Matches in ScholarlyData for { ?s ?p ?o. }
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- item-4 hasContent bei-xu.
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- 38 creator albert-zuendorf.
- 38 creator christopher-brink.
- 38 creator martin-peters.
- 38 creator sabine-sachweh.
- 38 type InProceedings.
- 38 label "Scaling Parallel Rule-based Reasoning".
- 38 sameAs 38.
- 38 abstract "Using semantic technologies the materialization of implicit given facts that can be derived from a dataset is an important task performed by a reasoner. With respect to the answering time for queries and the growing amount of available data, scaleable solutions that are able to process large datasets are needed. In previous work we described a rule-based reasoner implementation that uses massively parallel hardware to derive new facts based on a given set of rules. This implementation was limited by the size of processable input data as well as on the number of used parallel hardware devices. In this paper we introduce further concepts for a workload partitioning and distribution to overcome this limitations. Based on the introduced concepts, additional levels of parallelization can be proposed that benefit from the use of multiple parallel devices. Furthermore, we introduce a concept to reduce the amount of invalid triple derivations like duplicates. We evaluate our concepts by applying different rulesets to the real-world DBPedia dataset as well as to the synthetic Lehigh University benchmark ontology (LUBM) with up to 1.1 billion triples. The evaluation shows that our implementation scales in a linear way and outperforms current state of the art reasoner with respect to the throughput achieved on a single computing node.".
- 38 hasAuthorList authorList.
- 38 isPartOf proceedings.
- 38 keyword "GPU".
- 38 keyword "RETE algorithm".
- 38 keyword "parallel".
- 38 keyword "rule-based reasoning".
- 38 keyword "scaleable reasoning".
- 38 title "Scaling Parallel Rule-based Reasoning".
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- item-1 hasContent martin-peters.
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- item-2 hasContent christopher-brink.
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- item-3 hasContent sabine-sachweh.
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- item-4 hasContent albert-zuendorf.
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- 39 creator dominik-wienand.
- 39 creator heiko-paulheim.
- 39 type InProceedings.
- 39 label "Detecting Incorrect Numerical Data in DBpedia".
- 39 sameAs 39.
- 39 abstract "DBpedia is a central hub of Linked Open Data (LOD). Being based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping methods. Our approach reaches 87% precision, and has lead to the identification of 11 systematic errors in the DBpedia extraction framework.".
- 39 hasAuthorList authorList.
- 39 isPartOf proceedings.
- 39 keyword "Clustering".
- 39 keyword "DBpedia".
- 39 keyword "Data Quality".
- 39 keyword "Error Detection".
- 39 keyword "Linked Open Data".
- 39 keyword "Outlier Detection".
- 39 title "Detecting Incorrect Numerical Data in DBpedia".
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- item-1 hasContent dominik-wienand.
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- item-2 hasContent heiko-paulheim.
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- 43 creator christian-gottron.
- 43 creator thomas-gottron.
- 43 type InProceedings.
- 43 label "Perplexity of Index Models over Evolving Linked Data".
- 43 sameAs 43.
- 43 abstract "In this paper we analyse the sensitivity of twelve prototypical linked data index models towards evolving data. Thus, we consider the reliability and accuracy of results obtained from an index in scenarios where the original data has changed after having been indexed. Our analysis is based on empirical ob- servations over real world data covering a time span of more than one year. The quality of the index models is evaluated w.r.t. their ability to give reliable esti- mations of the distribution of the indexed data. To this end we use metrics such as perplexity, cross-entropy and Kullback-Leibler divergence. Our experiments show that all considered index models are affected by the evolution of data, but to different degrees and in different ways. We also make the interesting observa- tion that index models based on schema information seem to be more stable than index models based on triples or context information.".
- 43 hasAuthorList authorList.
- 43 isPartOf proceedings.
- 43 keyword "density estimations".
- 43 keyword "evaluation".
- 43 keyword "evolving data".
- 43 keyword "index structures".
- 43 keyword "linked data".
- 43 keyword "perplexity".
- 43 title "Perplexity of Index Models over Evolving Linked Data".
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- item-1 hasContent thomas-gottron.
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- item-2 hasContent christian-gottron.
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- 50 creator axel-cyrille-ngonga-ngomo.
- 50 creator muhammad-saleem.
- 50 type InProceedings.
- 50 label "HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation".
- 50 sameAs 50.
- 50 abstract "Efficient federated query processing is of significant importance to tame the large amount of data available on the Web of Data. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. This work presents HiBISCuS, a novel hypergraph-based source selection approach to federated SPARQL querying. Our approach can be directly combined with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We extend three well-known SPARQL query federation engines -- DARQ, SPLENDID, and FedX -- with HiBISCus and compare our extensions with the original approaches on FedBench. Our evaluation shows that HiBISCuS can efficiently reduce the total number of sources selected without losing the recall. Moreover, our approach significantly reduces the execution time of the selected engines on most of the benchmark queries.".
- 50 hasAuthorList authorList.
- 50 isPartOf proceedings.