Matches in ScholarlyData for { ?s ?p ?o. }
- 18 keyword "Node-Labelling Scheme".
- 18 keyword "Semi-Structured Data".
- 18 title "A Node Indexing Scheme for Web Entity Retrieval".
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- authorList hasFirstItem item-1.
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- item-1 type ListItem.
- item-1 hasContent renaud-delbru.
- item-1 next item-2.
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- item-2 hasContent nickolai-toupikov.
- item-2 next item-3.
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- item-3 type ListItem.
- item-3 hasContent michele-catasta.
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- item-4 type ListItem.
- item-4 hasContent giovanni-tummarello.
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- 28 creator isabel-rojas.
- 28 creator mohammad-s-mir.
- 28 creator steffen-staab.
- 28 type InProceedings.
- 28 label "An Unsupervised Approach for Acquiring Ontologies and RDF Data from Online Life Science Databases".
- 28 sameAs 28.
- 28 abstract "in the Linked Open Data cloud one of the largest data sets, comprising of 2.5 billion triples, is derived from the Life Science domain. Yet this represents a small fraction of the total number of publicly available data sources on the Web. In this paper, we briefly describe past attempts to transform specific Life Science sources from a plethora of open as well as proprietary formats into RDF data. In particular, we identify and tackle two bottlenecks in current practice: Acquiring ontologies to formally describe these data and creating RDFizer programs to convert data from legacy formats into RDF. We propose an unsupervised method, based on transformation rules, for performing these two key tasks, which makes use of our previous work on unsupervised wrapper induction for extracting labelled data from complete Life Science Web sites. We apply our approach to 13 real-world online Life Science databases. The learned ontologies are evaluated by domain experts as well as against gold standard ontologies. Furthermore, we compare the learned ontologies against ontologies that are “lifted” directly from the underlying relational schema using an existing unsupervised approach. Finally, we apply our approach to three online databases to extract RDF data. Our results indicate that this approach can be used to bootstrap and speed up the migration of life science data into the Linked Open Data cloud.".
- 28 hasAuthorList authorList.
- 28 isPartOf proceedings.
- 28 keyword "Life Sciences".
- 28 keyword "Ontology Learning".
- 28 keyword "RDFizing".
- 28 keyword "Semantic Web".
- 28 title "An Unsupervised Approach for Acquiring Ontologies and RDF Data from Online Life Science Databases".
- authorList type List.
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- item-1 hasContent mohammad-s-mir.
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- item-2 hasContent steffen-staab.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent isabel-rojas.
- item-3 previous item-2.
- 29 creator guenter-ladwig.
- 29 creator haofen-wang.
- 29 creator lei-zhang-1.
- 29 creator thanh-tran.
- 29 type InProceedings.
- 29 label "Combining Keyword Translation with Structured Query Answering for Efficient Keyword Search".
- 29 sameAs 29.
- 29 abstract "Keyword search has been regarded as an intuitive paradigm for searching not only documents but also data, especially when the users are not familiar with the data and the query language. Two types of approaches can be distinguished. Answers to keywords can be computed by searching for matching subgraphs directly in the data. The alternative to this is keyword translation, which is based on searching the data schema for matching join graphs, which are then translated to queries. Answering these queries is performed in the later stage. While clear advantages have been shown for the approaches based on query translation, we observe that processing done during query translation has some overlaps with the processing needed for query answering. Thus, we propose a tight integration of query translation with query answering. Instead of using the schema, we employ a bisimulation-based structure index graph. Searching this index for matching subgraphs results not only in queries, but also candidate answers. We propose a set of algorithms which allow for an incremental process where intermediate results computed during query translation can be reused for subsequent query answering. In experiments, we show that this integrated approach consistently outperforms the state of the art.".
- 29 hasAuthorList authorList.
- 29 isPartOf proceedings.
- 29 keyword "keyword search".
- 29 keyword "query processing".
- 29 keyword "query translation".
- 29 keyword "rdf data".
- 29 keyword "scalability".
- 29 title "Combining Keyword Translation with Structured Query Answering for Efficient Keyword Search".
- authorList type List.
- authorList hasFirstItem item-1.
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- item-1 type ListItem.
- item-1 hasContent guenter-ladwig.
- item-1 next item-2.
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- item-2 hasContent thanh-tran.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent lei-zhang-1.
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- item-4 type ListItem.
- item-4 hasContent haofen-wang.
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- 30 creator raphael-doehring.
- 30 creator sebastian-dietzold.
- 30 creator soeren-auer.
- 30 type InProceedings.
- 30 label "LESS - Template-based Syndication and Presentation of Linked Data for End-users".
- 30 sameAs 30.
- 30 abstract "Recently, the publishing of structured, semantic information as linked data has gained quite some momentum. For ordinary users on the Internet, however, this information is not yet very visible and (re-) usable. With LESS we present an end-to-end approach for the syndication and use of linked data based on the definition of templates for linked data resources and SPARQL query results. Such syndication templates are edited, published and shared by using a collaborative Web platform. Templates for common types of entities can then be combined with specific, linked data resources or SPARQL query results and integrated into a wide range of applications, such as personal homepages, blogs/wikis, mobile widgets etc. In order to improve reliability and performance of linked data, LESS caches versions either for a certain time span or for the case of inaccessibility of the original source. LESS supports the integration of information from various sources as well as any text-based output formats. This allows not only to generate HTML, but also diagrams, RSS feeds or even complete data mashups without any programming involved.".
- 30 hasAuthorList authorList.
- 30 isPartOf proceedings.