Tuesday, August 20, 2019 - 1:45pm
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.
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”
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
Thursday, August 8, 2019 - 1:41pm
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 email@example.com.
Thursday, August 1, 2019 - 12:41pm
DBpedia Live is a long term core project of DBpedia that immediately extracts fresh triples from all changed Wikipedia articles. After a long hiatus, fresh and live updated data is available once again, thanks to our former co-worker Lena Schindler whose work we feature in this blog post. Before we dive into Lena’s report, let’s have a look at some general info about DBpedia Live:
Live Enterprise Version
OpenLink Software provides a scalable, dedicated, live Virtuoso instance, built on Lena’s remastering. Kingsley Idehen announced the dedicated business service in our new DBpedia forum. .
On the Databus, we collect publicly shared and business-ready dedicated services in the same place where you can download the data. Databus allows you to download the data, build a service, and offer that service, all in one place. Data up-loaders can also see who builds something with their data
Remastering the DBpedia Live Module
Contribution by Lena Schindler
After developing the DBpedia REST API as part of a student project in 2018, I worked as a student Research Assistant for DBpedia. My task was to analyze and patch severe issues in the DBpedia Live instance. I will shortly describe the purpose of DBpedia Live, the reasons it went out of service, what I did to fix these, and finally, the changes needed to support multi-language abstract extraction.
The DBpedia Extraction Framework is Scala-based software with numerous features that have evolved around extracting knowledge (as RDF) from Wikis. One part is the DBpedia Live module in the “live-deployed” branch, which is intended to provide a continuously updated version of DBpedia by processing Wikipedia pages on demand, immediately after they have been modified by a user. The backbone of this module is a queue that is filled with recently edited Wikipedia pages, combined with a relational database, called Live Cache, that handles the diff between two consecutive versions of a page. The module that fills the queue, called Feeder, needs some kind of connection to a Wiki instance that reports changes to a Wiki Page. The processing then takes place in four steps:
- A wiki page is taken out of the queue.
- Triples are extracted from the page, with a given set of extractors.
- The new triples from the page are compared to the old triples from the Live Cache.
- The triple sets that have been deleted and added are published as text files, and the Cache is updated.
DBpedia Live has been out of service since May 2018, due to the termination of the Wikimedia RCStream Service, upon which the old DBpedia Live Feeder module relied. This socket-based service provided information about changes to an existing Wikimedia instance and was replaced by the EventStreams service, which runs over a single HTTP connection using chunked transfer encoding, and is following the Server-Sent Event (SSE) protocol. It provides a stream of events, each of which contains information about title, id, language, author, and time of every page edit of all Wikimedia instances.
Starting in September 2018, my first task was to implement a new Feeder for DBpedia Live that is based on this new Wikimedia EventStreams Service. For the Java world, the Akka framework provides an implementation of a SSE client. Akka is a toolkit developed by Lightbend. It simplifies the construction of concurrent and distributed JVM applications, enabling both Java and Scala access. The Akka SSE client and the Akka Streams module are used in the new EventStreamsFeeder (Akka Helper) to extract and process the data stream. I decided to use Scala instead of Java, because it is a more natural fit to Akka.
After I was able to process events, I had the problem that frequent interruptions in the upstream connection were causing the processing stream to fail. Luckily, Akka provides a fallback mechanism with back-off, similar to the Binary Exponential Backoff of the Ethernet protocol which I could use to restart the stream (called “Graph” in Akka terminology).
Another problem was that in many cases, there were many changes to a page within a short time interval, and if events were processed quickly enough, each change would be processed separately, stressing the Live Instance with unnecessary load. A simple “thread sleep” reduced the number of change-sets being published every hour from thousands to a few hundred.
