A Pragmatic Approach to Semantic Repositories Benchmarking

Thakker, Dhavalkumar; Osman, Taha; Gohil, Shakti; Lakin, Phil

doi:10.1007/978-3-642-13486-9_26

Cited by 24 publications

(21 citation statements)

References 9 publications

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“…We adopted OWLIM (GeoSpatial 2013) to host our semantic knowledgebase as it offers non-restricted and well-supported version for academic use. OWLIM also scored very well in terms of response-time and recall in a benchmarking investigation we performed in partnership with the Press Association (Thakker et al 2010).…”

Section: Related Workmentioning

confidence: 95%

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

Osman¹,

Shires²,

Omitola³

et al. 2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

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Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend.

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Section: Related Workmentioning

confidence: 95%

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

Osman¹,

Shires²,

Omitola³

et al. 2013

ECMS 2013 Proceedings Edited By: Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang

Self Cite

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“…Various works also compare these RDF data stores [17][18][19][20][21]. In addition to comparing the RDF stores, RDF loaders are also compared in [22]. Distinct from our work; [17][18][19] use generated data, [17,18] use insufficiently low data for the performance evaluation, [17][18][19][20]22] do not use medical data, and [19,22] compare both AllegroGraph and Oracle 12c.…”

Section: Related Workmentioning

confidence: 99%

Comparing Relational and Ontological Triple Stores in Healthcare Domain

Can

Sezer

Bursa

et al. 2017

Entropy

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Today's technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients' problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores.

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“…The data layer also contains a semantic repository that allows storing the ontologies and performing reasoning. OWLIM was chosen due to scalability reasons [6] as the system is required to reason over a large number of tunnels and inspection data. The system also contains a relational database in the form of MySQL to store inspection and result data for caching purpose.…”

Section: Fig 1 Padtun System Architecturementioning

confidence: 99%

PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain

Thakker

Dimitrova

Cohn

et al. 2015

The Semantic Web. Latest Advances and New Domains

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Abstract.A Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts expectations of decision support.

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A Pragmatic Approach to Semantic Repositories Benchmarking

Cited by 24 publications

References 9 publications

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications

Comparing Relational and Ontological Triple Stores in Healthcare Domain

PADTUN - Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain

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