Category Archives: DBpedia Application

An application published on wiki.dbpedia.org

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


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

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

timbr – the DBpedia SQL Semantic Knowledge Platform

With timbr, WPSemantix and the DBpedia Association launch the first SQL Semantic Knowledge Graph that integrates Wikipedia and Wikidata Knowledge into SQL engines.

In part three of DBpedia’s growth hack blog series, we feature timbr, the latest development at DBpedia in collaboration with WPSemantix. Read on to find out how it works.

timbr – DBpedia SQL Semantic Knowledge Platform

Tel Aviv, Israel and Leipzig, Germany – July 18, 2019 – WP-Semantix (WPS) – the “SQL Knowledge Graph Company™” and DBpedia Association – Institut für Angewandte Informatik e.V., announced today the launch of the timbr-DBpedia SQL Semantic Knowledge Platform, a unique version of WPS’ timbr SQL Semantic Knowledge Graph that integrates timbr-DBpedia ontology, timbr’s ontology explorer/visualizer and timbr’s SQL query service, to provide for the first time semantic access to DBpedia knowledge in SQL and to thus facilitate DBpedia knowledge integration into standard data warehouses and data lakes.

DBpedia

DBpedia is the crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects and publish these as files on the Databus and via online databases. This structured information resembles an open knowledge graph which has been available for everyone on the Web for over a decade. Knowledge graphs are a new kind of databases developed to store knowledge in a machine-readable form, organized as connected, relationship-rich data. After the publication of DBpedia (in parallel to Freebase) 12 years ago, knowledge graphs have become very successful and Google uses a similar approach to create the knowledge cards displayed in search results.

Query the world’s knowledge in standard SQL

Amit Weitzner, founder and CEO at WPS commented: “Knowledge graphs use specialized languages, require resource-intensive, dedicated infrastructure and require costly ETL operations. That is, they did until timbr came along. timbr employs SQL – the most widely known database language, to eliminate the technological barriers to entry for using knowledge graphs and to implement Semantic Web principles to provide knowledge graph functionality in SQL. timbr enables modelling of data as connected, context-enriched concepts with inference and graph traversal capabilities while being queryable in standard SQL, to represent knowledge in data warehouses and data lakes. timbr-DBpedia is our first vertical application and we are very excited by the prospects of our cooperation with the DBpedia team to enable the largest user base to query the world’s knowledge in standard SQL.”

Sebastian Hellmann, executive director of the DBpedia Association commented that:

“our vision of the DBpedia Databus – transforming Linked Data into a networked data economy, is becoming a reality thanks to tools such as timbr-DBpedia which take full advantage of our unique data sets and data architecture. We look forward to working with WPS to also enable access to new data sets as they become available .”

timbr will help to explore the power of semantic technologies

Prof. James Hendler, pioneer and a world-leading authority in Semantic Web technologies and WPS’ advisory board member commented “timbr can be a game-changing solution by enabling the semantic inference capabilities needed in many modelling applications to be done in SQL. This approach will enable many users to get the advantages of semantic AI technologies and data integration without the learning curve of many current systems. By giving more people access to the semantic version of Wikipedia, timbr-DBpedia will definitely contribute to allowing the majority of the market to explore the power of semantic technologies.”

timbr-DBpedia is available as a query service or licensed for use as SaaS or on-premises. See the DBpedia website: wiki.dbpedia.org/timbr.

About WPSemantix

WP-Semantix Ltd. (wpsemantix.com) is the developer of the timbr SQL semantic knowledge platform, a dynamic abstraction layer over relational and non-relational data, facilitating declaration and powerful exploration of semantically rich ontologies using a standard SQL query interface. timbr is natively accessible in Apache Spark, Python, R and SQL to empower data scientists to perform complex analytics and generate sophisticated ML algorithms.  Its JDBC interface provides seamless integration with the most popular business intelligence solutions to make complex analytics accessible to analysts and domain experts across the organization.

WP-Semantix, timbr, “SQL Knowledge Graph”, “SQL Semantic Knowledge Graph” and associated marks and trademarks are registered trademarks of WP Semantix Ltd.

DBpedia is looking forward to this cooperation. Follow us on Twitter for the latest information and stay tuned for part four of our growth hack series. The next post features the GlobalFactSyncRe. Curious? You have to be a little more patient and wait till Thursday, July 25th.

Yours DBpedia Association

Chaudron, chawdron , cauldron and DBpedia

Meet Chaudron

Before getting into the technical details of, did you know the term Chaudron derives from Old French and denotes a large metal cooking pot? The word was used as an alternative form of chawdron which means entrails.  Entrails and cauldron –  a combo that seems quite fitting with Halloween coming along.

And now for something completely different

To begin with, Chaudron is a dataset of more than two million triples. It complements DBpedia with physical measures. The triples are automatically extracted from Wikipedia infoboxes using a pattern-matching and a formal grammar approaches.  This dataset adds triples to the existing DBpedia resources. Additionally, it includes measures on various resources such as chemical elements, railway, people places, aircrafts, dams and many other types of resources.

