DBpedia Youtube channel.

Member Tracks 

The member tracks are hosted by DBpedia Association members, who are technology leaders in the area of Knowledge Engineering. Additional tracks can be proposed until Sep 14th, please contact dbpedia-events@infai.org.

  • timbr SQL Knowledge Graph: Learn how to model, map and query ontologies in timbr and then model an ontology of GDELT, map it to the GDELT database, and answer a number of questions that currently are quite impossible to get from the BigQuery GDELT database. Cash prizes planned. 
  • GNOSS Knowledge Graph Builder: Give meaning to your organisation’s documents and data with a Knowledge Graph. 
  • ImageSnippets: Labeling images with semantic descriptions. Use DBpedia spotlight and an entity matching lookup to select DBpedia terms to describe images. Then explore the resulting dataset through searches over inference graphs and explore the ImageSnippets dataset through our SPARQL endpoint. Prizes planned. 
  • Diffbot: Build Your Own Knowledge Graph! Use the Natural Language API to extract triples from natural language text and expand these triples with data from the Diffbot Knowledge Graph (10+ billion entities, 1+ trillion facts). Check out the demo. All participants will receive access to the Diffbot KG and tools for (non-commercial) research for one year ($10,000 value).

Dutch National Knowledge Graph Track

Following the DBpedia FlexiFusion approach, we are currently flexi-fusing a huge, dbpedia-style knowledge graph that will connect many Linked Data sources and data silos relevant to the country of the Netherlands. We hope that this will eventually crystallize a well-connected sub-community linked open data (LOD) cloud in the same manner as DBpedia crystallized the original LOD cloud with some improvements (you could call it LOD Mark II). Data and hackathon details will be announced on 21st of September.

Organising committee:

Improve DBpedia Track

A community track, where everybody can participate and contribute in improving existing DBpedia components, in particular the extraction framework, the mappings, the ontology, data quality test cases, new extractors, links and other extensions. Best individual contributions will be acknowledged on the DBpedia website by anointing the WebID/Foaf profile.

(chaired by Milan Dojchinovski and Marvin Hofer from the DBpedia Association & InfAI and the DBpedia Hacking Committee, please message @m1ci to volunteer to the hacking committee)

DBpedia Open Innovation Track 

(not part of the hackathon, pre-announcement)

For the DBpedia Spring Event 2021, we are planning an Open Innovation Track, where DBpedians can showcase their applications. This endeavour will not be part of the hackathon as we are looking for significant showcases with development effort of months & years built on the core infrastructure of DBpedia such as the SPARQL endpoint, the data, lookup, spotlight, DBpedia Live, etc. Details will be announced during the Hackathon Final Event on October 5.  

(chaired by Heiko Paulheim et al.)

Stay tuned and check Twitter, Facebook and our Website or subscribe to our Newsletter for latest news and information.

The DBpedia Organizing Team


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‘Knowledge Graphs in Action’ online event on Oct 6, 2020

Tuesday, July 14, 2020 - 2:43pm

Due to current circumstances, the SEMANTiCS Onsite Conference 2020 had, unfortunately, to be postponed till September 2021. To bridge the gap until 2021, DBpedia, PLDN and EuroSDR will organize a SEMANTiCS satellite event online, on October 6, 2020. We set up an exciting themed program around ‘Knowledge Graphs in Action: DBpedia, Linked Geodata and Geo-information Integration’.

This new event is a combination of two already existing ones: the DBpedia Community Meeting, which is regularly held as part of the SEMANTiCS, and the annual Spatial Linked Data conference organised by EuroSDR and the Platform Linked Data Nederland. We fused both together and as a bonus, we added a track about Geo-information Integration hosted by EuroSDR. For the joint opening session, we recruited four amazing keynote speakers to kick the event off.    

Highlights of the Knowledge Graph in Action event

– Hackathon (starts 2 weeks earlier)

– Keynote by Carsten Hoyer-Click, German Aerospace Center

– Keynote by Marinos Kavouras, National Technical University of Athens

– Keynote by Peter Mooney, Maynooth University

– Spatial Linked Data Country Session

– DBpedia Chapter Session

– Self Service GIS Session

– DBpedia Showcase Session

Quick Facts

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

– When: October 6, 2020

– Where: The conference will take place fully online.

