DL-Learner – AKSW Blog https://blog.aksw.org Agile Knowledge Engineering and Semantic Web Tue, 24 Sep 2019 20:45:28 +0000 en-US hourly 1 https://wordpress.org/?v=5.5.3 DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released https://blog.aksw.org/dl-learner-1-4-supervised-structured-machine-learning-framework-released/ Tue, 24 Sep 2019 20:41:46 +0000 http://blog.aksw.org/?p=3093 Continue reading →]]> Dear all,

The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce

DL-Learner 1.4.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis.

Website: http://dl-learner.org
GitHub page: https://github.com/SmartDataAnalytics/DL-Learner
Download: https://github.com/SmartDataAnalytics/DL-Learner/releases/tag/1.4.0

In the current release, we continued to improve the code and work on our query tree and class expression learning algorithms. The config file can now optionally be written in Json syntax. We updated the packaging to be ready for Java 11 and also tested DL-Learner on Windows. Some logical fixes to the Horizontal Expansion in CELOE were reported and analysed by Yingbing Hua, thanks!

The DL-Learner system has also been presented at The Web Conference in Lyon 2018 [3]. We want to thank everyone who helped to create this release. We also acknowledge support by the following projects: LIMBO [4], QROWD [5], SAKE [6], Big Data Europe [7], HOBBIT [8], GeoKnow [9], GOLD [10], and SLIPO [11].

Kind regards,

Jens Lehmann, Lorenz Bühmann, Patrick Westphal and Simon Bin

[1] http://sda.tech
[2] https://infai.org/efficient-technology-integration/
[3] http://jens-lehmann.org/files/2018/www_dllearner.pdf
[4] https://www.limbo-project.org/
[5] http://qrowd-project.eu/
[6] https://www.sake-projekt.de/
[7] https://www.big-data-europe.eu/
[8] http://project-hobbit.eu/
[9] http://geoknow.eu/
[10] http://aksw.org/Projects/GOLD.html
[11] http://www.slipo.eu/

]]> Article accepted in Journal of Web Semantics https://blog.aksw.org/article-accepted-in-journal-of-web-semantics/ Tue, 02 Aug 2016 07:54:24 +0000 http://blog.aksw.org/?p=2747 Continue reading →]]> We are happy to announce that the article “DL-Learner – A Framework for Inductive Learning on the Semantic Web” by Lorenz Bühmann, Jens Lehmann and Patrick Westphal was accepted for publication in the Journal of Web Semantics: Science, Services and Agents on the World Wide Web.

Abstract:

In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases.

]]> DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released https://blog.aksw.org/dl-learner-1-2-supervised-structured-machine-learning-framework-released/ Tue, 09 Feb 2016 15:19:55 +0000 http://blog.aksw.org/?p=2494 Continue reading →]]> Dear all,

we are happy to announce DL-Learner 1.2.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis.

Website: http://dl-learner.org
GitHub page: https://github.com/AKSW/DL-Learner
Download: https://github.com/AKSW/DL-Learner/releases
ChangeLog: http://dl-learner.org/development/changelog/

DL-Learner is used for data analysis tasks within other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern-based and evolutionary techniques for learning on structured data. For a practical example, see http://dl-learner.org/community/carcinogenesis/. It also offers a plugin for Protégé, which can give suggestions for axioms to add. DL-Learner is part of the Linked Data Stack – a repository for Linked Data management tools.

In the current release, we improved the support for SPARQL endpoints as knowledge sources. You can now directly use a SPARQL endpoint for learning without an OWL reasoner on top of it. Moreover, we extended DL-Learner to also consider dates and inverse properties for learning. Further efforts were made to improve our Query Tree Learning algorithms (those are used to learn SPARQL queries rather than OWL class expressions).

We want to thank everyone who helped to create this release, in particular Robert Höhndorf and Giuseppe Rizzo. We also acknowledge support by the recently started SAKE project, in which DL-Learner will be applied to event analysis in manufacturing use cases, as well as the GeoKnow and Big Data Europe projects where it is part of the respective platforms.

Kind regards,

Lorenz Bühmann, Jens Lehmann, Patrick Westphal and Simon Bin

]]> DL-Learner 1.1 (Supervised Structured Machine Learning Framework) Released https://blog.aksw.org/dl-learner-1-1/ Wed, 22 Jul 2015 14:14:57 +0000 http://blog.aksw.org/?p=2208 Continue reading →]]> Dear all,

we are happy to announce DL-Learner 1.1.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis.

