Matches in ScholarlyData for { ?s ?p ?o. }
- 207 keyword "machine learning".
- 207 keyword "ontology integration".
- 207 title "Instance-based ontological knowledge acquisition".
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- item-1 hasContent lihua-zhao.
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- item-2 hasContent ryutaro-ichise.
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- 209 creator ahmed-abdelrahman.
- 209 creator aidan-hogan.
- 209 creator juergen-umbrich.
- 209 creator patrick-obyrne.
- 209 creator tobias-kaefer.
- 209 type InProceedings.
- 209 label "Observing Linked Data Dynamics".
- 209 sameAs 209.
- 209 abstract "In this paper we present the design of the Dynamic Linked Data Observatory: a long-term experiment to monitor the two-hop neighbourhood of a core set of one hundred thousand diverse Linked Data documents on a weekly basis. We present the methodology used for sampling the URIs to monitor, retrieving the documents, and further crawling part of the two-hop neighbourhood. Having now run this experiment for six months, we analyse the dynamics of the monitored documents over the data collected thus far. We look at the estimated lifespan of the core documents, how often they go on-line or off-line, how often they change; we further investigating domain-level trends. Next we look at changes within the RDF content of the core documents across the weekly snapshops, examining the elements (i.e., triples, subjects, predicates, objects, classes) that are most frequently added or removed. Thereafter, we look at how the links between dereferenceable documents evolves over time in the two-hop neighbourhood.".
- 209 hasAuthorList authorList.
- 209 isPartOf proceedings.
- 209 keyword "dynamics".
- 209 keyword "linked data".
- 209 keyword "web of data".
- 209 title "Observing Linked Data Dynamics".
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- item-1 type ListItem.
- item-1 hasContent tobias-kaefer.
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- item-2 hasContent ahmed-abdelrahman.
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- item-3 hasContent juergen-umbrich.
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- item-4 hasContent patrick-obyrne.
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- item-5 hasContent aidan-hogan.
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- 219 creator patrik-schneider.
- 219 creator thomas-eiter.
- 219 creator thomas-krennwallner.
- 219 type InProceedings.
- 219 label "Lightweight Spatial Conjunctive Query Answering using Keywords".
- 219 sameAs 219.
- 219 abstract "With the advent of publicly available geospatial data, ontology-based data access (OBDA) over spatial data has gained increasing interest. Spatio-relational DBMSs are used to implement geographic information systems (GIS) and are fit to manage large amounts of data and geographic objects such as points, lines, polygons, etc. In this paper, we extend the Description Logic DL-Lite with spatial objects and show how to answer spatial conjunctive queries (SCQs) over ontologies---that is, conjunctive queries with point-set topological relations such as next and within---expressed in this language. The goal of this extension is to enable an off-the-shelf use of spatio-relational DBMSs to answer SCQs using rewriting techniques, where data sources and geographic objects are stored in a database and spatial conjunctive queries are rewritten to SQL statements with spatial functions. Furthermore, we consider keyword-based querying over spatial OBDA data sources; as ordinary web users are not familiar with formal query languages and the less with SCQ, this is a reasonable and common way to express a search intention. We show how to map queries expressed as simple lists of keywords that describe objects of interest to SCQs, using a meta-model for completing the SCQs with spatial aspects. We have implemented our lightweight approach to spatial OBDA in a prototype and show initial experimental results using data sources such as Open Street Maps and Open Government Data Vienna from an associated project. We show that for real-world scenarios, practical queries are expressible under meta-model completion, and that query answering is computationally feasible.".
- 219 hasAuthorList authorList.
- 219 isPartOf proceedings.
- 219 keyword "Keyword-based Query Answering".
- 219 keyword "Lightweight reasoning".
- 219 keyword "Ontology-Based Data Access".
- 219 keyword "Spatial Query Answering".
- 219 title "Lightweight Spatial Conjunctive Query Answering using Keywords".
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- item-1 hasContent thomas-eiter.
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- item-2 hasContent thomas-krennwallner.
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- item-3 hasContent patrik-schneider.
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- 34 creator felix-naumann.
- 34 creator johannes-lorey.
- 34 type InProceedings.
- 34 label "Detecting SPARQL Query Templates for Data Prefetching".
- 34 sameAs 34.
- 34 abstract "Publicly available Linked Data repositories provide a multitude of information. By utilizing SPARQL, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the SPARQL endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world SPARQL query logs.".
- 34 hasAuthorList authorList.
- 34 isPartOf proceedings.
- 34 keyword "Query Mining".
- 34 keyword "SPARQL Query Profiling".
- 34 keyword "Semantic Caching".
- 34 title "Detecting SPARQL Query Templates for Data Prefetching".
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