GSoC2020 – Call for Contribution

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


ImageSnippets and DBpedia

 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

New Prototype: Databus Collection Feature

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

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

  • 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

GlobalFactSync and WikiDataCon2019

We will be spending the next three days in Berlin at WikidataCon 2019, the conference for open data enthusiasts. From October 24th till 26th we will be presenting the latest developments and first results of our work in the GlobalFactSyncRE-Project. 

Short Project Intro

Funded by the Wikimedia Foundation, the project started in June 2019 and has two goals:

  • Answer the following questions:
    • How is data edited in Wikipedia and Wikidata?
    • Where does it come from?
    • How can we synchronize it globally?
  • Build an information system to synchronize facts between all Wikipedia language-editions, Wikidata, DBpedia and eventually multiple external sources, while also providing respective references. 

In order to help Wikipedians to maintain their infoboxes, check for factual correctness, and also improve data in Wikidata, we use data from Wikipedia infoboxes of different languages, Wikidata, and DBpedia and fuse them into our PreFusion dataset (in JSON-LD). More information on the fusion process, which is the engine behind GFS, can be found in the FlexiFusion paper.

Can’t join the conference or want to find out more about GlobalFactSync?

No problem, the poster we are presenting at the conference is currently available here and will soon be available here. Additionally, why not go through our project timeline, follow up on our progress so far and find out what’s coming up next.

In case you have specific questions regarding GlobalfactSync or even some helpful feedback just ping us via dbpedia@infai.org. We also have our new DBpedia Forum, home to the DBpedia Comunity, which just waits for you to initialize a discussion around GlobalFactSync. Why not start it now?

For general DBpedia news and updates follow us on Twitter.

…And if you are in Berlin at WikiDataCon2019 stop by our poster and talk to our developers. They are looking forward to vital exchanges with you.

All the best

yours,


DBpedia Association


One Billion derived Knowledge Graphs

… by and for Consumers until 2025

One Billion – what a mission! We are proud to announce that the DBpedia Databus website at https://databus.dbpedia.org and the SPARQL API at https://databus.dbpedia.org/(repo/sparql|yasgui) (docu) are in public beta now!

The system is usable (eat-your-own-dog-food tested) following a “working software over comprehensive documentation” approach. Due to its many components (website, SPARQL endpoints, keycloak, mods, upload client, download client, and data debugging), we estimate approximately six months in beta to fix bugs, implement all features and improve the details.

But, let’s start from the beginning

The DBpedia Databus is a platform to capture invested effort by data consumers who needed better data quality (fitness for use) in order to use the data and give improvements back to the data source and other consumers. DBpedia Databus enables anybody to build an automated DBpedia-style extraction, mapping and testing for any data they need. Databus incorporates features from DNS, Git, RSS, online forums and Maven to harness the full work power of data consumers. Vision

Our vision

Professional consumers of data worldwide have already built stable cleaning and refinement chains for all available datasets, but their efforts are invisible and not reusable. Deep, cleaned data silos exist beyond the reach of publishers and other consumers trapped locally in pipelines. Data is not oil that flows out of inflexible pipelines. Databus breaks existing pipelines into individual components that together form a decentralized, but centrally coordinated data network. In this set-up, data can flow back to previous components, the original sources, or end up being consumed by external components.

One Billion interconnected, quality-controlled Knowledge Graphs until 2025

The Databus provides a platform for re-publishing these files with very little effort (leaving file traffic as only cost factor) while offering the full benefits of built-in system features such as automated publication, structured querying, automatic ingestion, as well as pluggable automated analysis, data testing via continuous integration, and automated application deployment (software with data). The impact is highly synergistic. Just a few thousand professional consumers and research projects can expose millions of cleaned datasets, which are on par with what has long existed in deep silos and pipelines.

To a data consumer network

As we are inverting the paradigm form a publisher-centric view to a data consumer network, we will open the download valve to enable discovery and access to massive amounts of cleaner data than published by the original source. The main DBpedia Knowledge Graph alone has 600k file downloads per year complemented by downloads at over 20 chapters, e.g. http://es.dbpedia.org as well as over 8 million daily hits on the main Virtuoso endpoint.