Schedule

– Please check the schedule for the upcoming Knowledge Graphs in Action event here: https://wiki.dbpedia.org/meetings/KnowledgeGraphsInAction  

Registration 

– Attending the conference is free. Registration is required though. Please get in touch with us if you have any problems during the registration stage. Register here to be part of the meeting: https://wiki.dbpedia.org/meetings/KnowledgeGraphsInAction 

Organisation

– Benedicte Bucher, University Gustave Eiffel, IGN, EuroSDR

– Erwin Folmer, Kadaster, University of Twente, Platform Linked Data Netherlands

– Rob Lemmens, University of Twente

– Sebastian Hellmann, AKSW/KILT, DBpedia Association

– Julia Holze, DBpedia Association

Don’t think twice and register now! Join the Knowledge Graph in Action event on October 6, 2020 to catch up with the latest research results and developments in the Semantic Web Community. Register here and meet us and other SEMANTiCS enthusiasts.

For latest news and updates check Twitter, LinkedIn, the DBpedia blog and our Website or subscribe to our newsletter.

We are looking forward to meeting you online!

Julia

on behalf of the DBpedia Association

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DBpedia Workshop at LDAC

Thursday, June 25, 2020 - 11:31am

More than 90 DBpedia enthusiasts joined the 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. This workshop series provides a focused overview on technical and applied research on the usage of Semantic Web, Linked Data and Web of Data technologies for the architecture and construction domains (design, engineering, construction, operation, etc.). The workshop aims at gathering researchers, industry stakeholders, and standardization bodies of the broader Linked Building Data (LBD) community.

First and foremost, we would like to thank the LDAC committee for hosting our virtual meeting and many thanks to Beyza Yaman, Milan Dojchinovski, Johannes Frey and Kris McGlinn for organizing and chairing the DBpedia workshop. 

Following, we will give you a brief retrospective about the presentations.

Opening & Keynote 

The first virtual DBpedia meeting was opened with a keynote presentation ‘{RDF} Data quality assessment – connecting the pieces’ by Dimitris Kontokostas (diffbot, US). He gave an overview on the latest developments and achievements around Data Quality. His presentation was focused on defining data quality and identification of data quality issues.  

Sebastian Hellmann gave a brief overview of DBpedia’s history. Furthermore, he presented the updated DBpedia Organisational architecture, including the vision of the new DBpedia chapters and benefits of the DBpedia membership.

Shortly after,  Milan Dojchinovski (InfAI/CTU in Prague) gave a presentation on  ‘Querying and Integrating (Architecture and Construction) Data with DBpedia’. ‘The New DBpedia Release Cycle’ was introduced by Marvin Hofer (InfAI). Closing the Showcase Session, Johannes Frey, InfAI, presented the Databus Archivo and demonstrated the downloading process with the DBpedia Databus

For further details of the presentations follow the links to the slides.

  • Keynote: {RDF} Data quality assessment – connecting the pieces, by Dimitris Kontokostas, diffbot, US (slides)
  • Overview of DBpedia Organisational Architecture, by Sebastian Hellmann, Julia Holze, Bettina Klimek, Milan Dojchinovski, INFAI / DBpedia Association (slides)
  • Querying and Integrating (Architecture and Construction) Data with DBpedia by Milan Dojchinovski, INFAI/CTU in Prague (slides)
  • The New DBpedia Release Cycle by Marvin Hofer and Milan Dojchinovski, INFAI (slides)
  • Databus Archivo and Downloading with the Databus by Johannes Frey, Fabian Goetz and Milan Dojchinovski, INFAI (slides)

Geospatial Data & DBpedia Session

After the opening session we had the Geospatial Data & DBpedia Session. Milan Dojchinovski (InfAI/CTU in Prague) chaired this session with three very stimulating talks. Hereafter you will find all presentations given during this session:

  • Linked Geospatial Data & Data Quality by Wouter Beek, Triply Ltd. (slides)
  • Contextualizing OSi’s Geospatial Data with DBpedia by Christophe Debruyne, Vrije Universiteit Brussel and ADAPT at Trinity College Dublin
  • Linked Spatial Data: Beyond The Linked Open Data Cloud by Chaidir A. Adlan, The Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (slides)