Chaudron was published on wiki.dbpedia.org and is one of many other projects and applications featuring DBpedia.

Want to find out more about our DBpedia Applications? Why not read about the DBpedia Chatbot, DBpedia Entity or the NLI-Go DBpedia Demo.?

Happy reading & happy Halloween!

Yours DBpedia Association

 

PS: In case you want your DBpedia tool, demo or any kind of application published on our Website and the DBpedia Blog, fill out this form and submit your information.

 

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DBpedia Chapters – Survey Evaluation – Episode Two

Welcome back to part two of the evaluation of the surveys, we conducted with the DBpedia chapters.

Survey Evaluation – Episode Two

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

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

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

Datasets, Services and Applications

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

 

 

 

 

 

 

 

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

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

 

 

 

 

 

 

 

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

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

 

 

 

 

 

 

 

 

 

DBpedia Chapter set-up

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

The hardest technical task for setting up the chapter was:

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

 

Final  words

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

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

We will keep you posted about any updates and news.

Yours

DBpedia Association

Beta-Test Updates

While everyone at the DBpedia Association was preparing for the SEMANTiCS Conference in Vienna, we also managed to reach an important milestone regarding the beta-test for our data release tool.

First and foremost, already 3500 files have been published with the plugin. These files will be part of the new DBpedia release and are available on our LTS repository.

Secondly, the documentation of the testing has been brought into good shape. Feel free to drop by and check it out.
Thirdly, we reached our first interoperability goal. The current metadata is sufficient to produce RSS 1.0 feeds. See here for further information. We also defined a loose roadmap on top of the readme, where interoperability to DCAT and DCAT-AP has high priority.

 

Now we have some time to support you and work one on one and also prepare the configurations to help you set up the data releases. Lastly, we already received data from DNB and SUMO, so we will start to look into these more closely.

Thanks to all the beta-testers for your nice work.

We keep you posted.

Yours

DBpedia Association

Meet the DBpedia Chatbot

This year’s GSoC is slowly coming to an end with final evaluations already being submitted. In order to bridge the waiting time until final results are published, we like to draw your attention to a former project and great tool that was developed during last years’ GSoC.

Meet the DBpedia Chatbot. 

DBpedia Chatbot is a conversational Chatbot for DBpedia which is accessible through the following platforms:

  1. A Web Interface
  2. Slack
  3. Facebook Messenger

Main Purpose

The bot is capable of responding to users in the form of simple short text messages or through more elaborate interactive messages. Users can communicate or respond to the bot through text and also through interactions (such as clicking on buttons/links). There are 4 main purposes for the bot. They are:

  1. Answering factual questions
  2. Answering questions related to DBpedia
  3. Expose the research work being done in DBpedia as product features
  4. Casual conversation/banter
Question Types

The bot tries to answer text-based questions of the following types:

Natural Language Questions
  1. Give me the capital of Germany
  2. Who is Obama?
Location Information
  1. Where is the Eiffel Tower?
  2. Where is France’s capital?
Service Checks

Users can ask the bot to check if vital DBpedia services are operational.

  1. Is DBpedia down?
  2. Is lookup online?
Language Chapters

Users can ask basic information about specific DBpedia local chapters.

  1. DBpedia Arabic
  2. German DBpedia
Templates

These are predominantly questions related to DBpedia for which the bot provides predefined templatized answers. Some examples include:

  1. What is DBpedia?
  2. How can I contribute?
  3. Where can I find the mapping tool?
Banter

Messages which are casual in nature fall under this category. For example:

  1. Hi
  2. What is your name?

if you like to have a closer look at the internal processes and how the chatbot was developed, check out the DBpedia GitHub pages. 

DBpedia Chatbot was published on wiki.dbpedia.org and is one of many other projects and applications featuring DBpedia.

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In case you want your DBpedia based tool or demo to publish on our website just follow the link and submit your information, we will do the rest.

 

Yours

DBpedia Association

DBpedia Entity – Standard Test Collection for Entity Search over DBpedia

Today we are featuring DBpedia Entity, in our blog series of introducting interesting DBpedia applications and tools to the DBpedia community and beyond. Read on and enjoy.

DBpedia-Entity is a standard test collection for entity search over the DBpedia knowledge base. It is meant for evaluating retrieval systems that return a ranked list of entities (DBpedia URIs) in response to a free text user query.

The first version of the collection (DBpedia-Entity v1) was released in 2013, based on DBpedia v3.7 [1]. It was created by assembling search queries from a number of entity-oriented benchmarking campaigns and mapping relevant results to DBpedia. An updated version of the collection, DBpedia-Entity v2, has been released in 2017, as a result of a collaborative effort between the IAI group of the University of Stavanger, the Norwegian University of Science and Technology, Wayne State University, and Carnegie Mellon University [2]. It has been published at the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’17), where it received a Best Short Paper Honorable Mention Award. See the paper and poster.

DBpedia Entity was published on wiki.dbpedia.org and is one of many other projects and applications featuring DBpedia.

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