Community extension from the alpha phase such as DBkWik, LinkedHypernyms are being loaded onto the bus and consolidated. We expect this number to reach over 100 by the end of the year. Companies and organisations who have previously uploaded their backlinks here will be able to migrate to the databus. Other datasets are cleaned and posted. In two of our research projects LOD-GEOSS and PLASS, we will re-publish open datasets, clean them and create collections, which will result in DBpedia-style knowledge graphs for energy systems and supply-chain management.

A new era for decentralized collaboration on data quality

DBpedia was established around producing a queryable knowledge graph derived from Wikipedia content that’s able to answer questions like “What have Innsbruck and Leipzig in common?” A community and consumer network quickly formed around this highly useful data, resulting in a large, well-structured, open knowledge graph that seeded the Linked Open Data Cloud — which is the largest knowledge graph on earth. The main lesson learned after these 13 years is that current data “copy” or “download” processes are inefficient by a magnitude that can only be grasped from a global perspective. Consumers spend tremendous effort fixing errors on the client-side. If one unparseable line needs 15 minutes to find and fix, we are talking about 104 days of work for 10,000 downloads. Providers – on the other hand – will never have the resources to fix the last error as cost increases exponentially (20/80 rule). 

One billion knowledge graphs in mind – the progress so far

Discarding faulty data often means that a substitute source has to be found, which is hours of research and might lead to similar problems. From the dozens of DBpedia Community meetings we held we can summarize that for each clean-up procedure, data transformation, linkset or schema mapping that a consumer creates client-side, dozens of consumers have invested the same effort client-side before him and none of it reaches the source or other consumers with the same problem. Holding the community meetings just showed us the tip of the iceberg. 

As a foundation, we implemented a mappings wiki that allowed consumers to improve data quality centrally. A next advancement was the creation of the SHACL standard by our former CTO and board member Dimitris Kontokostas. SHACL allows consumers to specify repeatable tests on graph structures and datatypes, which is an effective way to systematically assess data quality. We established the DBpedia Databus as a central platform to better capture decentrally created, client-side value by consumers.

It is an open system, therefore value that is captured flows right back to everybody.  

The full document “DBpedia’s Databus and strategic initiative to facilitate “One Billion derived Knowledge Graphs by and for Consumers” until 2025 is available here.  

If you have any feedback or questions, please use the DBpedia Forum, the “report issues” button, or dbpedia@infai.org.

Yours,

DBpedia Association

More than 50 DBpedia enthusiasts joined the Community Meeting in Karlsruhe.

SEMANTiCS is THE leading European conference in the field of semantic technologies and the platform for professionals who make semantic computing work, and understand its benefits and know its limitations.

Since we at DBpedia have a long-standing partnership with Semantics we also joined this year’s event in Karlsruhe. September 12, the last day of the conference was dedicated to the DBpedia community. 

First and foremost, we would like to thank the Institute for Applied Informatics for supporting our community and many thanks to FIZ Karlsruhe for hosting our community meeting.

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

Opening Session

Katja Hose – “Querying the web of data”

….on the search for the killer App.

The concept of Linked Open Data and the promise of the Web of Data have been around for over a decade now. Yet, the great potential of free access to a broad range of data that these technologies offer has not yet been fully exploited. This talk will, therefore review the current state of the art, highlight the main challenges from a query processing perspective, and sketch potential ways on how to solve them. Slides are available here.

Dan Weitzner – “timbr-DBpedia – Exploration and Query of DBpedia in SQL

The timbr SQL Semantic Knowledge Platform enables the creation of virtual knowledge graphs in SQL. The DBpedia version of timbr supports query of DBpedia in SQL and seamless integration of DBpedia data into data warehouses and data lakes. We already published a detailed blogpost about timbr where you can find all relevant information about this amazing new DBpedia Service.