Data Quality & DBpedia Session

The first online DBpedia workshop also covered a special data quality session. Johannes Frey (InfAI) chaired this session with three very stimulating talks. Hereafter you will find all presentations given during this session:

  • SeMantic AnsweR Type prediction with DBpedia – ISWC 2020 Challenge by Nandana Mihindukulasooriya, MIT-IBM Watson AI Lab (slides)
  • RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data by Ahmad Hemid, Fraunhofer IAIS (slides)
  • The Best of Both Worlds: Unlocking the Power of (big) Knowledge Graphs with SANSA by Gezim Sejdiu,  Deutsche Post DHL Group and University of Bonn (slides)
  • Closing words by the workshop organizers

In case you missed the event, all slides and presentations are also available on the DBpeda workshop website. Further insights, feedback and photos about the event are available on Twitter (#DBpediaDay hashtag).

We are now looking forward to our first DBpedia Stack tutorial, which will be held online on July 1st, 2020. Over the last year, the DBpedia core team has consolidated a great amount of technology around DBpedia. The tutorial primarily targets 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 will also be the new DBpedia Stack, which contains several dockerized applications that are automatically loading data from the Databus. Attending the DBpedia Stack tutorial is free and will be organized online. Please register to be part of the meeting.

Stay tuned and check Twitter, Facebook and our Website or subscribe to our Newsletter for latest news and information.

Julia and Milan 

on behalf of the DBpedia Association

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GSoC2020 – Call for Contribution

Tuesday, March 10, 2020 - 3:48pm

James: Sherry with the soup, yes… Oh, by the way, the same procedure as last year, Miss Sophie?

Miss Sophie: Same procedure as every year, James.

…and we are proud of it. We are very grateful to be accepted as an open-source organization in this years’  Google Summer of Code (GSoC2020) edition, again. The upcoming GSoC2020 marks the 16th consecutive year of the program and is the 9th year in a row for DBpedia. 

We did it again – We are mentoring organization!

What is GSoC2020? 

Google Summer of Code is a global program focused on bringing student developers into open source software development. Funds will be given to students (BSc, MSc, PhD.) to work for three months on a specific task. For GSoC-Newbies, this short video and the information provided on their website will explain all there is to know about GSoC2020.

This year’s Narrative

Last year we tried to increase female participation in the program and we will continue to do so this year. We want to encourage explicitly female students to apply for our projects. That being said, we already engaged excellent female mentors to also raise the female percentage in our mentor team. 

In the following weeks, we invite all students, female and male alike, who are interested in Semantic Web and Open Source development to apply for our projects. You can also contribute your own ideas to work on during the summer. 

And this is how it works: 4 steps to GSoC2020 stardom

  1. Open source organizations such as DBpedia announce their projects ideas. You can find our project here
  2. Students contact the mentor organizations they want to work with and write up a project proposal. Please get in touch with us via the DBpedia Forum or dbpedia@infai.org as soon as possible.
  3. The official application period at GSoC starts March, 16th. Please note, you have to submit your final application not through our Forum, but the GSoC Website
  4. After a selection phase, students are matched with a specific project and a set of mentors to work on the project during the summer.

To all the smart brains out there, if you are a student who wants to work with us during summer 2020, check our list of project ideas, warm-up tasks or come up with your own idea and get in touch with us.

Application Procedure

Further information on the application procedure is available in our DBpedia Guidelines. There you will find information on how to contact us and how to appropriately apply for GSoC2020. Please also note the official GSoC 2020 timeline for your proposal submission and make sure to submit on time.  Unfortunately, extensions cannot be granted. Final submission deadline is March 31st, 2020, 8 pm, CEST.

Finally, check our website for information on DBpedia, follow us on Twitter or subscribe to our newsletter.

And in case you still have questions, please do not hesitate to contact us via praetor@infai.org.

We are thrilled to meet you and your ideas.

