Matches in ScholarlyData for { ?s ?p ?o. }
- 6 keyword "Active Learning".
- 6 keyword "Query Interfaces".
- 6 keyword "SPARQL".
- 6 keyword "Supervised Learning".
- 6 title "AutoSPARQL: Let Users Query Your Knowledge Base".
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- authorList hasItem item-2.
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- item-1 type ListItem.
- item-1 hasContent jens-lehmann.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent lorenz-buehmann.
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- 8 creator amit-sheth.
- 8 creator kunal-verma.
- 8 creator mariana-damova.
- 8 creator pascal-hitzler.
- 8 creator peter-yeh.
- 8 creator prateek-jain.
- 8 creator reymonrod-vasquez.
- 8 type InProceedings.
- 8 label "Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton".
- 8 sameAs 8.
- 8 abstract "The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies -- in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) -- created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.".
- 8 hasAuthorList authorList.
- 8 isPartOf proceedings.
- 8 keyword "Contextual Ontology Alignment".
- 8 keyword "Linked Open Data".
- 8 keyword "Schema Mapping".
- 8 title "Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton".
- authorList type List.
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- authorList hasItem item-7.
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- item-1 type ListItem.
- item-1 hasContent prateek-jain.
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- item-2 type ListItem.
- item-2 hasContent peter-yeh.
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- item-3 type ListItem.
- item-3 hasContent kunal-verma.
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- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent reymonrod-vasquez.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent mariana-damova.
- item-5 next item-6.
- item-5 previous item-4.
- item-6 type ListItem.
- item-6 hasContent pascal-hitzler.
- item-6 next item-7.
- item-6 previous item-5.
- item-7 type ListItem.
- item-7 hasContent amit-sheth.
- item-7 previous item-6.
- 11 creator guenter-ladwig.
- 11 creator thanh-tran.
- 11 type InProceedings.
- 11 label "SIHJoin: Querying Remote and Local Linked Data".
- 11 sameAs 11.
- 11 abstract "The amount of Linked Data is increasing steadily. Optimized top-down Linked Data query processing based on complete knowledge about all sources, bottom-up processing based on run-time discovery of sources as well as a mixed strategy that combines them has been proposed. One particular problem with Linked Data processing is that the heterogeneity of the sources and access options lead to varying input latency, rendering the application of blocking join operators infea- sible. Previous work partially address this by proposing a non-blocking iterator-based operator and another one based on symmetric-hash join. In this paper, we propose detailed cost models for these two operators to systematically compare them, and to allow for query optimization. Further, we propose a novel operator called the Symmetric Index Hash Join to address one open problem of Linked Data query processing: to query not only remote but also local Linked Data. We perform experiments on real-world datasets to compare our approach against the iterator-based baseline, and create a synthetic dataset to more systematically analyze the impacts of the individual components captured by the proposed cost models.".
- 11 hasAuthorList authorList.
- 11 isPartOf proceedings.
- 11 keyword "data streams".
- 11 keyword "join operator".
- 11 keyword "linked data".
- 11 keyword "query optimization".
- 11 keyword "query processing".
- 11 title "SIHJoin: Querying Remote and Local Linked Data".
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- item-1 type ListItem.
- item-1 hasContent guenter-ladwig.
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- item-2 hasContent thanh-tran.
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- 28 creator olaf-hartig.
- 28 type InProceedings.
- 28 label "Zero-Knowledge Query Planning for an Iterator Implementation of Link Traversal Based Query Execution".
- 28 sameAs 28.
- 28 abstract "Link traversal based query execution is a new query execution paradigm for the Web of Data. This approach allows the execution engine to discover potentially relevant data during the query execution and, thus, enables users to tap the full potential of the Web. In earlier work we propose to implement the idea of link traversal based query execution using a synchronous pipeline of iterators. While this idea allows for an easy and efficient implementation, it introduces restrictions that cause less comprehensive result sets. In this paper we address this limitation. We analyze the restrictions and discuss how the evaluation order of a query may affect result set size and query execution costs. To identify a suitable order, we propose a heuristic for our scenario where no a-priory information about relevant data sources is present. We evaluate the effectiveness of this heuristic by executing real-world queries over the Web of Data.".
- 28 hasAuthorList authorList.