Showcase Session

Maribel Acosta“A closer look at the changing dynamics of DBpedia mappings”

Her presentation looked at the mappings wiki and how different language chapters use and edit it. Slides are available here.

Mariano Rico“Polishing a diamond: techniques and results to enhance the quality of DBpedia data”

DBpedia is more than a source for creating papers. It is also being used by companies as a remarkable data source. This talk is focused on how we can detect errors and how to improve the data, from the perspective of academic researchers and but also on private companies. We show the case for the Spanish DBpedia (the second DBpedia in size after the English chapter) through a set of techniques, paying attention to results and further work. Slides are available here.

Guillermo Vega-Gorgojo – “Clover Quiz: exploiting DBpedia to create a mobile trivia game”

Clover Quiz is a turn-based multiplayer trivia game for Android devices with more than 200K multiple choice questions (in English and Spanish) about different domains generated out of DBpedia. Questions are created off-line through a data extraction pipeline and a versatile template-based mechanism. A back-end server manages the question set and the associated images, while a mobile app has been developed and released in Google Play. The game is available free of charge and has been downloaded by +10K users, answering more than 1M questions. Therefore, Clover Quiz demonstrates the advantages of semantic technologies for collecting data and automating the generation of multiple-choice questions in a scalable way. Slides are available here.

Fabian Hoppe and Tabea Tiez – “The Return of German DBpedia”

Fabian and Tabea will present the latest news on the German DBpedia chapter as it returns to the language chapter family after an extended offline period. They will talk about the data set, discuss a few challenges along the way and give insights into future perspectives of the German chapter. Slides are available here.

Wlodzimierz Lewoniewski and Krzysztof Węcel  – “References extraction from Wikipedia infoboxes”

In Wikipedia’s infoboxes, some facts have references, which can be useful for checking the reliability of the provided data. We present challenges and methods connected with the metadata extraction of Wikipedia’s sources. We used DBpedia Extraction Framework along with own extensions in Python to provide statistics about citations in 10 language versions. Provided methods can be used to verify and synchronize facts depending on the quality assessment of sources. Slides are available here.

Wlodzimierz Lewoniewski – “References extraction from Wikipedia infoboxes” … He gave insight into the process of extracting references for Wikipedia infoboxes, which we will use in our GFS project.

Afternoon Session

Sebastian Hellmann, Johannes Frey, Marvin Hofer – “The DBpedia Databus – How to build a DBpedia for each of your Use Cases”

The DBpedia Databus is a platform that is intended for data consumers. It will enable users to build an automated DBpedia-style Knowledge Graph for any data they need. The big benefit is that users not only have access to data, but are also encouraged to apply improvements and, therefore, will enhance the data source and benefit other consumers. We want to use this session to officially introduce the Databus, which is currently in beta and demonstrate its power as a central platform that captures decentrally created client-side value by consumers.  

We will give insight on how the new monthly DBpedia releases are built and validated to copy and adapt for your use cases. Slides are available here.

Interactive session, moderator: Sebastian Hellmann – “DBpedia Connect & DBpedia Commerce – Discussing the new Strategy of DBpedia”

In order to keep growing and improving, DBpedia has been undergoing a growth hack for the last couple of months. As part of this process, we developed two new subdivisions of DBpedia: DBpedia Connect and DBpedia Commerce. The former is a low-code platform to interconnect your public or private databus data with the unified, global DBpedia graph and export the interconnected and enriched knowledge graph into your infrastructure. DBpedia Commerce is an access and payment platform to transform Linked Data into a networked data economy. It will allow DBpedia to offer any data, mod, application or service on the market. During this session, we will provide more insight into these as well as an overview of how DBpedia users can best utilize them. Slides are available here.

In case you missed the event, all slides and presentations are also available on our Website. Further insights, feedback and photos about the event are available on Twitter via #DBpediaDay

We are now looking forward to more DBpedia meetings next year. So, stay tuned and check Twitter, Facebook and the Website or subscribe to our Newsletter for the latest news and information.

If you want to organize a DBpedia Community meeting yourself, just get in touch with us via dbpedia@infai.org regarding program and organization.

