AKSW Blog

Dear all,

the LIMES Dev team is happy to announce LIMES 1.0.0.

LIMES, the Link Discovery Framework for Metric Spaces, is a link discovery framework for the Web of Data. It implements time-efficient approaches for large-scale link discovery based on the characteristics of metric spaces. Our approaches facilitate different approximation techniques to compute estimates of the similarity between instances. These estimates are then used to filter out a large amount of those instance pairs that do not suffice the mapping conditions. By these means, LIMES can reduce the number of comparisons needed during the mapping process by several orders of magnitude. The approaches implemented in LIMES include the original LIMES  algorithm for edit distances, HR3, HYPPO and ORCHID.

Additionally, LIMES supports the first planning technique for link discovery HELIOS, that minimizes the overall execution of a link specification, without any loss of completeness. Moreover, LIMES implements supervised and unsupervised machine-learning algorithms for finding accurate link specifications. The algorithms implemented here include the supervised, active and unsupervised versions of EAGLE and WOMBAT.

Website: http://aksw.org/Projects/LIMES.html

Download: https://github.com/AKSW/LIMES-dev/releases/tag/1.0.0

GitHub: https://github.com/AKSW/LIMES-dev

User manual: http://aksw.github.io/LIMES-dev/user_manual/

Developer manual: http://aksw.github.io/LIMES-dev/developer_manual/

What is new in LIMES 1.0.0:

• New LIMES GUI
• New Controller that supports manual and graphical configuration
• New machine learning pipeline: supports supervised, unsupervised and active learning algorithms
• New dynamic planning for efficient link discovery
• Updated execution engine to handle dynamic planning
• Added support for qualitative (Precision, Recall, F-measure etc.) and quantitative (runtime duration etc.) evaluation metrics for mapping evaluation, in the presence of a gold standard
• Added support for configuration files in XML and RDF formats
• Added support for pointsets metrics such as Mean, Hausdorff and Surjection
• Added support for MongeElkan, RatcliffObershelp string measures
• Added support for Allen’s algebra temporal relations for event data
• Added support for all topological relations derived from the DE-9IM model
• Migrated the codebase to Java 8 and Jena 3.0.1

We would like to thank everyone who helped to create this release. We also acknowledge the support of the SAKE  and HOBBIT projects.

Kind regards,

The LIMES Dev team

Dear all,

the Smart Data Analytics group at AKSW is happy to announce DL-Learner 1.3.

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.

In the current release, we added a large number of new algorithms and features. For instance, DL-Learner supports terminological decision tree learning, it integrates the LEAP and EDGE systems as well as the BUNDLE probabilistic OWL reasoner. We migrated the system to Java 8, Jena 3, OWL API 4.2 and Spring 4.3. We want to point to some related efforts here:

• A new DL-Learner overview article is available at the Journal of Web Semantics (pre-print PDF).
• We started a benchmarking framework for supervised machine learning from structured data (not restricted to RDF/OWL).
• An article about the SPARQL reasoning component is now available (published at ECAI).
• An article about terminological decision tree learning is available (published at EKAW).

We want to thank everyone who helped to create this release, in particular we want to thank Giuseppe Cota who visited the core developer team and significantly improved DL-Learner. We also acknowledge support by the recently SAKE project, in which DL-Learner will be applied to event analysis in manufacturing use cases, as well as Big Data Europe and HOBBIT projects.

Kind regards,

Lorenz Bühmann, Jens Lehmann, Patrick Westphal and Simon Bin

On the 15th of August at 3 PM, Michael Röder will present the paper “RDF Query Relaxation Strategies Based on Failure Causes” of Fokou et al. in P702.

Abstract

Recent advances in Web-information extraction have led to the creation of several large Knowledge Bases (KBs). Querying these KBs often results in empty answers that do not serve the users’ needs. Relaxation of the failing queries is one of the cooperative techniques used to retrieve alternative results. Most of the previous work on RDF query relaxation compute a set of relaxed queries and execute them in a similarity-based ranking order. Thus, these approaches relax an RDF query without knowing its failure causes (FCs). In this paper, we study the idea of identifying these FCs to speed up the query relaxation process. We propose three relaxation strategies based on various information levels about the FCs of the user query and of its relaxed queries as well. A set of experiments conducted on the LUBM benchmark show the impact of our proposal in comparison with a state-of-the-art algorithm.

The paper is available at researchgate.

About the AKSW Colloquium

This event is part of a series of events about Semantic Web technology. Please see http://wiki.aksw.org/Colloquium for further information about previous and future events. As always, Bachelor and Master students are able to get points for attendance and there is complimentary coffee and cake after the session.

On Monday 18.07.2016, Kleanthi Georgala will give her Colloquium presentation for her paper “An Efficient Approach for the Generation of Allen Relations”, that was accepted at the European Conference on Artificial Intelligence (ECAI) 2016.

Abstract

Event data is increasingly being represented according to the Linked Data principles. The need for large-scale machine learning on data represented in this format has thus led to the need for efficient approaches to compute RDF links between resources based on their temporal properties. Time-efficient approaches for computing links between RDF resources have been developed over the last years. However, dedicated approaches for linking resources based on temporal relations have been paid little attention to. In this paper, we address this research gap by presenting AEGLE, a novel approach for the efficient computation of links between events according to Allen’s interval algebra. We study Allen’s relations and show that we can reduce all thirteen relations to eight simpler relations. We then present an efficient algorithm with a complexity of O(n log n) for computing these eight relations. Our evaluation of the runtime of our algorithms shows that we outperform the state of the art by up to 4 orders of magnitude while maintaining a precision and a recall of 1.

Afterwards, Tommaso Soru will present a paper considered the latest chapter of the Everything-2-Vec saga, which encompasses outstanding works such as Word2Vec and Doc2Vec. The paper title is “node2vec: Scalable Feature Learning for Networks” [PDF] by Aditya Grover and Jure Leskovec, accepted for publication at the International Conference on Knowledge Discovery and Data Mining (KDD), 2016 edition.