The next task was to prepare the Live module for the extraction of abstracts (typically the first paragraph of a page, or the text before the table of contents). The extractors used for this task were re-implemented in 2017. It turned out to be a configuration issue first, and second a candidate for long debugging sessions, fixing issues in the dependencies between the “live” and “core” modules. Then, in order to allow the extraction of abstracts in multiple languages, the “live” module needed many small changes, at places spread across the code-base, and care had to be taken not to slow down the extraction in the single language case, compared to the performance before the change. Deployment was delayed by an issue with the remote management unit of the production server, but was accomplished by May 2019.
I also collected my knowledge of the Live module in detailed documentation, addressed to developers who want to contribute to the code. This includes an explanation of the architecture as well as installation instructions. After 400 hours of work, DBpedia Live is alive and kicking, and now supports multi-language abstract extraction. Being responsible for many aspects of Software Engineering, like development, documentation, and deployment, I was able to learn a lot about DBpedia and the Semantic Web, hone new skills in database development and administration, and expand my programming experience using Scala and Akka.
“Thanks a lot to the whole DBpedia Team who always provided a warm and supportive environment!”
Thank you Lena, it is people like you who help DBpedia improve and develop further, and help to make data networks a reality.
Yours DBpedia Association
Thursday, July 25, 2019 - 1:33pm
How is data edited in Wikipedia/Wikidata? Where does it come from? And how can we synchronize it globally?
The GlobalFactSync (GFS) Project — funded by the Wikimedia Foundation — started in June 2019 and has two goals:
- Answer the above-mentioned three questions.
- Build an information system to synchronize facts between all Wikipedia language-editions and Wikidata.
Now we are seven weeks into the project (10+ more months to go) and we are releasing our first prototypes to gather feedback.
How – Synchronization vs Consensus
We follow an absolute “Human(s)-in-the-loop” approach when we talk about synchronization. The final decision whether to synchronize a value or not should rest with a human editor who understands consensus and the implications. There will be no automatic imports. Our focus is to drastically reduce the time to research all references for individual facts.
A trivial example to illustrate our reasoning is the release date of the single “Boys Don’t Cry” (March 16th, 1989) in the English, Japanese, and French Wikipedia, Wikidata and finally in the external open database MusicBrainz. A human editor might need 15-30 minutes finding and opening all different sources, while our current prototype can spot differences and display them in 5 seconds.
We already had our first successful edit where a Wikipedia editor fixed the discrepancy with our prototype: “I’ve updated Wikidata so that all five sources are in agreement.” We are now working on the following tasks:
- Scaling the system to all infoboxes, Wikidata and selected external databases (see below on the difficulties there)
- Making the system:
- “live” without stale information
- “reliable” with less technical errors when extracting and indexing data
- “better referenced” by not only synchronizing facts but also references
Contributions and Feedback
To ensure that GlobalFactSync will serve and help the Wikiverse we encourage everyone to try our data and micro-services and leave us some feedback, either on our Meta-Wiki page or via email. In the following 10+ months, we intend to improve and build upon these initial results. At the same time, these microservices are available to every developer to exploit it and hack useful applications. The most promising contributions will be rewarded and receive the book “Engineering Agile Big-Data Systems”. Please post feedback or any tool or GUI here. In case you need changes to be made to the API, please let us know, too.
For the ambitious future developers among you, we have some budget left that we will dedicate to an internship. In order to apply, just mention it in your feedback post.
Data, APIs & Microservices (Technical prototypes)
Data Processing and Infobox Extraction
For GlobalFactSync we use data from Wikipedia infoboxes of different languages, as well as Wikidata, and DBpedia and fuse them to receive one big, consolidated dataset – a PreFusion dataset (in JSON-LD). More information on the fusion process, which is the engine behind GFS, can be found in the FlexiFusion paper. One of our next steps is to integrate MusicBrainz into this process as an external dataset. We hope to implement even more such external datasets to increase the amount of available information and references.
We deployed a set of microservices to show the current state of our toolchain.
- [Initial User Interface] The GlobalFactSync UI prototype (available at http://global.dbpedia.org) shows all extracted information available for one entity for different sources. It can be used to analyze the factual consensus between different Wikipedia articles for the same thing. Example: Look at the variety of population counts for Grimma.
