Rainbow: A distributed and hierarchical RDF triple store with dynamic scalability

2014 ◽  
Author(s):  
Rong Gu ◽  
Wei Hu ◽  
Yihua Huang
Keyword(s):  
Triple Store

scholarly journals 3XL: An Efficient DBMS-Based Triple-Store

2012 ◽  
Author(s):  
Xiufeng Liu ◽  
Christian Thomsen ◽  
Torben Bach Pedersen
Keyword(s):  
Triple Store

Semantic Data Mapping on E-Learning Usage Index Tool to Handle Heterogeneity of Data Representation

2014 ◽  
Vol 69 (5) ◽  
Author(s):  
Arda Yunianta ◽  
Norazah Yusof ◽  
Mohd Shahizan Othman ◽  
Abdul Aziz ◽  
Nataniel Dengen ◽  
...  
Keyword(s):  
Data Representation ◽  
Current Data ◽  
Data Mapping ◽  
Semantic Technology ◽  
Semantic Data ◽  
Triple Store ◽  
Integration Problem ◽  
E Learning ◽  
Semantic Ontology ◽  
Future Work

Distribution and heterogeneity of data is the current issues in data level implementation. Different data representation between applications makes the integration problem increasingly complex. Stored data between applications sometimes have similar meaning, but because of the differences in data representation, the application cannot be integrated with the other applications. Many researchers found that the semantic technology is the best way to resolve the current data integration issues. Semantic technology can handle heterogeneity of data; data with different representations and sources. With semantic technology data mapping can also be done from different database and different data format that have the same meaning data. This paper focuses on the semantic data mapping using semantic ontology approach. In the first level of process, semantic data mapping engine will produce data mapping language with turtle (.ttl) file format that can be used for Local Java Application using Jena Library and Triple Store. In the second level process, D2R Server that can be access from outside environment is provided using HTTP Protocol to access using SPARQL Clients, Linked Data Clients (RDF Formats) and HTML Browser. Future work to will continue on this topic, focusing on E-Learning Usage Index Tool (IPEL) application that is able to integrate with others system applications like Moodle E-Learning Systems. 

scholarly journals 2240

2017 ◽  
Vol 1 (S1) ◽  
pp. 13-13
Author(s):  
Peter Elkin ◽  
Sarah Mullin ◽  
Sanjay Sethi ◽  
Shyamashree Sinha ◽  
Animesh Sinha
Keyword(s):  
Outpatient Setting ◽  
Observational Research ◽  
Snomed Ct ◽  
Data Set ◽  
Learning Health System ◽  
Nosql Database ◽  
Obstructive Sleep ◽  
Triple Store ◽  
Increased Risk ◽  
Improved Accuracy

OBJECTIVES/SPECIFIC AIMS: To create a new semantically correct high-throughput phenotyping (HTP) platform. To demonstrate the utility of the HTP platform for observational research and can allow clinical investigators to perform studies in 5 minutes. To demonstrate the improved accuracy of observational research using this platform when compared with traditional observational research methods. To demonstrate that patients who have Roseacea are at increased risk of having obstructive sleep apnea (OSA). METHODS/STUDY POPULATION: This population is a set of 212,343 patients in the outpatient setting cared for in the Buffalo area over a 6-year period. All records for these patients were included in the study. Structured data was imported into an OMOP (OHDSI) database and all of the notes and reports were parsed by our HTP system which produces SNOMED CT codes. Each code is designated as a positive, negative or uncertain assertion and compositional expressions are automatically generated. We store the codified data 750,000,000 codes in Berkley DB, a NOSQL database, and we keep the compositional graphs in both Neo4J and in GraphDB (a triple store). Labs are coded in LOINC and drugs using RxNorm. We have developed a Web interface in .Net named BMI Search, which allows real-time query by subject matter experts. We analyzed the accuracy of structured Versus unstructured data by identifiying NVAF cases with ICD9 codes and then looked for any additional cases based on the SNOMED CT encodings of the clinical record. This was validated by 2 clinical human review of a set of 300 randomly selected cases. Separately we ran a study to determine the relative risk of OSA with and without Rosacea using the data set described above. We compared the rates using a Pearson χ2 test. RESULTS/ANTICIPATED RESULTS: We are able to parse 7,000,000 records in an hour and a half on 1 node with 4 CPUs. This yielded 750,000,000 SNOMED CT codes. The HTP data set yielded 1849 cases using ICD9 codes and another 873 using the HTP-NLU data, leading to a final data set of 2722 cases from our population of 212,343 patients. In total, 580 patients had Rosacea;5443 patients had OSA without Rosacea and 51 patients had OSA with Rosacea. Patients with Rosaca had an 8.8% risk of OSA whereas patients without Rosacia only had a 2.6% risk of OSA. This was highly statistically significant with a p<0.0001 (Pearson χ2 test). The number needed to test was only 12. DISCUSSION/SIGNIFICANCE OF IMPACT: HTP can change how we do observational research and can lead to more accurate and more prolific investigation. This rapid turn around is part of what is necessary for both precision medicine and to create a learning health system. Patients with Rosacea are at increased risk of and should be screened for OSA.

