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List of results
- KnoFuss: A Comprehensive Architecture for Knowledge Fusion + (-)
- Querying the Web of Interlinked Datasets using VOID Descriptions + (-)
- SLINT: A Schema-Independent Linked Data Interlinking System + (2.66Ghz quad-core CPU and 4GB of memory)
- FedX: Optimization Techniques for Federated Query Processing on Linked Data + (All experiments are carried out on an HP P … All experiments are carried out on an HP Proliant DL360 G6 with 2GHz</br>4Core CPU with 128KB L1 Cache, 1024KB L2 Cache, 4096KB L3 Cache, 32GB</br>1333MHz RAM, and a 160 GB SCSI hard drive.</br>In all scenarios we assigned 20GB RAM to the process executing the query</br>In the SPARQL federation we additionally assign 1GB RAM to each individual SPARQL endpoint process.o each individual SPARQL endpoint process.)
- A Probabilistic-Logical Framework for Ontology Matching + (All experiments were conducted on a desktop PC with AMD Athlon Dual Core Processor 5400B with 2.6GHz and 1GB RAM.)
- SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions + (Due to the unpredictable availability and … Due to the unpredictable availability and latency of the original SPARQL endpoints</br>of the benchmark dataset we used local copies of them which were hosted on five 64bit</br>Intel(R) Xeon(TM) CPU 3.60GHz server instances running Sesame 2.4.2 with each</br>instance providing the SPARQL endpoint for one life science and for one cross domain</br>dataset. The evaluation was performed on a separate server instance with 64bit Intel(R)</br>Xeon(TM) CPU 3.60GHz and a 100Mbit network connection. 3.60GHz and a 100Mbit network connection.)
- Adaptive Integration of Distributed Semantic Web Data + (Endpoint machines are connected to the machine on which the mediator is deployed (2GHz AMD Athlon X2, 2GB RAM) via a 100Mbs Ethernet LAN.)
- RDB2ONT: A Tool for Generating OWL Ontologies From Relational Database Systems + (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.)
- D2RQ – Treating Non-RDF Databases as Virtual RDF Graphs + (No data available now.)
- LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data + (No data available now.)
- Discovering and Maintaining Links on the Web of Data + (No data available now.)
- Use of OWL and SWRL for Semantic Relational Database Translation + (No data available now.)
- Accessing and Documenting Relational Databases through OWL Ontologies + (No data available now.)
- Optimizing SPARQL Queries over Disparate RDF Data Sources through Distributed Semi-joins + (No data available now.)
- From Relational Data to RDFS Models + (No data available now.)
- DataMaster – a Plug-in for Importing Schemas and Data from Relational Databases into Protégé + (No data available now.)
- AgreementMaker: Efficient Matching for Large Real-World Schemas and Ontologies + (No data available now.)
- Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web + (No data available now.)
- Analysing Scholarly Communication Metadata of Computer Science Events + (No data available now.)
- Relational.OWL - A Data and Schema Representation Format Based on OWL + (No data available now.)
- LogMap: Logic-based and Scalable Ontology Matching + (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.)
- Towards a Knowledge Graph for Science + (No data available now.)
- Cross: an OWL wrapper for teasoning on relational databases + (On an Intel Core 2, 2.33GHz, with 2GB of memory)
- Avalanche: Putting the Spirit of the Web back into Semantic Web Querying + (Test Avalanche using a five-node cluster. Each machine had 2GB RAM and an Intel Core 2 Duo E8500 @ 3.16GHz)
- Zhishi.links Results for OAEI 2011 + (Tests were carried out on a Hadoop computer cluster. Each node has a quad-core Intel Core 2 processor (4M Cache, 2.66 GHz), 8GB memory. The number of reduce tasks was set to 50.)
- Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles + (The evaluation started with the phase of letting researchers first read the given overview questions and letting them try in their own way to find the respective answer.)
- A Semantic Web Middleware for Virtual Data Integration on the Web + (The tests were performed with the followin … The tests were performed with the following setup: the mediator (and also the test client) where running on a 2.16 GHz Intel Core 2 Duo with 2 GB memory and a 2 MBit link to the remote endpoints. All endpoints were simulated on the same physical host running two AMD Opteron CPUs at 1.6 GHz and 2 GB memory.D Opteron CPUs at 1.6 GHz and 2 GB memory.)
- Querying the Web of Data with Graph Theory-based Techniques + (The three engines are run independently on a machine having an Intel Xeon W3520 processor, 12 GB memory and 1Gbps LAN.)
- Querying Distributed RDF Data Sources with SPARQL + (we split all data over two Sun-Fire-880 ma … we</br>split all data over two Sun-Fire-880 machines (8x sparcv9 CPU, 1050Mhz, 16GB RAM) running SunOS 5.10.</br>The SPARQL endpoints were provided using Virtuoso Server 5.0.37 with an allowed memory usage of 8GB . Note that, although</br>we use only two physical servers, there were five logical SPARQL endpoints.</br>DARQ was running on Sun Java 1.6.0 on a Linux system with Intel Core Duo</br>CPUs, 2.13 GHz and 4GB RAM. The machines were connected over a standard</br>100Mbit network connection.ver a standard 100Mbit network connection.)
- ANAPSID: An Adaptive Query Processing Engine for SPARQL Endpoints + (We empirically analyze the performance of … We empirically analyze the performance of the proposed query processing techniques and report on the execution time of plans comprised of ANAPSID operators versus queries posed against SPARQL endpoints, and state-of-the-art RDF engines.</br>Three sets of queries were considered (Table of Figure 5(b)); each sub-query was executed</br>as a query against its corresponding endpoint. Benchmark 1 is a set of 10 queries against LinkedSensorData-blizzards; each query can be grouped into 4 or 5 sub-queries. Benchmark 2 is a set of 10 queries over linkedCT with 3 or 4 subqueries. Benchmark 3 is a set of 10 queries with 4 or 5 sub-queries executed against linkedCT and DBPedia endpoints.</br>Experiments were executed on a Linux CentOS machine with an Intel Pentium Core2 Duo 3.0 GHz and 8GB RAM.tel Pentium Core2 Duo 3.0 GHz and 8GB RAM.)
- SERIMI – Resource Description Similarity, RDF Instance Matching and Interlinking + (We have loaded all these datasets into an … We have loaded all these datasets into an open-source instance of Virtuoso Universal server 10 , where around 2GB of data were loaded. An exception was the DBPedia dataset, which we accessed online via its Sparql endpoint. The Virtuoso server was installed in a Mac OS X – version 10.5.8, with 2.4 GHz Intel Core 2 Duo processor and with 4 GB 1067 MHz DDR3 of memory. We ran the script that implements the SERIMI approach directly over the local SPARQL endpoints and DBPedia online endpoint.RQL endpoints and DBPedia online endpoint.)
- Querying over Federated SPARQL Endpoints : A State of the Art Survey + ({{{ExperimentSetup}}})