DIESEL project finished first mile stone

Posted on March 2, 2016 by Sandra Bartsch

DIESEL_Logo_2015_rgb

DIESEL is a European project funded by the Eurostars Eureka grant (E!9367) and is running from 1st September 2015 to 31st September 2018.

The goal of the DIESEL project is to develop a novel scalable approach to enable search through distributed enterprise data. Based on the natural language input DIESEL will understand the semantics of the user input, e.g. a question, and convert the search into a federated search over the large enterprise data. The result of the federated search queries will be converted into a comprehensive answer. The aim of the research project is to enhance the efficiency, reduce costs and improve the decision making process through a holistic answer based on the large enterprise data that has been queried.

The project consortium consists of three partners, namely Ontos from Switzerland as the project coordinator, as well as the German research partners metaphacts and Leipzig University’s AKSW.

On February 29th, we finished the first milestone including an initial feasibility study, technical architecture and a thorough requirements specification pertaining to our use cases. In particular, we want to cover three domains:

  • Querying Enriched Encyclopedic Knowledge
  • Search over Wikidata
  • Medium-Large Enterprise Search and Knowledge Graph

The documents themselves can be found at http://diesel-project.eu/deliverables.

Please get in touch with us on twitter or get to know more at our website.

Downloadlogo-eurekalogo-ecCo-funded by EUREKA member
countries and the European Union
Horizon 2020 Framework Programme
Posted in Announcements, DIESEL, Projects | Comments Off on DIESEL project finished first mile stone

AKSW Colloquium, 29.02.2016, Path-based Semantic Relatedness

Posted on February 28, 2016 by TommasoSoru

In the incoming colloquium, Michael Röder will present the paper “Path-based Semantic Relatedness on Linked Data and its use to Word and Entity Disambiguation” from Hulpus et al., published in the proceedings of ISWC 2015 [PDF].

Abstract

Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-of-the-art graph-based algorithms.

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.

Posted in Colloquium, paper presentation | Tagged , , , , | Comments Off on AKSW Colloquium, 29.02.2016, Path-based Semantic Relatedness

Three Papers accepted at ESWC 2016

Posted on February 26, 2016 by Sandra Bartsch

 

ESWC2016-Logo-Web-S_0

We are happy to announce that three papers got accepted for presentations at the 13th Extended Semantic Web Conference (ESWC 2016), held in Heraklion, Crete, Grece. The ESWC is a important international forum for the Semantic Web / Linked Data Community.

In more detail, we will present the following papers:

Detecting Similar Linked Datasets Using Topic Modelling (Michael Röder, Axel-Cyrille Ngonga Ngomo, Ivan Ermilov and Andreas Both)

This paper presents a novel approach towards dataset search. We show how we can generate dataset signatures using topic modelling and how these topic models can be used to improve dataset search.

The Lazy Traveling Salesman —  Memory Management for Large-Scale Link Discovery (Axel-Cyrille Ngonga Ngomo and Mofeed Hassan)

We study the problem of linking large RDF datasets on hardware where the data does not fit in memory. We present a novel caching solutions which allows determining sequences of operations for the efficient loading of data and execution of link discovery based thereon.

AskNow: A Framework for Natural Language Query Formalization in SPARQL (Mohnish Dubey, Sourish Dasgupta, Ankit Sharma, Konrad Höffner and Jens Lehmann)

We present a novel approach for the formalization of SPARQL queries for better question answering.

One more paper has been conditionally accepted, so we are looking forward for the final acceptance of Semantically Enhanced Quality Assurance in the JURION Business Use Case (Dimitris Kontokostas, Christian Mader, Michael Leuthold, Christian Dirschl, Katja Eck, Jens Lehmann and Sebastian Hellmann)

The final versions of the papers will be made available soon.

Come over to ESWC and enjoy the talks. For more information on the conference program and other papers please see here.

Sandra on behalf of AKSW

Posted in Announcements, Papers, Uncategorized | Comments Off on Three Papers accepted at ESWC 2016

AKSW Colloquium, 22.02.2016, LDRank

Posted on February 22, 2016 by EdgardMarx

Edgard MarxOn the 22th of February at 3 PM, Edgard Marx will present a paper titled “Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets”.

Abstract

During the last years data applications are using a big variety of algorithms in order to provide a better rank and/or results to the user.
These algorithms made use statistics, often found in the datasets (e.g. number of instances of a resource) as well as outside them (PageRank).
In this colloquium we are going to present one of this algorithms dubbed as LDRank.
LDRank explores the structure underlying the Web and the Web of Data to rank entities.

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.

 

Posted in Colloquium | Comments Off on AKSW Colloquium, 22.02.2016, LDRank

AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible

Posted on February 13, 2016 by TommasoSoru

Tommaso SoruOn the 15th of February at 3 PM, Tommaso Soru will present his ongoing research titled “Mandolin: Markov Logic Networks for Discovering Links”.

Later on, at 3:30 PM, our guest Adnan Akhter from HFT Stuttgart will present “X-FEASIBLE: Extended Feature-Based SPARQL Benchmark Generation Framework out of Queries Log”.

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.

