RDF object description generator

Chen, Yanji; Kokar, Mieczyslaw M.; Moskal, Jakub

doi:10.1504/ijwet.2020.109729

Cited by 4 publications

(10 citation statements)

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“…In this section, we present an evaluation of SQG with respect to a number of metrics. Since one of our objectives was to show that SQG can be used on different ontologies, we reused the same set of ontologies for evaluating RODG [9], i.e., the SDR ontology [32] developed by us, and five existing ontologies in different domains: the eDIANA ontology [12], the IoT ontology [19,21], the Smart Appliances REFerence (SAREF) ontology [10,36], the Univ-Bench ontology [41] and the WM30 ontology [39]. Table 2 summarizes the basic characteristics of each ontology, including the number of classes, object properties, data properties, class axioms, object property axioms and data property axioms.…”

Section: Discussionmentioning

confidence: 99%

“…Note that all of the definitions originate from other papers. Definition 1 comes from [9]. Definitions 2 and 3 are based on [2] and [35], respectively.…”

Section: Formalization Of Basic Conceptsmentioning

confidence: 99%

“…It constructs a Java model using the OWL API [18]. The RDF dataset is generated by the RODG program [9].…”

Section: Sqg Overviewmentioning

confidence: 99%

“…In most cases, the ontologies were not designed just for this particular application scenario, and thus, some of the concepts in the signature of the ontology are not relevant to these kinds of query. Therefore, it is necessary to narrow down the scope of the signatures and focus only on the signatures, termed focus signatures [9], whose concepts are expected to be used for the generation of the queries. The number of focus signatures (class signatures |C|, object properties |O P| and data properties |D P|) versus the total number of signatures for each of the six ontologies is listed in Table 3.…”

Section: Coverage Of Owl Axiomsmentioning

confidence: 99%

“…We have described a description generator in [9]. In this paper, we are focusing on the query generator.…”

Section: Introductionmentioning

confidence: 99%

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SPARQL Query Generator (SQG)

2021

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This paper describes a program—SPARQL Query Generator (SQG)—which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.

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Section: Discussionmentioning

confidence: 99%

“…Note that all of the definitions originate from other papers. Definition 1 comes from [9]. Definitions 2 and 3 are based on [2] and [35], respectively.…”

Section: Formalization Of Basic Conceptsmentioning

confidence: 99%

“…It constructs a Java model using the OWL API [18]. The RDF dataset is generated by the RODG program [9].…”

Section: Sqg Overviewmentioning

confidence: 99%

Section: Coverage Of Owl Axiomsmentioning

confidence: 99%

“…We have described a description generator in [9]. In this paper, we are focusing on the query generator.…”

Section: Introductionmentioning

confidence: 99%

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SPARQL Query Generator (SQG)

2021

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TGR: Neural-symbolic ontological reasoner for domain-specific knowledge graphs

et al. 2023

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Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies

et al. 2021

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This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum-sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and that the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software-defined radios.

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RDF object description generator

Cited by 4 publications

References 0 publications

SPARQL Query Generator (SQG)

SPARQL Query Generator (SQG)

TGR: Neural-symbolic ontological reasoner for domain-specific knowledge graphs

Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies

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