- [PreFusion JSON API] While the UI allows simple, fast and easy browsing for one entity at a time, we also provide raw access to the underlying data (PreFusion dump). The query UI (http://global.dbpedia.org:8990 (user: read, pw: gfs) can be utilized to run simple analytical queries. Thus, we can determine the number of locations having at least one population value (1,194,007) but can also focus on examples with data quality problems (e.g. one of the 4,268 locations with more than 10 population values). Moreover, documentation about the PreFusion dataset and the download link for the data are available on the Databus website.
- [Reference Data Download] We ran the Reference Extraction Service over 10 Wikipedia languages. Download dumps here.
- [Reference Extraction Service] Good references are crucial for an import of facts from Wikipedia to Wikidata. We are currently working with colleagues from Poznań University of Economics and Business on reference extraction for facts from Wikipedia. A current development reference extraction microservice shows all references and the location where they were spotted in the Infobox – ad hoc – for a given article: http://dbpedia.informatik.uni-leipzig.de:8111/infobox/references?article=https://en.wikipedia.org/wiki/Facebook&format=json ( ‘&format=tsv’ also available)
- [Infobox Extraction Service] A similar ad hoc extraction of factual information from infoboxes and other Wikipedia article information is available here. This microservice displays information which can be extracted with the help of DBpedia mappings from an infobox e.g. from the German Facebook Wikipedia article: http://dbpedia.informatik.uni-leipzig.de:9998/server/extraction/en/extract?title=Facebook&revid=&format=trix&extractors=mappings. See here for more options: http://dbpedia.informatik.uni-leipzig.de:9999/server/extraction/.
- [ID service] Last but not least, we offer the Global ID Resolution Service. It ties together all available identifiers for one thing (i.e. at the moment all DBpedia/Wikipedia and Wikidata identifiers – MusicBrainz coming soon…) and shows their stable DBpedia Global ID.
Finding sync targets
In order to test out our algorithms, we started by looking at various groups of subjects, our so-called sync targets. Based on the different subjects a set of problems were identified with varying layers of complexity:
- identity check/check for ambiguity — Are we talking about the same entity?
- fixed vs. varying property — Some properties vary depending on nationality (e.g., release dates), or point in time (e.g., population count).
- reference — Depending on the entity’s identity check and the property’s fixed or varying state the reference might vary. Also, for some targets, no query-able online reference might be available.
- normalization/conversion of values — Depending on language/nationality of the article properties can have varying units (e.g., currency, metric vs imperial system).
The check for ambiguity is the most crucial step to ensure that the infoboxes that are being compared do refer to the same entity. We found, instances where the Wikipedia page and the infobox shown on that page were presenting information about different subjects (e.g., see here).
As a good sync target to start with the group ‘NBA players’ was identified. There are no ambiguity issues, it is a clearly defined group of persons, and the amount of varying properties is very limited. Information seems to be derived from mainly two web sites (nba.com and basketball-reference.com) and normalization is only a minor issue. ‘Video games’ also proved to be an easy sync target, with the main problem being varying properties such as different release dates for different platforms (Microsoft Windows, Linux, MacOS X, XBox) and different regions (NA vs EU).
More difficult topics, such as ‘cars’, ’music albums’, and ‘music singles’ showed more potential for ambiguity as well as property variability. A major concern we found was Wikipedia pages that contain multiple infoboxes (often seen for pages referring to a certain type of car, such as this one). Reference and fact extraction can be done for each infobox, but currently, we run into trouble once we fuse this data.
Further information about sync targets and their challenges can be found on our Meta-Wiki discussion page, where Wikipedians that deal with infoboxes on a regular basis can also share their insights on the matter. Some issues were also found regarding the mapping of properties. In order to make GlobalFactSync as applicable as possible, we rely on the DBpedia community to help us improve the mappings. If you are interested in participating, we will connect with you at http://mappings.dbpedia.org and in the DBpedia forum.