 

Posted in Colloquium, invited talk, LinkingLOD, paper presentation | Tagged , , , , , | Comments Off on AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible

DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released

Posted on February 9, 2016 by Patrick Westphal

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

Posted in DL-Learner, Uncategorized | Comments Off on DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released

AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets

Posted on February 1, 2016 by NatanaelArndt

Natanael ArndtAt the todays colloquium, Natanael Arndt will discuss the the paper “Co-evolution of RDF Dataset” by Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour and Sören Auer (2016, available on arXiv)

Link: http://arxiv.org/abs/1601.05270v1

Abstract: For many use cases it is not feasible to access RDF data in a truly federated fashion. For consistency, latency and performance reasons data needs to be replicated in order to be used locally. However, both a replica and its origin dataset undergo changes over time. The concept of co-evolution refers to mutual propagation of the changes between a replica and its origin dataset. The co-evolution process addresses synchronization and conflict resolution issues. In this article, we initially provide formal definitions of all the concepts required for realizing co-evolution of RDF datasets. Then, we propose a methodology to address the co-evolution of RDF datasets. We rely on a property-oriented approach for employing the most suitable strategy or functionality. This methodology was implemented and tested for a number of different scenarios. The result of our experimental study shows the performance and robustness aspect of this methodology.

Posted in Colloquium, LEDS, paper presentation | Comments Off on AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets

Holographic Embeddings of Knowledge Graphs

Posted on February 1, 2016 by Johannes Frey

During the upcoming colloquium, Nilesh Chakraborty will give a short introduction on factorising RDF tensors and present a paper on “Holographic Embeddings of Knowledge Graphs”:

Holographic Embeddings of Knowledge Graphs

Authors: Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio
Abstract: Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.

Posted in Colloquium, paper presentation | Comments Off on Holographic Embeddings of Knowledge Graphs

AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem

Posted on January 25, 2016 by Ivan Ermilov

On the upcoming colloquium, Muhammad Saleem will present his paper “LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation” about the benchmarking of federated SPARQL endpoints. The other talk will be an introduction to the Docker ecosystem by Tim Ermilov.

LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation

Authors: Muhammad Saleem, Ali Hasnain, Axel Ngonga
Abstract. Gathering information from the Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as they are either synthetic, too small in size and complexity or do not provide means for a fine-grained evaluation. We propose LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. We evaluate state-of-the-art SPARQL endpoint federation approaches on this benchmark with respect to their query runtime, triple pattern-wise source selection, result completeness and correctness. Our evaluation results suggest that the performance of current SPARQL query federation systems on simple queries (in terms of total triple patterns, query result set sizes, execution time, use of SPARQL features etc.) does not reflect the systems’ performance on more complex queries. Moreover, current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets.

Introduction To The Docker Ecosystem

Presented by: Tim Ermilov
Slides are available online

On the upcoming colloquium, Muhammad Saleem will present his paper “LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation” about the benchmarking of federated SPARQL endpoints. The other talk will be an introduction to the Docker ecosystem by Tim Ermilov.

LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation

Authors: Muhammad Saleem, Ali Hasnain, Axel Ngonga
Abstract. Gathering information from the Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as they are either synthetic, too small in size and complexity or do not provide means for a fine-grained evaluation. We propose LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. We evaluate state-of-the-art SPARQL endpoint federation approaches on this benchmark with respect to their query runtime, triple pattern-wise source selection, result completeness and correctness. Our evaluation results suggest that the performance of current SPARQL query federation systems on simple queries (in terms of total triple patterns, query result set sizes, execution time, use of SPARQL features etc.) does not reflect the systems’ performance on more complex queries. Moreover, current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets.

Introduction To The Docker Ecosystem

Presented by: Tim Ermilov
Slides are available online.

Each talk will last for 20 minutes. The audience will have 10 minutes to ask questions. There will be cookies and coffee break after the talks for discussion as well.

 

Posted in Colloquium, paper presentation, tutorial, workshop or tutorial | Comments Off on AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem

HOBBIT project kick-off

Posted on January 22, 2016 by Sandra Bartsch

HOBBIT, a new InfAI project within the EU’s “Horizon 2020” framework program kicked-off in Luxembourg on 18 and 19 january in 2016.

The main goal of the HOBBIT project (@hobbit_project on Twitter) is to benchmark linked and big data systems and assess their performance using industry-relevant key performance indicators. To achieve this goal, the project develops 1) a holistic open-source platform and 2) eight industry-grade benchmarks for systems of different parts of the linked data lifecycle. These benchmarks will contain datasets based on industry-related, real-world data and can be scaled up to evaluate even Big Data solutions.

Our partners in this project are iMinds, AGT Group R&D GmbH, Fraunhofer IAIS, USU Software AG, Foundation for Research & Technology – Hellas (FORTH), National Center for Scientific Research “Demokritos” (NCSR), OpenLink Software, TomTom and Ontos AG.

Please continue reading Press Release “New EU project develops a platform for benchmarking large linked datasets” by University of Leipzig Press. The Text is also available in English.

Find out more at http://project-hobbit.eu/ and by following us (@hobbit_project) on Twitter.

This project has received funding from the European Union’s H2020 research and innovation action program under grant agreement number 688227.

HOBBIT ProjectEC-H2020

Posted in Announcements, HOBBIT, project kick-off | Comments Off on HOBBIT project kick-off