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- item-1 hasContent mohamed-ben-ellefi.
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- item-2 hasContent zohra-bellahsene.
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- 45 creator blake-regalia.
- 45 creator krzysztof-janowicz.
- 45 creator song-gao.
- 45 type InProceedings.
- 45 label "VOLT: A Provenance-Producing, Transparent SPARQL Proxy for the On-Demand Computation of Linked Data and its Application to Spatiotemporally Dependent Data".
- 45 sameAs 45.
- 45 abstract "Powered by Semantic Web technologies the Linked Data paradigm aims at weaving a globally interconnected graph of raw data that transforms the ways we publish, retrieve, share, reuse, and integrate data from a variety of distributed and heterogeneous sources. In practice, however, this vision faces substantial challenges with respect to data quality, coverage, and longevity, the amount of background knowledge required to query distant data, the reproducibility of query results and their derived (scientific) findings, and the lack of computational capabilities required for many tasks. One key issues underlying these challenges is the trade-off between storing data and computing them. Intuitively, data that is derived from already stored data, changes frequently in space and time, or is the result of some workflow or procedure, should be compute. However, this functionality is not readily available on the Linked Data cloud and its technology stack. In this work, we introduce a proxy that can transparently run on top of arbitrary SPARQL endpoints and enables the on-demand computation of Linked Data together with the provenance information required to understand how they were derived. While our work can be generalized to multiple domains, we focus on two geographic use case to showcase the proxy's capabilities.".
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- 45 title "VOLT: A Provenance-Producing, Transparent SPARQL Proxy for the On-Demand Computation of Linked Data and its Application to Spatiotemporally Dependent Data".
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- item-1 hasContent blake-regalia.
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- 5 creator heiko-paulheim.
- 5 creator heiner-stuckenschmidt.
- 5 type InProceedings.
- 5 label "Efficient Approximation of Ontology Reasoning by Machine Learning".
- 5 sameAs 5.
- 5 abstract "Ontology reasoning is typically a computationally intensive operation. While soundness and completeness of results is required in some use cases, for many others, a sensible trade-off between computation efforts and correctness of results makes more sense. In this paper, we show that it is possible to approximate the results delivered by a reasoner by observing an actual reasoner and training a machine learning model which approximates the behavior of that reasoner. On four different datasets, we show that such learned models constantly achieve an accuracy above 95% at less than 2% of the runtime of a reasoner, using a decision tree with no more than 20 inner nodes. For example, this allows for checking the consistency of all relational assertions in DBpedia in less than 90 minutes, compared to 15 days required by a state of the art ontology reasoner. ".
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- 5 title "Efficient Approximation of Ontology Reasoning by Machine Learning".
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- item-1 hasContent heiko-paulheim.
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- 51 creator christos-tryfonopoulos.
- 51 creator lefteris-zervakis.
- 51 creator manolis-koubarakis.
- 51 creator spiros-skiadopoulos.
- 51 type InProceedings.
- 51 label "Full-text Support for Publish/Subscribe Ontology Systems".
- 51 sameAs 51.
- 51 abstract "In this work, we envision a publish/subscribe ontology system that is able to index large numbers of expressive continuous queries and filter them against RDF data that arrive in a streaming fashion. To this end, we propose a SPARQL extension that supports the creation of full-text continuous queries and propose a family of main-memory query indexing algorithms which perform matching at low complexity and minimal filtering time. We experimentally compare our approach against a state-of-the-art competitor (extended to handle indexing of full-text queries) both on structural and full-text tasks using real-world data. Our approach proves two orders of magnitude faster than the competitor in all types of filtering tasks.".
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- 51 title "Full-text Support for Publish/Subscribe Ontology Systems".
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- item-1 hasContent lefteris-zervakis.
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- item-2 hasContent christos-tryfonopoulos.
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- 7 creator antoine-zimmermann.