scholarly journals Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0144578 ◽  
Author(s):  
Davide Alocci ◽  
Julien Mariethoz ◽  
Oliver Horlacher ◽  
Jerven T. Bolleman ◽  
Matthew P. Campbell ◽  
...  
Keyword(s):  
Substructure Search ◽  
Triple Store

scholarly journals The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research

PLoS ONE ◽  
2014 ◽  
Vol 9 (12) ◽  
pp. e115460 ◽  
Author(s):  
Joseline Ratnam ◽  
Barbara Zdrazil ◽  
Daniela Digles ◽  
Emiliano Cuadrado-Rodriguez ◽  
Jean-Marc Neefs ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Triple Store

scholarly journals Interoperability and Integration: An Updated Approach to Linked Data Publication at the Dutch Land Registry

2022 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Alexandra Rowland ◽  
Erwin Folmer ◽  
Wouter Beek ◽  
Rob Wenneker
Keyword(s):  
Spatial Data ◽  
Linked Data ◽  
Knowledge Graph ◽  
Knowledge Model ◽  
New Approach ◽  
Data Publication ◽  
Triple Store ◽  
Form Part ◽  
Increasing Demand ◽  
External Community

Kadaster, the Dutch National Land Registry and Mapping Agency, has been actively publishing their base registries as linked (open) spatial data for several years. To date, a number of these base registers as well as a number of external datasets have been successfully published as linked data and are publicly available. Increasing demand for linked data products and the availability of new linked data technologies have highlighted the need for a new, innovative approach to linked data publication within the organisation in the interest of reducing the time and costs associated with said publication. The new approach to linked data publication is novel in both its approach to dataset modelling, transformation, and publication architecture. In modelling whole datasets, a clear distinction is made between the Information Model and the Knowledge Model to capture both the organisation-specific requirements and to support external, community standards in the publication process. The publication architecture consists of several steps where instance data are loaded from their source as GML and transformed using an Enhancer and published in the triple store. Both the modelling and publication architecture form part of Kadaster’s larger vision for the development of the Kadaster Knowledge Graph through the integration of the various linked datasets.

scholarly journals Balancing control, usability and visibility of linked open government data to create public value

2019 ◽  
Vol 32 (5) ◽  
pp. 451-466 ◽  
Author(s):  
Benedikt Simon Hitz-Gamper ◽  
Oliver Neumann ◽  
Matthias Stürmer
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Knowledge Bases ◽  
Open Government ◽  
Public Value ◽  
Content Type ◽  
Triple Store ◽  
Open Government Data ◽  
Government Data ◽  
Governance Modes

Purpose Linked data is a technical standard to structure complex information and relate independent sets of data. Recently, governments have started to use this technology for bridging separated data “(silos)” by launching linked open government data (LOGD) portals. The purpose of this paper is to explore the role of LOGD as a smart technology and strategy to create public value. This is achieved by enhancing the usability and visibility of open data provided by public organizations. Design/methodology/approach In this study, three different LOGD governance modes are deduced: public agencies could release linked data via a dedicated triple store, via a shared triple store or via an open knowledge base. Each of these modes has different effects on usability and visibility of open data. Selected case studies illustrate the actual use of these three governance modes. Findings According to this study, LOGD governance modes present a trade-off between retaining control over governmental data and potentially gaining public value by the increased use of open data by citizens. Originality/value This study provides recommendations for public sector organizations for the development of their data publishing strategy to balance control, usability and visibility considering also the growing popularity of open knowledge bases such as Wikidata.

Towards Scalingless Generation of Formal Contexts from an Ontology in a Triple Store

2013 ◽  
Vol 1 (1) ◽  
pp. 18-38 ◽  
Author(s):  
Frithjof Dau
Keyword(s):  
Visual Analytics ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Research Project ◽  
Funded Research ◽  
Object Properties ◽  
Triple Store ◽  
Funded Research Project ◽  
The Eu

The EU-funded research project CUBIST investigates how Formal Concept Analysis can be applied as a Visual Analytics tool on top of information stored in a Triple Store (TS). This paper provides first steps for utilizing SPARQL in order to generate formal contexts out of the data in the TS, where the emphasis is put on using object-properties between individuals. Thus it complements FcaBedrock, which will be used in CUBIST as well and focuses on the scaling of datatype-properties between individuals and literals. It is discussed how the approaches of this paper and FcaBedrock can be combined.

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