Federated Grid Architecture for Language Services

Murakami, Yohei; Nakaguchi, Takao; Lin, Donghui; Ishida, Toru

doi:10.1007/978-981-10-7793-7_1

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…As such, and due to the QoR different units and dimensions, it is essential to normalize their values while computing the score(

). Similar to [ 35 ], Equations (1) and (2) are used to normalize the attribute values of group (i) and group (ii) respectively, with

, denoting the attribute value that is related to a resource, or the attribute value corresponding to a resource provided function. In the equation below, a normalised attribute value,

, is equal to 1 if

= 0, with

and

refers respectively to the maximum and minimum values of

among the identified resources in the Directed Resource Acyclic Graph (DRAG):

…”

Section: Location-aware Resource Discovery and Qor-driven Resource Selection For I-compositionsmentioning

confidence: 99%

Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

Kallab

Chbeir

Mrissa

2021

Sensors

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In the Web of Things (WoT) context, an increasing number of stationary and mobile objects provide functions as RESTful services, also called resources, that can be combined with other existing Web resources, to create value-added processes. However, nowadays resource discovery and selection are challenging, due to (1) the growing number of resources providing similar functions, making Quality of Resource (QoR) essential to select appropriate resources, (2) the transient nature of resource availability due to sporadic connectivity, and (3) the location changes of mobile objects in time. In this paper, we first present a location-aware resource discovery that relies on a 3-dimensional indexing schema, which considers object location for resource identification. Then, we present a QoR-driven resource selection approach that uses a Selection Strategy Adaptor (SSA) to form i-compositions (with i ∈N*) offering different implementation alternatives. The defined SSA allows forming resource compositions while considering QoR constraints and Inputs/Outputs matching of related resources, as well as resource availability and users different needs (e.g., optimal and optimistic compositions obtained using a scoring system). Analyses are made to evaluate our service quality model against existing ones, and experiments are conducted in different environments setups to study the performance of our solution.

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“…As such, and due to the QoR different units and dimensions, it is essential to normalize their values while computing the score(

). Similar to [ 35 ], Equations (1) and (2) are used to normalize the attribute values of group (i) and group (ii) respectively, with

, denoting the attribute value that is related to a resource, or the attribute value corresponding to a resource provided function. In the equation below, a normalised attribute value,

, is equal to 1 if

= 0, with

and

refers respectively to the maximum and minimum values of

among the identified resources in the Directed Resource Acyclic Graph (DRAG):

…”

Section: Location-aware Resource Discovery and Qor-driven Resource Selection For I-compositionsmentioning

confidence: 99%

Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

Kallab

Chbeir

Mrissa

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In the Language Grid, language service interfaces are organized in a hierarchical manner. The LanguageService class in the upper level can be further classified into four classes: The SpeechService class for processing speech data, DataService class for dealing with linguistic data resources, TransformationService class for transforming input texts to output texts, and AnalysisService class for analyzing input data and outputting analysis results [31]. Some of the available low-level service interface classes and examples of corresponding service types defined by the Language Grid Ontology are shown below.…”

Section: Language Service Infrastructure: the Language Gridmentioning

confidence: 99%

Towards Language Service Creation and Customization for Low-Resource Languages

2020

Self Cite

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The most challenging issue with low-resource languages is the difficulty of obtaining enough language resources. In this paper, we propose a language service framework for low-resource languages that enables the automatic creation and customization of new resources from existing ones. To achieve this goal, we first introduce a service-oriented language infrastructure, the Language Grid; it realizes new language services by supporting the sharing and combining of language resources. We then show the applicability of the Language Grid to low-resource languages. Furthermore, we describe how we can now realize the automation and customization of language services. Finally, we illustrate our design concept by detailing a case study of automating and customizing bilingual dictionary induction for low-resource Turkic languages and Indonesian ethnic languages.

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Federated Grid Architecture for Language Services

Cited by 2 publications

References 12 publications

Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

Location-Aware Resource Discovery and QoR-Driven Resource Selection for Hybrid Web Environments

Towards Language Service Creation and Customization for Low-Resource Languages

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