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Scalable and Parallel Reasoning in the Semantic Web [chapter]

Jacopo Urbani
2010 Lecture Notes in Computer Science  
The current state of the art regarding scalable reasoning consists of programs that run on a single machine.  ...  We propose a distributed approach that overcomes these limitations and we sketch a research methodology.  ...  Some of the approaches here presented have good scalability but on a weak logic ( [12] ), while others implement a complex logic like OWL but do not appear to scale to a very large size ( [7] , [3]  ... 
doi:10.1007/978-3-642-13489-0_49 fatcat:mkhixjupf5ewda45z4amky4cry

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)  
However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data.  ...  In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the  ...  "III: Small: TROn -Tractable Reasoning with Ontologies."  ... 
doi:10.1017/s0269888918000255 fatcat:bergc5uphbceznigppektgvzrm

Scalable integration and processing of linked data

Andreas Harth, Aidan Hogan, Spyros Kotoulas, Jacopo Urbani
2011 Proceedings of the 20th international conference companion on World wide web - WWW '11  
Web data through reasoning.  ...  As such, the tutorial will focus on Linked Data publishing and related Semantic Web technologies, introducing scalable techniques for crawling, indexing and automatically integrating structured heterogeneous  ...  Scalable Distributed Reasoning over Map-Reduce This session presents scalable distributed reasoning using the MapReduce distribution framework, enabling high performance over a cluster of commodity hardware  ... 
doi:10.1145/1963192.1963318 dblp:conf/www/HarthHKU11 fatcat:63pzk35ga5b4hegnyf3dcmd2ey

Very Large Scale OWL Reasoning through Distributed Computation [chapter]

Raghava Mutharaju
2012 Lecture Notes in Computer Science  
In our work, we study several distributed classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile).  ...  The memory requirements for a reasoner are already quite high, and considering the ever increasing size of the data to be processed and the goal of making reasoning Web scale, this assumption becomes overly  ...  This work was supported by the National Science Foundation under award 1017225 "III: Small: TROn -Tractable Reasoning with Ontologies."  ... 
doi:10.1007/978-3-642-35173-0_30 fatcat:lfaqfmd6bbdgpkfepzykvv2nyu

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  
In previous work we have shown that the MapReduce framework for distributed computation can be deployed for highly scalable inference over RDF graphs under the RDF Schema semantics.  ...  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

BigDataGrapes D4.2 - Methods and Tools for Distributed Inference

Milena Yankova, Boyan SImeonov, Atanas Kiryakov, Vladimir Alexiev
2018 Zenodo  
The second challenge is how the actual inference over distributed sources can be performed and implemented.  ...  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.  ...  Parallel reasoning and distributed reasoning are considered to be essential for Web-scale reasoning to improve scalability.  ... 
doi:10.5281/zenodo.1481809 fatcat:7jkignzjnfdmxomknr5vjrwhhm

Integrating Linked Data through RDFS and OWL: Some Lessons Learnt [chapter]

Aidan Hogan
2011 Lecture Notes in Computer Science  
In this paper, we summarise the lessons learnt from the PhD Thesis Exploiting RDFS and OWL for Integrating Heterogeneous, Large-Scale, Linked Data Corpora where we looked at three use-cases for reasoning  ...  We summarise how we overcome the challenges of scalability and robustness faced when reasoning over Linked Data.  ...  Acknowledgements: I would like to thank to my supervisor Axel Polleres and my examiners Stefan Decker and James Hendler for their support.  ... 
doi:10.1007/978-3-642-23580-1_20 fatcat:bwpwkumafbcsbfemlfez255yxu

Scalable Distributed Reasoning Using MapReduce [chapter]

Jacopo Urbani, Spyros Kotoulas, Eyal Oren, Frank van Harmelen
2009 Lecture Notes in Computer Science  
We address the problem of scalable distributed reasoning, proposing a technique for materialising the closure of an RDF graph based on MapReduce.  ...  We have implemented our approach on top of Hadoop and deployed it on a compute cluster of up to 64 commodity machines.  ...  A remaining challenge is to apply the same techniques successfully to OWL-Horst reasoning. Our first experiments have shown this to be more challenging.  ... 
doi:10.1007/978-3-642-04930-9_40 fatcat:djqm7scjibfp5cjbebilb5ualu