Website: http://dl-learner.org
GitHub page: https://github.com/AKSW/DL-Learner
Download: https://github.com/AKSW/DL-Learner/releases
ChangeLog: http://dl-learner.org/development/changelog/

DL-Learner is used for data analysis in other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern based and evolutionary techniques for learning on structured data. For a practical example, see http://dl-learner.org/community/carcinogenesis/. It also offers a plugin for Protege, which can give suggestions for axioms to add. DL-Learner is part of the Linked Data Stack – a repository for Linked Data management tools.

In the current release, we improved the support for SPARQL endpoints as knowledge sources. You can now directly use a SPARQL endpoint for learning without an OWL reasoner on top of it. Moreover, we extended DL-Learner to also consider dates and inverse properties for learning. Further efforts were made to improve our Query Tree Learning algorithms (those are used to learn SPARQL queries rather than OWL class expressions).

We want to thank everyone who helped to create this release, in particular Robert Höhndorf and Giuseppe Rizzo. We also acknowledge support by the recently started SAKE project, in which DL-Learner will be applied to event analysis in manufacturing use cases, as well as the GeoKnow and Big Data Europe projects where it is part of the respective platforms.

Kind regards,

Lorenz Bühmann, Jens Lehmann, Patrick Westphal and Simon Bin

]]> DL-Learner 1.0 (Supervised Structured Machine Learning Framework) Released https://blog.aksw.org/dl-learner-1-0/ Fri, 13 Feb 2015 09:38:26 +0000 http://blog.aksw.org/?p=1920 Continue reading →]]> Dear all,

we are happy to announce DL-Learner 1.0.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis.

Website: http://dl-learner.org
GitHub page: https://github.com/AKSW/DL-Learner
Download: https://github.com/AKSW/DL-Learner/releases
ChangeLog: http://dl-learner.org/development/changelog/

DL-Learner is used for data analysis in other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern based and evolutionary techniques for learning on structured data. For a practical example, see http://dl-learner.org/community/carcinogenesis/. It also offers a plugin for Protege, which can give suggestions for axioms to add. DL-Learner is part of the Linked Data Stack – a repository for Linked Data management tools.

We want to thank everyone who helped to create this release, in particular (alphabetically) An Tran, Chris Shellenbarger, Christoph Haase, Daniel Fleischhacker, Didier Cherix, Johanna Völker, Konrad Höffner, Robert Höhndorf, Sebastian Hellmann and Simon Bin. We also acknowledge support by the recently started SAKE project, in which DL-Learner will be applied to event analysis in manufacturing use cases, as well as the GeoKnow and Big Data Europe projects where it is part of the respective platforms.

Kind regards,

Lorenz Bühmann, Jens Lehmann and Patrick Westphal

]]> Kick-Off for the BMWi project SAKE https://blog.aksw.org/kick-off-for-the-bmwi-project-sake/ Tue, 03 Feb 2015 10:39:22 +0000 http://blog.aksw.org/?p=1896 Continue reading →]]> Hi all!

One of AKSW’s Big Data Project, SAKE – Semantische Analyse Komplexer Ereignisse (SAKE – Semantic Analysis of Complex Events) kicked-off in Karlsruhe. SAKE is one of the winners of the Smart Data Challenge and is funded by the German BMWi (Bundesministerium für Wirtschaft und Energie) and has a duration of 3 years. Within this project, AKSW will develop powerful methods for analysis of industrial-scale Big Linked Data in real time. To this end, the team will extend existing frameworks like LIMES, DL-Learner, QUETSAL and FOX. Together with USU AG, Heidelberger Druckmaschinen, Fraunhofer  IAIS and AviComp Controls novel methods for tackling Business Intelligence challenges will be devised.

More info to come soon!

Stay tuned!

Axel on behalf of the SAKE team

]]> AKSW successful at #ISWC2014 https://blog.aksw.org/aksw-successful-at-iswc2014/ Tue, 28 Oct 2014 16:03:42 +0000 http://blog.aksw.org/?p=1805 Continue reading →]]> Dear followers, 9 members of AKSW have been participating at the 13th International Semantic Web Conference (ISWC) at Riva del Garda, Italy. Next to listening to interesting talks, giving presentations or discussing with fellow Semantic Web researchers, AKSW won 4 significant prizes:

We do work on way more projects, which you can find at http://aksw.org/projects/. Cheers, Ricardo on behalf of the AKSW group
Best Paper Award

]]> AKSW successful at ESWC 2014 https://blog.aksw.org/aksw-successful-at-eswc-2014/ Tue, 03 Jun 2014 14:20:48 +0000 http://blog.aksw.org/?p=1590 Continue reading →]]>

Greetings!

