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WebPIE: A Web-scale Parallel Inference Engine using MapReduce

Jacopo Urbani, Spyros Kotoulas, Jason Maassen, Frank Van Harmelen, Henri Bal
2012 Journal of Web Semantics  
We have implemented WebPIE (Web-scale Inference Engine) and we demonstrate its performance on a cluster of up to 64 nodes.  ...  In this article, we propose a distributed technique to perform materialization under the RDFS and OWL ter Horst semantics using the MapReduce programming model.  ...  To evaluate our methods, we have implemented a prototype called WebPIE (Web-scale Parallel Inference Engine) using the Hadoop framework.  ... 
doi:10.1016/j.websem.2011.05.004 fatcat:7hifunbllrf25fastlqnkt6bvi

Corrigendum to "WebPIE: A Web-scale Parallel Inference Engine using MapReduce" [Web Semant. Sci. Serv. Agents World Wide Web 10 (2012) 59–75]

Jacopo Urbani, Spyros Kotoulas, Jason Maassen, Frank Van Harmelen, Henri Bal
2012 Journal of Web Semantics  
On page 69, Add a footnote after the second sentence of Section 6.5: ''Notice that in some case the implementation used for the duplicate strategies ''threshold'' and ''end'' can lead to an infinite loop  ...  This can be prevented by simply forcing the deletion after a fixed number of steps. 7. On page 72, add, at the end of the first paragraph of Appendix A.  ... 
doi:10.1016/j.websem.2012.09.005 fatcat:lphkehs3wff5bjx325k6qcq53e

OWL Reasoning with WebPIE: Calculating the Closure of 100 Billion Triples [chapter]

Jacopo Urbani, Spyros Kotoulas, Jason Maassen, Frank van Harmelen, Henri Bal
2010 Lecture Notes in Computer Science  
We demonstrate the WebPIE inference engine, built on top of the Hadoop platform and deployed on a compute cluster of 64 machines.  ...  We have evaluated our approach using some real-world datasets (UniProt and LDSR, about 0.9-1.5 billion triples) and a synthetic benchmark (LUBM, up to 100 billion triples).  ...  Introduction In this paper, we address the problem of massively scalable OWL reasoning and present WebPIE (Web-scale Parallel Inference Engine).  ... 
doi:10.1007/978-3-642-13486-9_15 fatcat:uqzzpudchzhrxneqx547u73fqy

Large Scale Fuzzy pD * Reasoning Using MapReduce [chapter]

Chang Liu, Guilin Qi, Haofen Wang, Yong Yu
2011 Lecture Notes in Computer Science  
The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD * semantics.  ...  The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD * semantics and has shown promising results.  ...  The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for large scale OWL ontologies in pD * fragment.  ... 
doi:10.1007/978-3-642-25073-6_26 fatcat:f5zqayakbrgmtmrbbvixssb3se

Large-Scale Reasoning with OWL [article]

Michael Ruster
2016 arXiv   pre-print
The WebPIE reasoner is discussed in detail as an example for forward chaining using MapReduce for materialisation.  ...  Moreover, the QueryPIE reasoner is presented as a backward chaining/hybrid approach which uses query rewriting. Furthermore, an overview on other reasoners is given such as OWLIM and TrOWL.  ...  [41] employ MapReduce in their reasoner Web-scale Inference Engine (short: WebPIE ) for reasoning on OWL pD* rules.  ... 
arXiv:1602.04473v1 fatcat:4b3gdsoct5ge3a5txqw2ah23cu

A survey of large-scale reasoning on the Web of data

Grigoris Antoniou, Sotiris Batsakis, Raghava Mutharaju, Jeff Z. Pan, Guilin Qi, Ilias Tachmazidis, Jacopo Urbani, Zhangquan Zhou
2018 Knowledge engineering review (Print)  
In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain  ...  This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies.  ...  Part of this work was done by Raghava Mutharaju when he was a PhD student at Wright State University during which time he acknowledges the support of the National Science Foundation under award 1017225  ... 
doi:10.1017/s0269888918000255 fatcat:bergc5uphbceznigppektgvzrm

Large-Scale Reasoning with (Semantic) Data

Grigoris Antoniou, Sotiris Batsakis, Ilias Tachmazidis
2014 Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) - WIMS '14  
In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain  ...  This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies.  ...  Part of this work was done by Raghava Mutharaju when he was a PhD student at Wright State University during which time he acknowledges the support of the National Science Foundation under award 1017225  ... 
doi:10.1145/2611040.2611041 dblp:conf/wims/AntoniouBT14 fatcat:g3ljwx4eqrg3po242ckkv6rwme

Reasoning with Large Scale Ontologies in Fuzzy pD* Using MapReduce

Chang Liu, Guilin Qi, Haofen Wang, Yong Yu
2012 IEEE Computational Intelligence Magazine  
The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for large scale OWL ontologies in pD * fragment.  ...  Soma and Prasanna [19] presented a technique for parallel OWL inference through data partitioning.  ...  This join can be simply calculated using a MapReduce program. 5) Handling Vague SameAs Closure However, not all sameAs triples are certain sameAs triples.  ... 
doi:10.1109/mci.2012.2188589 fatcat:swiitidb7nhp5jjiqlcyy3nv5q

