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List of results
- Adaptive Integration of Distributed Semantic Web Data + (No data available now.)
- Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins + (No data available now.)
- Discovering and Maintaining Links on the Web of Data + (No data available now.)
- LogMap: Logic-based and Scalable Ontology Matching + (No data available now.)
- SERIMI – Resource Description Similarity, RDF Instance Matching and Interlinking + (No data available now.)
- Accessing and Documenting Relational Databases through OWL Ontologies + (No data available now.)
- Zhishi.links Results for OAEI 2011 + (No data available now.)
- D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs + (No data available now.)
- Use of OWL and SWRL for Semantic Relational Database Translation + (No data available now.)
- DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé + (No data available now.)
- RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems + (No data available now.)
- From Relational Data to RDFS Models + (No data available now.)
- Relational.OWL - A Data and Schema Representation Format Based on OWL + (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.)
- Bringing Relational Databases into the Semantic Web: A Survey + (No data available now.)
- Analysing Scholarly Communication Metadata of Computer Science Events + (No data available now.)
- A Survey of Current Link Discovery Frameworks + (No data available now.)
- Integration of Scholarly Communication Metadata using Knowledge Graphs + (No data available now.)
- Cross: an OWL wrapper for teasoning on relational databases + (No data available now.)
- Towards a Knowledge Graph for Science + (No data available now.)
- SLINT: A Schema-Independent Linked Data Interlinking System + (SLINT system totally outperforms the other … 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.of SLINT is still 0.05 higher in overall.)
- Querying Distributed RDF Data Sources with SPARQL + (The experiments show that
our optimization … The experiments show that our optimizations significantly improve query evaluation performance. For query Q1 the execution times of optimized and unoptimized execution are almost the same. This is due to the fact that the query plans for both cases are the same and bind joins of all sub-queries in order of appearance is exact the right strategy. For queries Q2 and Q4 the unoptimized queries took longer than 10 min to answer and timed out, whereas the execution time of the optimized queries is quiet reasonable. The optimized execution of Q1 and Q2 takes almost the same time because Q2 is rewritten into Q1.same time because Q2 is rewritten into Q1.)
- A Probabilistic-Logical Framework for Ontology Matching + (Using stability constraints improves alignment quality with both learned and manually set weights.)
- Querying the Web of Data with Graph Theory-based Techniques + (We divide evaluation results of GDS, FedX … We divide evaluation results of GDS, FedX and DARQ into three categories. First is query performance related metrics. Limited by space, we only provide QPS in this paper7. Second is system loads including CPU usage and memory usage. Third is network overhead including uploading data and downloading data. Here we especially compare GDS with FedX, because DARQ fails providing correct results or is time out on many queries.ct results or is time out on many queries.)
- ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints + (We observe that SHJ and ANAPSID operators … We observe that SHJ and ANAPSID operators are able to produce the first tuple faster than ARQ or Hash join, even in an ideal scenario with no delays; further, ARQ performance is clearly aff_ected by data transfer distribution and its execution time can be almost two orders of magnitude greater than the time of SHJ or ANAPSID. We notice that SHJ and ANAPSID are competitive, this is because the number of intermediate results is very small, and the benefits of the RJTs cannot be exploited. This suggests that the performance of ANAPSID operators depends on the selectivity of the join operator and the data transfer delays.oin operator and the data transfer delays.)
- FedX: Optimization Techniques for Federated Query Processing on Linked Data + (With our optimization techniques, we are able to reduce the number of requests significantly, e.g., from 170,579 (DARQ) and 93,248 (AliBaba) to just 23 (FedX) for query CD3.)
- Querying over Federated SPARQL Endpoints : A State of the Art Survey + ({{{Results}}})