After a week full of exciting presentations, demos, posters and especially keynotes AKSW members are back from the 11th Extended Semantic Web Conference 2014 (ESWC).

Furthermore, we bring happy news with us. AKSW members won four prizes this year! In detail these are:

Meet us at aksw.org!

All the best,
Ricardo on behalf of AKSW

]]> LOD2 STACK USABILITY SURVEY STARTED https://blog.aksw.org/lod2-stack-usability-survey-started/ https://blog.aksw.org/lod2-stack-usability-survey-started/#comments Tue, 16 Apr 2013 11:06:40 +0000 http://blog.aksw.org/?p=976 Continue reading →]]> In the recent years the LOD2 stack established a collection of applications developed in the context of the LOD2 project, presented as an unified environment. These applications are referred to as components although they can also be installed independently. However, having all these components in a single environment eases the access from one application to the other and improves the UI experience.

As LOD2 stack is now available in it’s second version, questions of usability and end users experience came more in the focus of the ungoing development. So the LOD2 consortium set up  a survey asking users of the LOD2 stack (or the online Demonstrator) for feedback, regarding their experiences with the LOD2stack and the separate components. The outcome of this, is to fine tune development and improve the user experience in each phase of the Linked Data life cycle.

The survey is open from April 15 to June 30 and will only demand 15 minutes of your time.

]]> https://blog.aksw.org/lod2-stack-usability-survey-started/feed/ 2 AKSW @ ISWC 2012 https://blog.aksw.org/iswc/ https://blog.aksw.org/iswc/#respond Fri, 23 Nov 2012 16:51:54 +0000 http://blog.aksw.org/?p=788 Continue reading →]]> The 11th edition of the International Semantic Web Conference (ISWC) took place in Boston this year and AKSW was very active with 5 of our researchers participating in the conference. ISWC is one of the key conferences for presenting the latest advancement in Semantic Web research. We were active in the following activities:

Workshop Keynote:

  1. “Managing the Life-Cycle of Large Scale Web Datasets” presented by Jens Lehmann at the Joint Workshop for Evolution and Dynamics; presentation

Workshop Presentations:

  1. “Extending the WebID Protocol with Access Delegation” (Sebastian Tramp, Henry Story, Andrei Sambra, Philipp Frischmuth, Michael Martin and Sören Auer) presented by Philipp Frischmuth; paper
  2. “Query Segmentation and Resource Disambiguation Leveraging Background Knowledge” (Saeedeh Shekarpour, Axel-Cyrille Ngonga Ngomo and Sören Auer) – presented by Axel-Cyrille Ngonga Ngomo
  3. “Learning conformation rules for linked data integration” (Axel-Cyrille Ngonga Ngomo) – presented by Axel-Cyrille Ngonga Ngomo

Conference Articles:

  1. “DeFacto – Deep Fact Validation” (Jens Lehmann, Daniel Gerber,Mohamed Morsey, Axel-Cyrille Ngonga Ngomo) – presented by Daniel Gerber; project page; paper
  2. “DEQA: Deep Web Extraction for Question Answering” (Jens Lehmann, Tim Furche,Giovanni Grasso, Axel-Cyrille Ngonga Ngomo, Christian Schallhart, Andrew Sellers, Christina Unger, Lorenz Bühmann, Daniel Gerber, Konrad Höffner) – presented by Jens Lehmann; project page; paper
  3. “Managing the life-cycle of Linked Data with the LOD2 Stack” (Sören Auer, Lorenz Bühmann, Jens Lehmann, Michael Hausenblas, Sebastian Tramp, Bert van Nuffelen, Pablo Mendes, Christian Dirschl, Robert Isele, Hugh Williams) – presented by Jens Lehmann; LOD2 website; paper
  4. “RDFS Reasoning on Massively Parallel Hardware” (Norman Heino, Jeff Z. Pan) — presented by Norman Heino
  5. “Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures.” (Axel-Cyrille Ngonga Ngomo) — presented by Axel-Cyrille Ngonga Ngomo

Meetings:

  1. Semantic Web Journal Editorial Board Meeting
  2. DBpedia Meeting
  3. MIT Campus Tour
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