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A list of all pages that have property "Has Description" with value "No data available now.". Since there have been only a few results, also nearby values are displayed.

Showing below up to 31 results starting with #1.

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    • A Probabilistic-Logical Framework for Ontology Matching  + (We applied the reasoner Pellet to create the ground MLN formulation and used TheBeast2 (Riedel 2008) to convert the MLN formulations to the corresponding ILP instances. Finally, we applied the mixed integer programming solver SCIP3 to solve the ILP.)
    • Querying the Web of Data with Graph Theory-based Techniques  + (We deploy 6 SPARQL endpoints (Sesame 2.4.0
      We deploy 6 SPARQL endpoints (Sesame 2.4.0) on 5 remote virtual machines. About 400,000 triples (generated by BSBM) are distributed to these endpoints following Gaussian distribution. We follow the metrics presented in (23). For each query, we calculate the number of queries executed per second (QPS) and average results count. For the whole test, we record the overall runtime, CPU usage, memory usage and network overhead. We perform 10 warm up runs and 50 testing runs for each engine. Time out is set to 30 seconds. In each run, only one instance of each engine is used for all queries, but cache is cleared after finishing each query. Warm up runs are not counted in query time related metrics, but included in system and network overhead.
      t included in system and network overhead.)
    • Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles  + (We followed these steps: – A set of 10 pre
      We followed these steps: – A set of 10 predefined natural language queries has been prepared for evaluation Table 4. Then, asking participants to try to answer these queries using their own tools and services. The queries were chosen in increasing order of complexity. – We implemented SPARQL queries corresponding to each of these queries to enable non-expert participants, not familiar with SPARQL, to query the knowledge graph. – We asked researchers to review the answers of the pre-defined queries that we formulated based on the SemSur ontology. We asked them to tell us whether they consider the provided answers and the way queries are formulated comprehensive and reasonable. – We finally asked the same researchers to fill in a satisfaction questionnaire with 18 questions14
      sfaction questionnaire with 18 questions14)
    • ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints  + (We report on runtime performance, which co
      We report on runtime performance, which corresponds to the user time produced by the _ 􀀀_ command of the Unix operation system. Experiments in RDF-3X were run in both cold and warm caches; to run cold cache, we executed the same query five times by dropping the cache just before running the first iteration of the query. Each query executed by ANAPSID and SPARQL endpoints was run ten times, and we report on the average time.
      times, and we report on the average time.)
    • SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions  + (we investigated how the information from t
      we investigated how the information from the VOID descriptions effect the accuracy of the source selection. For each query, we look at the number of sources selected and the resulting number of requests to the SPARQL endpoints. We tested three different source selection approaches, based on 1) predicate index only (no type information), 2) predicate and type index, and 3) predicate and type index and grouping of sameAs patterns as described in Section 4.2.
      meAs patterns as described in Section 4.2.)
    • Querying over Federated SPARQL Endpoints : A State of the Art Survey  + ({{{Description}}})
    • Avalanche: Putting the Spirit of the Web back into Semantic Web Querying  + ({{{Description}}})