Bottomline – We value your feedback
Your DBpedia Association
The post Global Fact Sync – Synchronizing Wikidata & Wikipedia’s infoboxes appeared first on DBpedia Blog.
Thursday, July 18, 2019 - 11:39am
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 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:
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.
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
The post timbr – the DBpedia SQL Semantic Knowledge Platform appeared first on DBpedia Blog.
Thursday, July 11, 2019 - 12:54pm
With part II of our growth hack series, we would like to introduce you to the latest feature of our development – the new DBpedia Forum.
Why a new forum?
DBpedia has an inclusionist model and DBpedia is huge. At the core, there is data extracted from Wikipedia and Wikidata. Around this, there are derived datasets like the fusion/enrichment and also LHD. Additionally, we offer services such as DBpedia Spotlight, DBpedia Lookup, SameAs, and not to forget the main endpoint http://dbpedia.org/sparql as well as our DBpedia Chapters. All of this is surrounded by 25k academic papers and a vivid business network.
Since we have this inclusionist model, we believe that access to data and knowledge should be global and unified (and free where possible). That is exactly why we established the DBpedia Forum – to further this mission.
The DBpedia Forum is a shared community resource — a place to share skills, knowledge, and interests through an ongoing conversation about DBpedia and related topics. It is meant (among others) to replace our old support page for assistance with DBpedia. In the long run, we will shut down our (former) support page, as it is not serving our growing needs anymore.
This is what the forum currently looks like. Traffic and communication are still a little low. Start your conversation about DBpedia here and now.
Where are all the DBpedians?
We figured, most of you are already actively involved in exchange about DBpedia. However, the majority of that is scattered all over the web which makes it hard for us and others to keep track of. With the new forum, we offer you a playground for vivid exchange, and to meet and greet fellow DBpedians – a platform for everyone’s benefit.
The DBpedia Forum simplifies communication
Make this a great place for discussion by contributing yourself. It is super easy. Just visit https://forum.dbpedia.org/, browse the topics, and find the info that helps you or add your own. If you want to contribute just register and off you go. Improve the discussion by discovering ones that are already happening. Help us influence the future of the DBpedia community by engaging in discussions that make this forum an interesting place to be.
Transparency is all
To assist with maintaining an appropriate code of conduct the forum utilizes little discourse tools that enable the community to collectively identify the best (and worst) contributions. The forum tracks bookmarks, likes, flags, replies, edits, and many more. That is similar to the ranking in the old support system but much more transparent and much more fun.
For the hunter-gatherers among you, you can also earn batches for various activities – as long as you are active. And if you feel very passionate about a certain topic, we would gladly make you a moderator – just let us know.
Now is the time
Since you are already talking about DBpedia somewhere on the WWW, why not do it here and now for everyone else to follow? Your knowledge and skills are key, not only for individuals in this forum but also for the whole DBpedia community.
Happy posting and stay tuned for part III in the growth hack series. The next post will feature timbr – DBpedia SQL Semantic Knowledge Platform.
The post DBpedia Forum – New Ways to Exchange about DBpedia appeared first on DBpedia Blog.
Thursday, July 4, 2019 - 1:40pm
*UPDATE* – We are now 5 weeks in our growth hack. Read on below to find out how it all started. Click here to follow up on each of our milestones.
A growth hack – how come?
Things have gone a bit quiet around DBpedia. No new releases, no clear direction to go. Did DBpedia stop? Actually not. There were community and board member meetings, discussions, 500 messages per week on dbpedia.slack.com.
We are still there. We, as a community, restructured and now we are done, which means that DBpedia will now work more focused to build on its Technology Leadership role in the Web of Data and thus – with our very own DBpedia Growth Hack – bring new innovation and free fuel to everybody.
What is this growth hack?
We restructured in two areas:
- The agility of knowledge delivery – our release cycle was too slow and too expensive. We were unable to include substantial contributions from DBpedians. Therefore, quality and features stagnated.