Your DBpedia-GSoC-Team


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ImageSnippets and DBpedia

Wednesday, December 18, 2019 - 1:15pm

 by Margaret Warren 

The following post introduces to you ImageSnippets and how this tool profits from the use of DBpedia.

ImageSnippets – A Tool for Image Curation

For over two decades, ImageSnippets has been evolving as an ontology and data-driven framework for image annotation research. Representing the informal knowledge people have about the context and provenance of images as RDF/linked data is challenging, but it has also been an enlightening and engaging journey in not only applying formal semantic web theory to building image graphs but also to weave together our interests with what others have been doing in the field of semantic annotation and knowledge graph building over these many years. 

DBpedia provides the entities for our RDF descriptions

Since the beginning, we have always made use of DBpedia and other publicly available datasets to provide the entities for use in our RDF descriptions.  Though ImageSnippets can be used to build special vocabularies around niche domains, our primary research is around relation ontology building and we prefer to avoid the creation of new entities unless we absolutely can not find them through any other service.

When we first went live with our basic system in 2013, we began hand-building tens of thousands of triples using terms primarily from DBpedia (the core of the linked data cloud.) While there would often be an overlap of terms with other datasets – almost a case of too many choices – we formed a best practice of preferentially using DBpedia terms as often as possible, because they gave us the most utility for reasoning using the SKOS concepts built into the DBpedia service. We have also made extensive use of DBpedia Spotlight for named-entity extraction.

How to combine DBpedia & Wikidata and make it useful for ImageSnippets

But the addition of the Wikidata Query Service over the past 18 months or so has now given us an even more unique challenge: how to work with both! Since DBpedia and Wikidata both have class relationships that we can reason from, we found ourselves in a position to be able to examine both DBpedia and Wikidata in concert with each other through the use of mapping techniques between the two datasets.

How it works: ImageSnippets & DBpedia

When an image is saved, we build inference graphs over results from both DBpedia and Wikidata. These graphs can be revealed with simple SPARQL queries at our endpoint and queries from subclasses, taxons and SKOS concepts can find image results in our custom search tool.  We have also just recently added a pathfinder utility – highly useful for semantic explainability as it will return the precise path of connections from an originating source entity to the target entity that was used in our custom image search.

Sometimes a query will produce very unintuitive results, and the pathfinder tool enables us to quickly locate semantic errors which lead to clearly erroneous misclassifications (for example, a search for the Wikidata subclass of ‘communication medium’ reveals images of restaurants and hotels because of misclassifications in Wikidata.) In this way we can quickly troubleshoot the results of queries, using the images as visual cues to explore the accuracy of the semantic modelling in both datasets.


We are very excited with the new directions that we feel can come of our knitting together of the two knowledge graphs through the use of our visual interface and believe there is a great potential for ImageSnippets to serve a more complex role in cleaning and aligning the two datasets, using the images as our guides.

A big thank you to Margaret Warren for providing some insights into her work at ImageSnippets.

Yours,

DBpedia Association

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New Prototype: Databus Collection Feature

Thursday, November 14, 2019 - 1:39pm

We are thrilled to announce that our Databus Collection Feature for the DBpedia Databus has been developed and is now available as a prototype. It simplifies the way to bundle your data and use it in your application.

A new Databus Collection Feature? How come, and how does it work? Read below and find out how using the DBpedia Databus becomes easier by the day and with each new tool.

Motivation

With more and more data being uploaded to the databus we started to develop test applications using that data. The SPARQL endpoint offers a central hub to access all metadata for datasets uploaded to the databus provided you know how to write SPARQL queries. The metadata includes the download links of the data files – it was, therefore, possible to pass a SPARQL query to an application, download the actual data and then use for whatever purpose the app had.

The Databus Collection Editor

The DBpedia Databus now provides an editor for collections. A collection is basically a labelled SPARQL query that is retrievable via URI. Hence, with the collection editor you can group Databus groups and artifacts into a bundle and publish your selection using your Databus account. It is now a breeze to select the data you need, share the exact selection with others and/or use it in existing or self-made applications.

If you are not familiar with SPARQL and data queries, you can think of the feature as a shopping cart for data: You create a new cart, put data in it and tell your friends or applications where to find it. Quite neat, right?

In the following section, we will cover the user interface of the collection editor.

