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List of results

• KnoFuss: A Comprehensive Architecture for Knowledge Fusion  + (-)
• Querying the Web of Interlinked Datasets using VOID Descriptions  + (-)

• A Semantic Web Middleware for Virtual Data Integration on the Web  + (For the following sample queries, real-wor …
  For the following sample queries, real-world data of sunspot observations recorded at Kanzelh¨ohe Solar Observatory (KSO) have been used. The observatory is also a partner in the Austrian Grid project. The queries are shown in Fig. 2. Query 1 retrieves the first name, the last name, and optionally the e-mail address of scientists who have done observations. Query 2 retrieves all observations ever recorded by Mr. Otruba.
  observations ever recorded by Mr. Otruba.)
• Querying Distributed RDF Data Sources with SPARQL  + (In this section we evaluate the performanc …
  In this section we evaluate the performance of the DARQ query engine. The prototype was implemented in Java as an extension to ARQ5. We used a subset of DBpedia6. DBpedia contains RDF information extracted from Wikipedia. The dataset is offered in different parts.
  The dataset is offered in different parts.)
• 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.)
• AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies  + (No data available now.)
• Discovering and Maintaining Links on the Web of Data  + (No data available now.)
• LIMES - A Time-Efficient Approach for Large-Scale Link Discovery 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.)
• SLINT: A Schema-Independent Linked Data Interlinking System  + (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.)
• 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.)
• Avalanche: Putting the Spirit of the Web back into Semantic Web Querying  + ({{{Description}}})
• Querying over Federated SPARQL Endpoints : A State of the Art Survey  + ({{{Description}}})