The state-of-the-art in web-scale semantic information processing for cloud computing [article]

Wei Yu, Junpeng Chen
2013 arXiv   pre-print
These applications also bring a large scale heterogeneous and distributed information which pose a great challenge in terms of the semantic ambiguity.  ...  semantic reasoning and parallel semantic computing by exploiting semantic information newly available in cloud computing environment.  ...  WebPie (web-scale parallel inference engine) [63, 64] performs parallel rule-based forward reasoning based on Mapreduce framework.  ... 
arXiv:1305.4228v1 fatcat:duay2zecwfbqdbwzvy66fyixa4

An unsupervised classification process for large datasets using web reasoning

Rafael Peixoto, Thomas Hassan, Christophe Cruz, Aurélie Bertaux, Nuno Silva
2016 Proceedings of the International Workshop on Semantic Big Data - SBD '16  
We aim to extract knowledge from these sources using a Hierarchical Multi-Label Classification process called Semantic HMC.  ...  This paper focuses in the last two steps and presents a new highly scalable process to classify items from huge sets of unstructured text by using ontologies and rule-based reasoning.  ...  the classification at query time approach by using a triple-store with a backward-chaining inference engine.  ... 
doi:10.1145/2928294.2928301 dblp:conf/sigmod/PeixotoHCBS16 fatcat:f6ina2i74rfmrpzk76z45wmrnu

A Semantic Engine for Internet of Things: Cloud, Mobile Devices and Gateways

Amelie Gyrard, Soumya Kanti Datta, Christian Bonnet, Karima Boudaoud
2015 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing  
In this paper, we design a semantic engine to meet these requirements and integrate it in different components available in IoT architectures.  ...  Semantics is becoming a requirement in Internet of Things (IoT).  ...  In 2013, WebPIE (Web-scale Parallel Inference Engine) is an inference engine for semantic web reasoning (OWL and RDFS) based on the Hadoop platform designed by Urbani et al [15] .  ... 
doi:10.1109/imis.2015.83 dblp:conf/imis/GyrardDBB15 fatcat:b4c63f3agnf7vf7zya6tz6zzoq

RDF in the clouds: a survey

Zoi Kaoudi, Ioana Manolescu
2014 The VLDB journal  
The Resource Description Framework (RDF) pioneered by the W3C is increasingly being adopted to model data in a variety of scenarios, in particular data to be published or exchanged on the Web.  ...  In this article, we survey RDF data management architectures and systems designed for a cloud environment, and more generally, those large-scale RDF data management systems that can be easily deployed  ...  fully distributed evaluation engine on top of MapReduce, (iii) implementing a fully distributed evaluation engine on top of a parallel processing framework other than MapReduce, and finally (iv) based  ... 
doi:10.1007/s00778-014-0364-z fatcat:qyp6euinnvexxlliqe2wf45ona

RORS: Enhanced Rule-based OWL Reasoning on Spark [article]

Zhihui Liu and Zhiyong Feng and Xiaowang Zhang and Xin Wang and Guozheng Rao
2016 arXiv   pre-print
The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).  ...  Finally, we implement the new rule execution order on Spark in a prototype called RORS.  ...  Then Urbani proposed a MapReduce-based parallel reasoning system with OWL-Horst rules called WebPIE [18] . It can deal with large scale ontology on a distributed computing cluster.  ... 
arXiv:1605.02824v1 fatcat:j6sslb3e4fcf3hlnuiof3hya7q

BigDataGrapes D4.2 - Methods and Tools for Distributed Inference

Milena Yankova, Boyan SImeonov, Atanas Kiryakov, Vladimir Alexiev
2018 Zenodo  
The Final section is dedicated to state of the art with a standard theoretical approach to inference from descriptive logic standpoint, as well as related work in implementing those approaches.  ...  We address the main principles applied to data inference and different types of inference – rule-based, query-based, model-based and fuzzy inference – and their application in BigDataGrapes project.  ...  RELATED WORK WebPIE by VU Amsterdam "WebPIE (Web-scale Parallel Inference Engine) 5 is a MapReduce distributed RDFS/OWL inference engine written using the Hadoop framework.  ... 
doi:10.5281/zenodo.1481809 fatcat:7jkignzjnfdmxomknr5vjrwhhm

QueryPIE: Backward Reasoning for OWL Horst over Very Large Knowledge Bases [chapter]

Jacopo Urbani, Frank van Harmelen, Stefan Schlobach, Henri Bal
2011 Lecture Notes in Computer Science  
As a proof of concept, we have implemented a prototype called QueryPIE (Query Parallel Inference Engine), and we have tested its performance on different datasets of up to 1 billion triples.  ...  To the best of our knowledge, QueryPIE is the first reported backward-chaining reasoner for OWL Horst that efficiently scales to a billion triples.  ...  We have used the Hadoop MapReduce framework [4] and WebPIE [14] to create the data indexes and compute the sameAs closure and consolidation, which is a common practice among reasoners [14] .  ... 
doi:10.1007/978-3-642-25073-6_46 fatcat:o6zepb5y2rbznoo6rdtvy3mw7a
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