- Transparent processes – DBpedia has a crafty community with highly skilled knowledge engineers backing it. At some point, we grew too much and became lumpy, with a big monolithic system that nobody could improve because of side effects. So we designed a massive curation infrastructure where information can be retrieved, adjusted and errors discussed and fixed.
We have been consistently working on this restructuring for two years now and we now have the infrastructure ready as horizontal prototype meaning each part works and everybody can start using it. We ate our own dog food and built the first application.
Now we will go through each part and polish & document it, and will report about it with a blog post each. Stay tuned !
Is DBpedia Academic or Industrial?
The Semantic Web has a history of being labelled as too academic and a part of it colored DBpedia as well. Here is our personal truth: It is an engineering project and therefore it swings both ways. It is a great academic success with 25,000 papers using the data and enabling research and innovation. The free data drives research on data-driven research. Also, we are probably THE fastest pathway from lab to market as our industry adoption has unprecedented speed. Proof will follow in the blog posts of the Growth Hack series.
Blog Posts of the Growth Hack series:
(not necessarily in that order, depending on how fast we can polish & document )
- Query DBpedia as SQL – a first service on the Databus
- DBpedia Live Extraction – Realtime updates of Wikipedia
- DBpedia Business Models – How to earn money with DBpedia & the Databus
- MARVIN Release Bot – together with https://blogs.tib.eu/wp/tib/ incl. an update of https://wiki.dbpedia.org/Datasets
- The new forum – https://forum.dbpedia.org is already ready to register, but needs some structure. Intended as replacement of support.dbpedia.org
In addition some announcements of on-going projects:
- GlobalFactSync (GFS) – Syncing facts between Wikipedia and Wikidata
- Energy Databus: LOD GEOSS project focusing on energy system data on the bus
- Supply-Chain-Management Databus – PLASS project focusing on SCM data on the bus
So, stay tuned for our upcoming posts and follow our journey.
Tuesday, June 18, 2019 - 2:52pm
For the second time now, we co-located one of our DBpedia community meetings with the LDK-conference. After the previous edition in Galway two years ago, It was Leipzig’s turn to host the event. Thus, the 13th DBpedia community meeting took place in this beautiful city which is also home to the DBpedia Association’s head office. Win, Win we’d say.
After a very successful LDK conference May 20th-21st, representatives of the European DBpedia community met at Villa Ida Mediencampus, on Thursday, May 23rd, to present their work with DBpedia and to exchange about the DBpedia Databus.
For those of you who missed it or for those who want a little retrospective on the day, this blog post provides you with a short LDK-wrap-up as well as a recap of our DBpedia Day.
First things first
First and foremost, we would like to thank LDK organizers for co-locating our meeting and thus enabling fruitful synergies, and a platform for the DBpedia community to exchange.
The first presentation that kicked-off the conference was given by Prof. Christiane Fellbaum from Princeton University. The topic of her talk was on “Mapping the Lexicons of Signs and Words” with the main focus on her research of mapping WordNet and SignStudy, a resource for American Sign Language. Shortly after, Prof Eduard Werner from Leipzig University gave a very exciting talk on the “Sorbian languages”. He discussed the nature of the Sorbian languages, their historical background, and the unfortunate imminent extinction of lower Sorbian due to a decline of native speakers.
The first day of LDK was full of exciting presentations related to various language-oriented topics. Researchers exchanged about linguistic vocabularies, SPARQL query recommendations, role and reference grammar, language detection, entity recognition, machine translation, under-resourced languages, metaphor identification, event detection and linked data in general. The first day ended with fruitful discussions during the poster session. After at the end of the first conference day, LDK visitors had the chance to mingle with locals in some of Leipzig’s most exciting bars during a pub crawl.