The Editor UI

Firstly, you can find the collection editor by going to the DBpedia Databus and following the Collections link at the top or you can get there directly by clicking here.

What you will see is the following:

General Collection Info

Secondly, since you do not have any collections yet, the editor has already created an empty collection named “Unnamed” for you. At the right side next to the label and description you will find a pen icon. By clicking the icon or the label itself you can edit its content. The collection is not published yet, so the Collection URI is blank.

Whenever you are not logged in or the collection has not been published yet, the editor will also notify you that your changes are only saved in your local browser cache and NOT remotely on our server. Keep that in mind when clearing your cache. Publishing the collection however is easy: Simply log into (or create) your Databus account and hit the publish button in the action bar. This will open up a modal where you can pick your unique collection id and hit publish again. That’s it!

The Collection Info section will now show the collection URI. Following the link will take you to the HTML representation of your collection that will be visible to others. Hitting the Edit button in the action bar will bring you back to the editor.

Collection Hierarchy

Let’s have a look at the core piece of the collection editor: the hierarchy view. A collection can be a bundle of different Databus groups and artifacts but is not limited to that. If you know how to write a SPARQL query, you can easily extend your collection with more powerful selections. Therefore, the hierarchy is split into two nodes:

  • Generated Queries: Contains all queries that are generated from your selection in the UI
  • Custom Queries: Contains all custom written SPARQL queries

Both, hierarchy nodes have a “+” icon. Clicking on this button will let you add generated or custom queries respectively.

Custom Queries

If you hit the “+” icon on the Custom Queries node, a new node called “Custom Query” will appear in the hierarchy. You can remove a custom query by clicking on the trashcan icon in the hierarchy. If you click the node it will take you to a SPARQL input field where you can edit the query.

To make your collection more understandable for others, you can even document the query by adding a label and description.

Writing Your Own Custom Queries

A collection query is a SPARQL query of the form:

SELECT DISTINCT ?file WHERE {
    {
        [SUBQUERY]
    }
    UNION
    {
        [SUBQUERY]
    }
    UNION
    ...
    UNION
    {
        [SUBQUERY]
    }
}

All selections made by generated and custom queries will be joined into a single result set with a single column called “file“. Thus it is important that your custom query binds data to a variable called “file” as well.

Generated Queries

Clicking the “+” icon on the Generated Queries node will take you to a search field. Make use of the indexed search on the Databus to find and add the groups and artifacts you need. If you want to refine your search, don’t worry: you can do that in the next step!

Once the artifact or group has been added to your collection, the Add to Collection button will turn green. Once you are done you can go back to the Editor with Back to Hierarchy button.

Your hierarchy will now contain several new nodes.

Group Facets, Artifact Facets and Overrides

Group and artifacts that have been added to the collection will show up as nodes in the hierarchy. Clicking a node will open a filter where you can refine your dataset selection. Setting a filter to a group node will apply it to all artifact nodes unless you override that setting in any artifact node manually. The filter set in the group node is shown in the artifact facets in dark grey. Any overrides in the artifact facets will be highlighted in green:

Group Nodes

A group node will provide a list of filters that will be applied to all artifacts of that group:

Artifact Nodes

Artifact nodes will then actually select data files which will be visible in the faceted view. The facets are generated dynamically from the available variants declared in the metadata.

Example: Here we selected the latest version of the databus dump as n-triple. This collection is already in use: The collection URI is passed to the new generic lookup application, which then creates the search function for the databus website. If you are interested in how to configure the lookup application, you can go here: https://github.com/dbpedia/lookup-application. Additionally, there will also be another blog post about the lookup within the next few weeks

Use Cases

The DBpedia Databus Collections are useful in many ways.

  • You can share a specific dataset with your community or colleagues.
  • You can re-use dataset others created
  • You can plug collections into databus-ready applications and avoid spending time on the download and setup process
  • You can point to a specific piece of data (e.g. for testing) with a single URI in your publications
  • You can help others to create data queries more easily

We hope you enjoy the Databus Collection Feature and we would love to hear your feedback! You can leave your thoughts and suggestions in the new DBpedia Forum. Feedback of any kinds is highly appreciated since we want to improve the prototype as fast and user-driven as possible! Cheers!

