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Showing 11 pages using this property.
|RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems +||No data available now. +|
|Relational.OWL - A Data and Schema Representation Format Based on OWL +||No data available now. +|
|SERIMI – Resource Description Similarity, RDF Instance Matching and Interlinking +||No data available now. +|
|SLINT: A Schema-Independent Linked Data Interlinking System +||SLINT system totally outperforms the others on both precision and recall. AgreementMaker has a competitive precision with SLINT on dataset D3 but this system is much lower in recall. Zhishi.Links results on dataset D3 are very high, but the F1 score of SLINT is still 0.05 higher in overall. +|
|SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions +||AliBaba and DARQ fail to return results for six out of the 14 queries for different reasons. AliBaba generates malformed sub queries for CD3, CD5, LS6, and LS7. DARQ can not handle the unbound predicate in CD1 and LS2. For CD3 and CD5 DARQ opens too many connections to GeoNames. All other unsuccessful queries take longer than the time limit of five minutes. Overall, FedX has the best query evaluation performance. The reason is its novel and efficient query execution based on block transmission of result tuples and parallelization of joins. However, there is only a significant difference between FedX and SPLENDID for CD6, CD7, LS3, LS5-7. For the other queries SPLENDID is close to FedX and for CD3 and CD4 even slightly faster, which indicates that SPLENDID, indeed, generates better query execution plans. +|
|Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles +||5 out of the 9 researchers immediately started with wellknown standardWeb search engines to explore the given topic. They tried to use several variations of keywords from the questions, e.g., “Federated Query Engines”, “SPARQL Federation”, etc. They also used digital libraries and scientific metadata services, e.g., ACM DL or Microsoft Academic Search, following the same approach and sometimes using advanced search options and filters. However, the retrieved results were either out of scope for the question but more related to the search keywords. Overall, 8 researchers found it difficult to collect information and reach a conclusive overview of the research topics or related work using current methods. Six of the participants pointed out that for some of the overview questions, search engines were as good as the proposed system particularly when the framework name is part of the search keyword. They all agreed that for complicated questions our SemSur approach outperformed any existing approach/tool. Seven participants agreed that our system would be helpful for both new and experienced researchers. Two-thirds of them strongly agreed that the time and effort they spent to find such information using our system in comparison to other traditional ways is relatively low. Finally, 100% of the participants would like to use SemSur approach in their further research for studying the literature of a research topic or writing a survey article. Since the results of queries were shown to the participants in table view, the main feedback from all participants about possible improvements was to provide a better way of data representation. +|
|Towards a Knowledge Graph for Science +||No data available now. +|
|Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web +||No data available now. +|
|Updating Relational Data via SPARQL/Update +||No data available now. +|
|Use of OWL and SWRL for Semantic Relational Database Translation +||No data available now. +|
|Zhishi.links Results for OAEI 2011 +||No data available now. +|