Prof. Christian Bizer from the University of Mannheim opened the second day with a keynote on “Schema.org Annotations and Web Tables: Underexploited Semantic Nuggets on the Web?”. In his talk, he gave a nice overview of the research on knowledge extraction around the large-scale Web Data Commons corpus, findings, open challenges and possible exploitations of this corpus.
The second day was busy with four sessions, each populated with presentations on exciting topics ranging from relation classification, dictionary linking and entity linking, to terminology models, topical thesauri and morphology.
The series of presentations was ended with an Organ Prelude played by David Timm, the University Music Director at the Leipzig University. Finally, the day and the conference was concluded with a conference dinner at Moritzbastei, one of Leipzig’s famous cultural centres.
On May 23rd, the DBpedia Community met for the 13th DBpedia community meeting. The event attracted more than 60 participants who extended their LDK experience or followed our call to Leipzig.
Opening & keynotes
The meeting was opened by Dr. Sebastian Hellmann, the executive director of the DBpedia Association. He gave an overview of the latest developments and achievements around DBpedia, with the main focus on the DBpedia Databus technologies. The first keynote was given by Dr. Peter Haase, from metaphacts, with an unusual interactive presentation on “Linked Data Fun with DBpedia”. The second keynote speaker was Prof. Heiko Paulheim, presenting findings, challenges and results from his work on the construction of the DBkWiki Knowledge Graph by exploiting the DBpedia extraction framework.
The showcases session started with a presentation given by Krzysztof Węcel on “Citations and references in DBpedia”, followed by Peter Nancke with a presentation on the “TeBaQA Question Answering System”, Maribel Acosta Deibe speaking about “Crowdsourcing the Quality of DBpedia” and finally, a presentation by Angus Addlesee on “Data Reconciliation using DBpedia”.
NLP & DBpedia session
The DBpedia & NLP session was opened by Diego Moussallem presenting the results from his work on “Generating Natural Language from RDF Data”. The second presentation was given by Christian Jilek on the topic of “Named Entity Recognition for Real-Time Applications”, which at the same time won the best research paper at the LDK conference. Next, Jonathan Kobbe presented the best student paper at the LDK conference on the topic of “Argumentative Relation Classification”. Finally, Edgard Marx closed the session with an overview presentation on “From the word to the resource”.
Side-Event – Hackathon
The “Artificial Intelligence for Smart Agriculture” Hackathon focused on enhancing the usability of automatic analysis tools which utilize semantic big data for agriculture, as well as conducting an outreach of the DataBio project for the DBpedia community. The event was supported by PNO, Spacebel, PSNC, and InfAI e.V.
In addition, we presented a paper about Linked Data publication pipelines, focusing on agri-related data, at the co-located LSWT conference.
After the event, DBpedians joined the DBpedia Association in the nearby pub Gosenschenke to delve into more vital talks about the Semantic Web world, Linked Data & DBpedia.
We are currently looking forward to the next DBpedia Community Meeting, on Sept, 12th in Karlsruhe, Germany. This meeting is co-located with the SEMANTiCS Conference. Contributions are still welcome. Just ping us via firstname.lastname@example.org and show us what you’ve got. You should also get in touch with us if you want to host a DBpedia Meetup yourself. We will help you with the program, the dissemination or organizational matters of the event if need be.
Your DBpedia Association
The post Home Sweet Home – The 13th DBpedia Community Meeting appeared first on DBpedia Blog.
Thursday, April 11, 2019 - 3:20pm
Artificial Intelligence (AI) is currently the central subject of the just announced ‘Year of Science’ by the Federal German Ministry. In recent years, new approaches were explored on how to facilitate AI, new mindsets were established and new tools were developed, new technologies implemented. AI is THE key technology of the 21st century. Together with Machine Learning (ML), it transforms society faster than ever before and, will lead humankind to its digital future.
In this digital transformation era, success will be based on using analytics to discover the insights locked in the massive volume of data being generated today. Success with AI and ML depends on having the right infrastructure to process the data.
