Query Rewriting in RDF Stream Processing

Calbimonte, Jean-Paul; Mora, José Ferrater; Corcho, Óscar

doi:10.1007/978-3-319-34129-3_30

Cited by 22 publications

(14 citation statements)

References 22 publications

(45 reference statements)

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“…The choice of ontology management tools to implement the chosen method depends on the level of expressiveness for the proposed ontology. The ontology representation languages include OWL (both version 1 and 2) [29,30,31], RDF [32,33,34], RDF Schema [35,36,37], DARPA Agent Markup Language and Ontology Interchange Language (DAML+OIL) [38,39] and the rest of them.…”

Section: Ontology Developmentmentioning

confidence: 99%

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Aminu¹,

Oyefolahan²,

Abdullahi³

et al. 2020

IJIEEB

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The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology's weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.

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Section: Ontology Developmentmentioning

confidence: 99%

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Aminu¹,

Oyefolahan²,

Abdullahi³

et al. 2020

IJIEEB

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“…The Ontop system [40] is an example for an ontology-based data access (OBDA) system, where a global schema is defined as an OWL2 QL ontology, and the source schemas are mapped to the global schema by SQL queries. OBDA has been extended to non-standard data formats such as GIS [9,20], temporal data [4,11], and streamed data [14,35].…”

Section: Semantic Web Technologiesmentioning

confidence: 99%

Towards a Semantically Enriched Local Dynamic Map

et al. 2018

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With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three application scenarios that illustrate the usability and benefits of our approach, thus we provide an in-depth validation of the scenarios in an experimental prototype.

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“…In parallel, Ren and Pan proposed an alternative approach via truth maintenance systems [74]. Calbimonte et al exploited ontology-based data access [22]. Heintz et al developed logic-based spatio-temporal stream reasoning [36].…”

Section: Stream Reasoningmentioning

confidence: 99%

Stream Processing Languages in the Big Data Era

Hirzel¹,

Baudart²,

Bonifati

et al. 2018

SIGMOD Rec.

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This paper is a survey of recent stream processing languages, which are programming languages for writing applications that analyze data streams. Data streams, or continuous data flows, have been around for decades. But with the advent of the big-data era, the size of data streams has increased dramatically. Analyzing big data streams yields immense advantages across all sectors of our society. To analyze streams, one needs to write a stream processing application. This paper showcases several languages designed for this purpose, articulates underlying principles, and outlines open challenges.

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Query Rewriting in RDF Stream Processing

Cited by 22 publications

References 22 publications

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

A Review on Ontology Development Methodologies for Developing Ontological Knowledge Representation Systems for various Domains

Towards a Semantically Enriched Local Dynamic Map

Stream Processing Languages in the Big Data Era

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