The Maturing Semantic Web: Lessons in Web-Scale Knowledge Representation [chapter]

Mark Greaves
2009 Lecture Notes in Computer Science  
Innsbruck and Vrije University Amsterdam, plus 12 partners  Goals of LarKC -Scaling to Infinity -A platform for massive distributed incomplete reasoning -Remove the scalability barriers of currently existing  ...  Innsbruck and Vrije University Amsterdam, plus 12 partners  Goals of LarKC -Scaling to Infinity -A platform for massive distributed incomplete reasoning -Remove the scalability barriers of currently existing  ... 
doi:10.1007/978-3-642-03079-6_1 fatcat:bgkvdqaj3ndrtmz56qwqi4mbse

Querying Heterogeneous Datasets on the Linked Data Web: Challenges, Approaches, and Trends

Andre Freitas, Edward Curry, Joao Gabriel Oliveira, Sean O'Riain
2012 IEEE Internet Computing  
We thank the reviewers and editors for their careful and valuable feedback.  ...  The Scalable Authoritative OWL Reasoner (SAOR) provides an RDFS and partial Web Ontology Language (OWL) reasoning engine to address scalability issues. 4 SAOR applies reasoning only on dataset fragments  ...  Approaches used for querying siloed databases fail at Web-scale because users don't have an a priori understanding of all the available datasets.  ... 
doi:10.1109/mic.2011.141 fatcat:kovzfmfjvfhpdeavhxcb3pueba

A Linked Data Reasoner in the Cloud [chapter]

Jules Chevalier
2013 Lecture Notes in Computer Science  
Nevertheless, current methods for reasoning over linked data are well suited for small to medium datasets, and they fail at reaching the scale of the Web of Data.  ...  In this PhD thesis, we are interested in how distributed computing in the Cloud can help a linked data reasoner to scale. We present in this paper the early state of this thesis.  ...  That is why we need more powerful reasoners, scalable enough to make inference over very large datasets.  ... 
doi:10.1007/978-3-642-38288-8_59 fatcat:3ymssiz4zfdy5ngdeaoitzhdji

Optimizing Enterprise-Scale OWL 2 RL Reasoning in a Relational Database System [chapter]

Vladimir Kolovski, Zhe Wu, George Eadon
2010 Lecture Notes in Computer Science  
OWL 2 RL was standardized as a less expressive but scalable subset of OWL 2 that allows a forward-chaining implementation.  ...  Finally, to handle the increasing number of owl:sameAs relationships present in Semantic Web datasets, we have provided a hybrid in-memory/disk based approach to efficiently compute compact equivalence  ...  We thank Jay Banerjee for his continuous support and suggestions. We thank Tim Cline for his help in providing server-class machines S3 and S4.  ... 
doi:10.1007/978-3-642-17746-0_28 fatcat:qpfkoxfbs5afdb22qipuqw2ipa

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  
The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning.  ...  On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance.  ...  This is a limiting factor for performance and scalability. A distributed approach to reasoning is potentially more scalable because its performance can be improved by adding more computational nodes.  ... 
doi:10.1016/j.websem.2011.05.004 fatcat:7hifunbllrf25fastlqnkt6bvi

Can we ever catch up with the Web?

Axel Polleres, Aidan Hogan, Andreas Harth, Stefan Decker
2010 Semantic Web Journal  
Standards such as OWL 2, RIF and SPARQL 1.1 shall allow us to reason with and ask complex structured queries on this data, but still they do not play together smoothly and robustly enough to cope with  ...  By efforts such as the Linking Open Data initiative, we finally find ourselves at the edge of a Web of Data becoming reality.  ...  Although distribution is not, per-se, a 'magic bullet' -a task that is not scalable on one machine will likely not scale either over multiple -appropriate parallel execution of data processing, indexing  ... 
doi:10.3233/sw-2010-0016 fatcat:nul7t74wwjcx7lhwtztt7dk5na

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  
The process is implemented in a scalable and distributed platform to process Big Data and some results are discussed.  ...  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.  ...  Rule-based reasoning approach allows the parallelization and distribution of work by large clusters of inexpensive machines by programming models for processing and generating large data sets as Mapreduce  ... 
doi:10.1145/2928294.2928301 dblp:conf/sigmod/PeixotoHCBS16 fatcat:f6ina2i74rfmrpzk76z45wmrnu
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