Yours

DBpedia Association

SEMANTiCS 2019 Interview: Katja Hose

Today’s post features an interview with our DBpedia Day keynote speaker Katja Hose, a Professor of Computer Science at Aalborg University, Denmark. In this Interview, Katja talks about increasing the reliability of Knowledge Graph Access as well as her expectations for SEMANTiCS 2019

Prior to joining Aalborg University, Katja was a postdoc at the Max Planck Institute for Informatics in Saarbrücken. She received her doctoral degree in Computer Science from Ilmenau University of Technology in Germany.

Can you tell us something about your research focus?

The most important focus of my research has been querying the Web of Data, in particular, efficient query processing over distributed knowledge graphs and Linked Data. This includes indexing, source selection, and efficient query execution. Unfortunately, it happens all too often that the services needed to access remote knowledge graphs are temporarily not available, for instance, because a software component crashed. Hence, we are currently developing a decentralized architecture for knowledge sharing that will make access to knowledge graphs a reliable service, which I believe is the key to a wider acceptance and usage of this technology.

How do you personally contribute to the advancement of semantic technologies?

I contribute by doing research, advancing the state of the art, and applying semantic technologies to practical use cases.  The most important achievements so far have been our works on indexing and federated query processing, and we have only recently published our first work on a decentralized architecture for sharing and querying semantic data. I have also been using semantic technologies in other contexts, such as data warehousing, fact-checking, sustainability assessment, and rule mining over knowledge bases.

Overall, I believe the greatest ideas and advancements come when trying to apply semantic technologies to real-world use cases and problems, and that is what I will keep on doing.

Which trends and challenges do you see for linked data and the semantic web?

The goal and the idea behind Linked Data and the Semantic Web is the second-best invention after the Internet. But unlike the Internet, Linked Data and the Semantic Web are only slowly being adopted by a broader community and by industry.

I think part of the reason is that from a company’s point of view, there are not many incentives and added benefit of broadly sharing the achievements. Some companies are simply reluctant to openly share their results and experiences in the hope of retaining an advantage over their competitors. I believe that if these success stories were shared more openly, and this is the trend we are witnessing right now, more companies will see the potential for their own problems and find new exciting use cases.

Another particular challenge, which we will have to overcome, is that it is currently still far too difficult to obtain and maintain an overview of what data is available and formulate a query as a non-expert in SPARQL and the particular domain… and of course, there is the challenge that accessing these datasets is not always reliable.

As artificial intelligence becomes more and more important, what is your vision of AI?

AI and machine learning are indeed becoming more and more important. I do believe that these technologies will bring us a huge step ahead. The process has already begun. But we also need to be aware that we are currently in the middle of a big hype where everybody wants to use AI and machine learning – although many people actually do not truly understand what it is and if it is actually the best solution to their problems. It reminds me a bit of the old saying “if the only tool you have is a hammer, then every problem looks like a nail”. Only time will tell us which problems truly require machine learning, and I am very curious to find out which solutions will prevail.

However, the current state of the art is still very far away from the AI systems that we all know from Science Fiction. Existing systems operate like black boxes on well-defined problems and lack true intelligence and understanding of the meaning of the data. I believe that the key to making these systems trustworthy and truly intelligent will be their ability to explain their decisions and their interpretation of the data in a transparent way.

What are your expectations about Semantics 2019 in Karlsruhe?

First and foremost, I am looking forward to meeting a broad range of people interested in semantic technologies. In particular, I would like to get in touch with industry-based research and to be exposed 

The End

We like to thank Katje Hose for her insights and are happy to have her as one of our keynote speakers.

Visit SEMANTiCS 2019 in Karlsruhe, Sep 9-12 and get your tickets for our community meeting here. We are looking forward to meeting you during DBpedia Day.

Yours DBpedia Association

SEMANTiCS Interview: Dan Weitzner

As the upcoming 14th DBpedia Community Meeting, co-located with SEMANTiCS 2019 in Karlsruhe, Sep 9-12, is drawing nearer, we like to take that opportunity to introduce you to our DBpedia keynote speakers.

