Matches in ScholarlyData for { ?s ?p ?o. }
- item-6 previous item-5.
- paper-49 type InProceedings.
- paper-49 label "Explaining Clusters with Inductive Logic Programming and Linked Data".
- paper-49 sameAs paper-49.
- paper-49 abstract "Knowledge Discovery consists in discovering hidden regulari- ties in large amount of data using data mining techniques. The obtained patterns require an interpretation that is usually achieved using some background knowledge given by experts from several domains. On the other hand, the rise of Linked Data has increased the number of con- nected cross-disciplinary knowledge, in the form of RDF datasets, classes and relationships. Here we show how Linked Data can be used in an Inductive Logic Programming process, where they provide background knowledge for finding hypotheses regarding the unrevealed connections between items of a cluster. By using an example with clusters of books, we show how different Linked Data sources can be used to automatically generate rules giving an underlying explanation to such clusters.".
- paper-49 hasAuthorList authorList.
- paper-49 isPartOf poster-demo-proceedings.
- paper-49 title "Explaining Clusters with Inductive Logic Programming and Linked Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
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- authorList hasItem item-3.
- authorList hasLastItem item-3.
- item-1 type ListItem.
- item-1 hasContent ilaria-tiddi.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent mathieu-daquin.
- item-2 next item-3.
- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent enrico-motta.
- item-3 previous item-2.
- paper-50 type InProceedings.
- paper-50 label "Exploring Linked Open Data with Tag Clouds".
- paper-50 sameAs paper-50.
- paper-50 abstract "In this paper we present the contextual tag cloud system: a novel application that helps users explore a large scale RDF dataset. Unlike folksonomy tags used in most traditional tag clouds, the tags in our system are ontological terms (classes and properties), and a user can construct a context with a set of tags that defines a subset of instances. Then in the contextual tag cloud, the font size of each tag depends on the number of instances that are associated with that tag and all tags in the context. Each contextual tag cloud serves as a summary of the distribution of relevant data, and by changing the context, the user can quickly gain an understanding of patterns in the data. Furthermore, the user can choose to include RDFS taxonomic and/or domain/range entailment in the calculations of tag sizes, thereby understanding the impact of semantics on the data. The system runs on the BTC2012 dataset with more than 1.4 billion triples from which we extract over 380,000 tags. Several scalability challenges must be overcome in order to achieve a responsive interface.".
- paper-50 hasAuthorList authorList.
- paper-50 isPartOf poster-demo-proceedings.
- paper-50 title "Exploring Linked Open Data with Tag Clouds".
- authorList type List.
- authorList hasFirstItem item-1.
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- authorList hasItem item-4.
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- item-1 type ListItem.
- item-1 hasContent xingjian-zhang.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent dezhao-song.
- item-2 next item-3.
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- item-3 type ListItem.
- item-3 hasContent sambhawa-priya.
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- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent jeff-heflin.
- item-4 previous item-3.
- paper-51 type InProceedings.
- paper-51 label "Publishing Data from the Smithsonian American Art Museum as Linked Open Data".
- paper-51 sameAs paper-51.
- paper-51 abstract "Museums around the world have built databases with meta-data about millions of objects, their history, the people who created them, and the entities they represent. This data is stored in proprietary databases and is not readily available for use. Recently, museums embraced the Semantic Web as a means to make this data available to the world, but the experience so far shows that publishing museum data to the linked data cloud is difficult: the databases are large and complex, the information is richly structured and varies from museum to museum, and it is difficult to link the data to other datasets. We have been collaborating with the Smithsonian American Art Museum to create a set of tools that allow museums and other cultural heritage institutions to publish their data as Linked Open Data. In this demonstration we will show the end-to-end process of starting with the original source data, modeling the data with respect to a ontology of cultural heritage data, linking the data to DBpedia, and then publishing the information as Linked Open Data. Video: http://youtu.be/1Vaytr09H1w".
- paper-51 hasAuthorList authorList.
- paper-51 isPartOf poster-demo-proceedings.
- paper-51 title "Publishing Data from the Smithsonian American Art Museum as Linked Open Data".
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- authorList hasFirstItem item-1.
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- item-1 type ListItem.
- item-1 hasContent craig-a-knoblock.
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- item-2 type ListItem.
- item-2 hasContent pedro-szekely.
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- item-3 type ListItem.
- item-3 hasContent shubham-gupta.
- item-3 next item-4.
- item-3 previous item-2.
- item-4 type ListItem.
- item-4 hasContent animesh-manglik.
- item-4 next item-5.
- item-4 previous item-3.
- item-5 type ListItem.
- item-5 hasContent ruben-verborgh.
- item-5 next item-6.
- item-5 previous item-4.
- item-6 type ListItem.
- item-6 hasContent fengyu-yang.
- item-6 previous item-5.
- paper-52 type InProceedings.
- paper-52 label "GRAPHIUM: Visualizing Performance of Graph and RDF Engines on Linked Data".
- paper-52 sameAs paper-52.
- paper-52 abstract "We present GRAPHIUM a tool to visualize trends and patterns in the performance of existing graph and RDF engines. We will demonstrate GRAPHIUM and attendees will be able to observe and analyze the performance exhibited by Neo4j, DEX, HypergraphDB and RDF-3x when core graph-based and mining tasks are run against a variety of benchmark of graphs of diverse characteristics.".
- paper-52 hasAuthorList authorList.
- paper-52 isPartOf poster-demo-proceedings.
- paper-52 title "GRAPHIUM: Visualizing Performance of Graph and RDF Engines on Linked Data".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.