A big thanks goes to DBpedia developer Jan Forberg who finalized the Databus Collection Feature and compiled this text.

Yours

DBpedia Association

The post New Prototype: Databus Collection Feature appeared first on DBpedia Blog.

Better late than never – GSOC 2019 recap & outlook GSoC 2020

Friday, November 8, 2019 - 12:57pm

  • Pinky: Gee, Brain, what are we gonna do this year?
  • Brain: The same thing we do every year, Pinky. Taking over GSoC.

And, this is exactly what we did. We had been accepted as one of 206 open source organizations to participate in Google Summer of Code (GSoC) again. More than 25 students followed our call for project ideas. In the end, we chose six amazing students and their project proposals to work with during summer 2019. 
In the following post, we will show you some insights into the project ideas and how they turned out. Additionally, we will shed some light onto our amazing team of mentors who devoted a lot of time and expertise in mentoring our students. 

Meet the students and their projects

A Neural QA Model for DBpedia by Anand Panchbhai

With booming amount of information being continuously added to the internet, organising the facts and serving this information to the users becomes a very difficult task. Currently, DBpedia hosts billions of data points and corresponding relations in the RDF format. Accessing data on DBpedia via a SPARQL query is difficult for amateur users, who do not know how to write a query. This project tried to make this humongous linked data available to a larger user base in their natural languages (now restricted to English). The primary objective of the project was to translate natural language questions to a valid SPARQL query. Click here if you want to check his final code.

Multilingual Neural RDF Verbalizer for DBpedia by Dwaraknath Gnaneshwar

Presently, the generation of Natural Language from RDF data has gained substantial attention and has also been proven to support the creation of Natural Language Generation benchmarks. However, most models are aimed at generating coherent sentences in English, while other languages have enjoyed comparatively less attention from researchers. RDF data is usually in the form of triples, <subject, predicate, object>. Subject denotes the resource, the predicate denotes traits or aspects of the resource and expresses the relationship between subject and object. In this project, we aimed to create a multilingual Neural Verbalizer, ie, generating high-quality natural-language text from sets of RDF triples in multiple languages using one stand-alone, end-to-end trainable model. You can follow up on the progress and outcome of the project here. 

Predicate Detection using Word Embeddings for Question Answering over Linked Data by Yajing Bian

Knowledge-based question-answering system (KBQA) has demonstrated an ability to generate answers to natural language from information stored in a large-scale knowledge base. Generally, it completes the analysis challenge via three steps: identifying named entities, detecting predicates and generate SPARQL queries. In these three steps, predicate detection identifies the KB relation(s) a question refers to. To build a predicate detection structure, we identified all possible named entity first, then collected all predicates corresponding to the above entities. What follows is to calculate the similarity between problem and candidate predicates using a multi-granularity neural network model (MGNN). To find the globally optimal entity-predicate assignment, we use a joint model which is based on the result of entity linking and predicate detection process rather than considering the local predictions (i.e. most possible entity or predicate) as the final result. More details on the project are available here

A tool to generate RDF triples from DBpedia abstract by  Jayakrishna Sahit

The main aim of this project was to research and develop a tool in order to generate highly trustable RDF triples from DBpedia abstracts. In order to develop such a tool, we implemented algorithms which would take the output generated from the syntactic analyzer along with DBpedia spotlight’s named entity identifiers. Further information and the project’s results can be found here

A transformer of Attention Mechanism for Long-context QA by Stuart Chan

In this GSoC project, I choose to employ the language model of the transformer with an attention mechanism to automatically discover query templates for the neural question-answering knowledge-based model. The ultimate goal was to train the attention-based NSpM model on DBpedia with its evaluation against the QALD benchmark. Check here for more details on the project.

Workflow for linking External datasets by Jaydeep Chakraborty

The requirement of the project was to create a workflow for entity linking between DBpedia and external data sets. We aimed at an approach for ontology alignment through the use of an unsupervised mixed neural network. We explored reading and parsing the ontology and extracted all necessary information about concepts and instances. Additionally, we generated semantic vectors for each entity with different meta information like entity hierarchy, object property, data property, and restrictions and designed a User Interface based system which showed all necessary information about the workflow. Further info, download details and project results are available here

Meet our Mentors

First of all, a big shout out and thank you to all mentors and co-mentors who helped our students to succeed in their endeavours.