Today’s post features an interview with Dan Weitzner from WPSemantix who talks about timbr-DBpedia, which we blogged about recently, as well as future trends and challenges of linked data and the semantic web.

Dan Weitzner is co-founder and Vice President of Research and Development of WPSemantix. He obtained his Bachelor of Science in Computer Science from Florida Atlantic University. In collaboration with DBpedia, he and his colleagues at WPSemantix launched timbr, the first SQL Semantic Knowledge Graph that integrates Wikipedia and Wikidata Knowledge into SQL engines.

Dan Weitzner

1. Can you tell us something about your research focus?

WPSemantix bridges the worlds of standard databases and the Semantic Web by creating ontologies accessible in standard SQL. 

Our platform – timbr is a virtual knowledge graph that maps existing data-sources to abstract concepts, accessible directly in all the popular Business Intelligence (BI) tools and also natively integrated into Apache Spark, R, Python, Java and Scala. 

timbr enables reasoning and inference for complex analytics without the need for costly Extract-Transform-Load (ETL) processes to graph databases.

2. How do you personally contribute to the advancement of semantic technologies?

We believe we have lowered the fundamental barriers to adoption of semantic technologies for large organizations who want to benefit from knowledge graph capabilities without firstly requiring fundamental changes in their database infrastructure and secondly, without requiring expensive organizational changes or significant personnel retraining.  

Additionally, we implemented the W3C Semantic Web principles to enable inference and inheritance between concepts in SQL, and to allow seamless integration of existing ontologies from OWL. Subsequently, users across organizations can do complex analytics using the same tools that they currently use to access and query their databases, and in addition, to facilitate the sophisticated query of big data without requiring highly technical expertise.  
timbr-DBpedia is one example of what can be achieved with our technology. This joint effort with the DBpedia Association allows semantic SQL query of the DBpedia knowledge graph, and the semantic integration of the DBpedia knowledge into data warehouses and data lakes. Finally, timbr-DBpedia allows organizations to benefit from enriching their data with DBpedia knowledge, combining it with machine learning and/or accessing it directly from their favourite BI tools.

3. Which trends and challenges do you see for linked data and the semantic web?

Currently, the use of semantic technologies for data exploration and data integration is a significant trend followed by data-driven communities. It allows companies to leverage the relationship-rich data to find meaningful insights into their data. 

One of the big difficulties for the average developer and business intelligence analyst is the challenge to learn semantic technologies. Another one is to create ontologies that are flexible and easily maintained. We aim to solve both challenges with timbr.

4. Which application areas for semantic technologies do you perceive as most promising?

I think semantic technologies will bloom in applications that require data integration and contextualization for machine learning models.

Ontology-based integration seems very promising by enabling accurate interpretation of data from multiple sources through the explicit definition of terms and relationships – particularly in big data systems,  where ontologies could bring consistency, expressivity and abstraction capabilities to the massive volumes of data.

5. As artificial intelligence becomes more and more important, what is your vision of AI?

I envision knowledge-based business intelligence and contextualized machine learning models. This will be the bedrock of cognitive computing as any analysis will be semantically enriched with human knowledge and statistical models.

This will bring analysts and data scientists to the next level of AI.

6. What are your expectations about Semantics 2019 in Karlsruhe?

I want to share our vision with the semantic community and I would also like to learn about the challenges, vision and expectations of companies and organizations dealing with semantic technologies. I will present “timbr-DBpedia – Exploration and Query of DBpedia in SQL”

The End

Visit SEMANTiCS 2019 in Karlsruhe, Sep 9-12 and find out more about timbr-DBpedia and all the other new developments at DBpedia. Get your tickets for our community meeting here. We are looking forward to meeting you during DBpedia Day.

Yours DBpedia Association

RDF2NL: Generating Texts from RDF Data

RDF2NL is featured in the following guest post by Diego Moussalem, (Dice Research Group & Portuguese DBpedia Chapter).

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