Matches in ScholarlyData for { ?s ?p ?o. }
- item-5 hasContent rik-van-de-walle.
- item-5 previous item-4.
- paper-36 type InProceedings.
- paper-36 label "KbQAS: A Knowledge-based QA System".
- paper-36 sameAs paper-36.
- paper-36 abstract "In this demo paper, we present the first ontology-based Vietnamese question answeringsystem KbQAS in which a knowledge acquisition approach for question analysis is integrated.".
- paper-36 hasAuthorList authorList.
- paper-36 isPartOf poster-demo-proceedings.
- paper-36 title "KbQAS: A Knowledge-based QA System".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent dat-quoc-nguyen.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent dai-quoc-nguyen.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent son-bao-pham.
- item-3 previous item-2.
- paper-37 type InProceedings.
- paper-37 label "PigSPARQL: A SPARQL Query Processing Baseline for Big Data".
- paper-37 sameAs paper-37.
- paper-37 abstract "In this paper, we discuss PigSPARQL, a competitive, yet easy to use, SPARQL query processing system based on MapReduce and thus intended for Big Data applications. Instead of a direct mapping, PigSPARQL uses the query language of Pig, a data analysis platform on top of Hadoop, as an intermediate layer between SPARQL and MapReduce. The additional level of abstraction makes our approach independent of the actual Hadoop version. Thus, it is automatically compatible to future changes of the Hadoop framework as they will be neutralized by the Pig layer and allows ad-hoc SPARQL query processing on large RDF graphs out of the box. In the paper we first revisit PigSPARQL and demonstrate PigSPARQL's gain of efficiency simply because switching from version Pig 0.5.0 to Pig 0.11.0. Because of this sustainability, PigSPARQL is an attractive long-term baseline for comparing various MapReduce based SPARQL implementations. This is underlined by PigSPARQL's competitiveness with existing systems, e.g. HadoopRDF.".
- paper-37 hasAuthorList authorList.
- paper-37 isPartOf poster-demo-proceedings.
- paper-37 title "PigSPARQL: A SPARQL Query Processing Baseline for Big Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasLastItem item-4.
- item-1 type ListItem.
- item-1 hasContent alexander-schaetzle.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent martin-przyjaciel-zablocki.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent thomas-hornung.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent georg-lausen.
- item-4 previous item-3.
- paper-38 type InProceedings.
- paper-38 label "Discoverability of SPARQL Endpoints in Linked Open Data".
- paper-38 sameAs paper-38.
- paper-38 abstract "Accessing Linked Open Data sources with query languages such as SPARQL provides more flexible possibilities than access based on derefencerable URIs only. However, discovering a SPARQL endpoint on the fly, given a URI, is not trivial. This paper provides a quantitative analysis on the automatic discoverability of SPARQL endpoints using different mechanisms.".
- paper-38 hasAuthorList authorList.
- paper-38 isPartOf poster-demo-proceedings.
- paper-38 title "Discoverability of SPARQL Endpoints in Linked Open Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasLastItem item-2.
- item-1 type ListItem.
- item-1 hasContent heiko-paulheim.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent sven-hertling.
- item-2 previous item-1.
- paper-39 type InProceedings.
- paper-39 label "Profiling of Linked Datasets using Structured Descriptions".
- paper-39 sameAs paper-39.
- paper-39 abstract "While there exists an increasingly large number of Linked Data, metadata about the content covered by individual datasets is sparse. In this paper, we introduce a processing pipeline to automatically assess, annotate and index available linked datasets. Given a minimal description of a dataset from the DataHub, the process produces a structured RDF-based description that includes information about its main topics. Additionally, the generated descriptions embed datasets into an interlinked graph of datasets based on shared topic vocabularies. We adopt and integrate techniques for Named Entity Recognition and automated data validation, providing a consistent workflow for dataset profiling and annotation. Finally, we validate the results obtained with our tool.".
- paper-39 hasAuthorList authorList.
- paper-39 isPartOf poster-demo-proceedings.
- paper-39 title "Profiling of Linked Datasets using Structured Descriptions".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
- authorList hasItem item-4.
- authorList hasItem item-5.
- authorList hasLastItem item-5.
- item-1 type ListItem.
- item-1 hasContent besnik-fetahu.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent stefan-dietze.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent bernardo-pereira-nunes.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent davide-taibi.
- item-4 next item-5.