  • Aman Mehta, former GSoC student and current junior mentor, recently interned as a software engineer at Facebook, London.
  • Beyza Yaman, a senior mentor and organizational admin, Post-Doctoral Researcher based in ADAPT, Dublin City University, former Springer Nature-DBpedia intern and former research associate at the InfAI/University of Leipzig. She is responsible for the Turkish DBpedia and her field of interests are information retrieval, data extraction and integration over Linked Data.
  • Tommaso Soru, senior mentor and organizational admin. I’m a Machine Learning & AI enthusiast, Data Scientist at Data Lens Ltd in London and a PhD candidate at the University of Leipzig. 

“DBpedia is my window to the world of semantic data, not only for its intuitive interface but also because its knowledge is organised in a simple and uncomplicated way”

Tommaso Soru, GSoC 2019
  • Amandeep Srivastava, Junior Mentor and analyst at Goldman Sachs. He’s a huge fan of Christopher Nolan and likes to read fiction books in his free time.
  • Diego Moussalem, Senior mentor, Senior Researcher at Paderborn University, an active and vital member of the Portuguese DBpedia Chapter
  • Luca Virgili, currently a Computer Science PhD student at the Polytechnic University of Marche.He was a GSoC student for a year and a GSoC mentor for 2 years in DBpedia. 
  • Bharat Suri, former GSOC student, Junior Mentor, Masters degree in Computer Science at The Ohio State University

“I have thoroughly enjoyed both my years of GSoC with DBpedia and I plan to stay and help out in whichever way I can”

Bharat Suri, GSoC 2019
  • Mariano Rico, senior mentor,  Senior Doctor Researcher at Ontology Engineering Group, Universidad Politécnica de Madrid.
  • Nausheen Fatma, senior mentor, Data Scientist, Natural Language Processing, Machine Learning at Info Edge (naukri.com).
  • Ram G Athreya long-term GSoC mentor, Research Engineer at Viv Labs, Bay Area, San Francisco. 
  • Ricardo Usbeck, team leader ‘Conversational AI and Knowledge Graphs’ at Fraunhofer IAIS.
  • Rricha Jalota, former GSoC students, current senior mentor, developer in the Data Science Group at University of Paderborn, Germany 

“The reason why I love collaborating with DBpedia (apart from the fact that, it’s a powerhouse of knowledge-driven applications) is not only it gave me my first big break to the amazing field of NLP but also to the world of open-source!”

Rricha Jalota, GSoC 2019

In addition, we also like to thank the rest of our mentor team namely, Thiago Castro Ferreira, Aashay Singhal and Krishanu Konar, former GSoC student and current senior mentor, for their great work.  

Mentor Summit Recap 

This GSoC marked the 15th consecutive year of the program and was the 8th season in a row for DBpedia. As usual in each year we had two of our mentors, Rricha Jalota and Aashay Singhal joining the annual GSoC mentor summit. Selected mentors get the chance to meet each other and engage in a vital knowledge and expertise exchange around various GSoC related and non-related topics. Apart from more entertaining activities such as games, a scavenger hunt and a guided trip through Munich mentors also discussed pressing questions such as “why is it important to fail your students” or “how can we have our GSoC students stay and contribute for long”.

After GSoC is before the next GSoC

If you are interested in either mentoring a DBpedia GSoC project or if you want to contribute to a project of your own we are happy to have you on board. There are a few things to get you started.

Likewise, if you are an ambitious student who is interested in open source development and working with DBpedia you are more than welcome to either contribute your own project idea or apply for project ideas we offer starting in early 2020.

Stay tuned, frequently check Twitter or the DBpedia Forum to stay in touch and don’t miss your chance of becoming a crucial force in this endeavour as well as a vital member of the DBpedia community.

See you soon,

yours

DBpedia Association

The post Better late than never – GSOC 2019 recap & outlook GSoC 2020 appeared first on DBpedia Blog.

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