The Value of Data Governance
One key element to facilitate ML and AI for the digital future of Europe, are ‘decentralized semantic data flows’, as stated by Sören Auer, a founding member of DBpedia and current director at TIB, during a meeting about the digital future in Germany at the Bundestag. He further commented that major AI breakthroughs were indeed facilitated by easily accessible datasets, whereas the Algorithms used were comparatively old.
In conclusion, Auer reasons that the actual value lies in data governance. Infact, in order to guarantee progress in AI, the development of a common and transparent understanding of data is necessary. 
DBpedia Databus – Digital Factory Platform
The DBpedia Databus – our digital factory Platform – is one of many drivers that will help to build the much-needed data infrastructure for ML and AI to prosper. With the DBpedia Databus, we create a hub that facilitates a ‘networked data-economy’ revolving around the publication of data. Upholding the motto, Unified and Global Access to Knowledge, the databus facilitates exchanging, curating and accessing data between multiple stakeholders – always, anywhere. Publishing data on the Databus means connecting and comparing (your) data to the network. Check our current DBpedia releases via http://downloads.dbpedia.org/repo/dev/.
DBpediaDay – & AI for Smart Agriculture
Furthermore, you can learn about the DBpedia Databus during our 13th DBpedia Community meeting, co-located with LDK conference, in Leipzig, May 2019. Additionally, as a special treat for you, we also offer an AI side-event on May 23rd, 2019.
May we present you the thinktank and hackathon – “Artificial Intelligence for Smart Agriculture”. The goal of this event is to develop new ideas and small tools which can demonstrate the use of AI in the agricultural domain or the use of AI for a sustainable bio-economy. In that regard, a special focus will be on the use and the impact of linked data for AI components.
In short, the two-part event, co-located with LSWT & DBpediaDay, comprises workshops, on-site team hacking as well as presentations of results. The activity is supported by the projects DataBio and Bridge2Era as well as CIAOTECH/PNO. All participating teams are invited to join and present their projects. Further Information are available here. Please submit your ideas and projects here.
Finally, the DBpedia Association is looking forward to meeting you in Leipzig, home of our head office. Pay us a visit!
 Zeus Kerravala; The Success of ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Requires an Architectural Approach to Infrastructure. ZK Research: A Division of Kerravala Consulting © 2018 ZK Research, available via http://bit.ly/2UwTJRo
 Sören Auer; Statement at the Bundestag during a meeting in AI, Summary is available via https://www.tib.eu/de/service/aktuelles/detail/tib-direktor-als-experte-...
Sunday, March 24, 2019 - 1:40pm
Call for Participation LDK – Conference
With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. In that context, we would like to draw your attention to the 2nd Language, Data and Knowledge conference, short LDK conference which will be held in Leipzig from May 20th till 22nd, 2019.
This new biennial conference series aims at bringing together researchers from across disciplines concerned with language data in data science and knowledge-based applications.
We are happy, that Christian Bizer, a founding member of DBpedia, will be one of the three amazing keynote speakers that open the LDK conference. Apart from Christian, Christiane Fellbaum from Princeton University and Eduart Werner, representative of Leipzig University will share their thoughts on current language data issues to start vital discussions revolving around language data.
Be part of this event in Leipzig and catch up with the latest research outcomes in the areas of acquisition, provenance, representation, maintenance, usability, quality as well as legal, organizational and infrastructure aspects of language data.
DBpedia Community Meeting
To get the full Leipzig experience, we also like to invite you to our DBpedia Community meeting, which is colocated with LDK and will be held on May, 23rd 2019. Contributions are still welcome. Just in get in touch via email@example.com .
We also offer an interesting side-event, the Thinktank and Hackathon “Artificial Intelligence for Smart Agriculture”. Visit our website for further information.
Join LDK conference 2019 and our DBpedia Community Meeting to catch up with the latest research and developments in the Semantic Web Community.
Yours DBpedia Association
The post Call for Participation – LDK Conference & DBpedia Day appeared first on DBpedia Blog.