Process Model Ontology-Based Matchmaking of Semantic Web Services

Paulraj, D.; Swamynathan, S.; Madhaiyan, M.

doi:10.1142/s0218843011002262

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…By using weight w i for each QoS attribute, the system becomes able to calculate the resource QoS score according to each learner preferences. This method was proposed by [19], and is one of the widely used approaches in service selection and composition.…”

Section: Qos Attributes Normalizationmentioning

confidence: 99%

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Essayah¹,

Youssfi²,

Bouattane³

et al. 2019

IJACSA

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In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

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Section: Qos Attributes Normalizationmentioning

confidence: 99%

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Essayah¹,

Youssfi²,

Bouattane³

et al. 2019

IJACSA

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show abstract

“…Kapitsaki (2012) proposed a semantic Web service matching approach where the context of the service was constructed by keyword retrieval in WSDL interface signature and textual service description. Additionally, process metadata describing how the service implements the 'Process' part of the advertisement can be used to improve the searching performance, especially for the retrieval of composite services (Paulraj et al, 2011;Shafiq et al, 2014). However, a normative framework for this type of service process extraction and translation is still immature.…”

Section: Related Workmentioning

confidence: 99%

A semantic similarity measure integrating multiple conceptual relationships for web service discovery

Chen

et al. 2017

Expert Systems with Applications

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The process of Web service discovery identifies the most relevant services to requesters' service queries. We propose a new measure of semantic similarity integrating multiple conceptual relationships (SIMCR) for Web service discovery. The new measure enables more accurate service-request comparison by treating different conceptual relationships in ontologies such as is-a, has-a and antonomy differently. Each service or request is represented by vectors of terms (or words) that characterize both the interface signature and textual description. The overall semantic similarity is computed as a weighted aggregation of interface similarity and description similarity. The experimental results confirm the effectiveness of the proposed semantic similarity measure. As demonstrated in this study, the semantic Web service discovery method based on the proposed similarity measure outperforms existing state-of-the-art discovery methods in terms of precision, recall and Fmeasure. The proposed semantic similarity measure has wider applications such as to improve document classification or clustering, and to more accurately represent and apply knowledge in expert and intelligent systems.

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“…Nevertheless, this approach was tested on a small repository (ten services) and start from a predefined workflow. Another approach [38,39] develops an algorithm that matches an I/O user query to each leaf node (atomic service) by traversing the whole processmodel through its root. If a match is found, it is added to a temporary List since it does not exist on it.…”

Section: Related Workmentioning

confidence: 99%

SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models

Merazi¹,

Malki²

2015

IJITCS

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With the advent of new forms of information and communication technologies, the consumer needs to combine and customize different travel components as a complete travel package, namely: Dynamic Packaging Technology. Nevertheless, disparate tourist offers and services make it difficult for consumer to use them effectively. Therefore, our paper presents an intelligent querying framework of OWLS travel services, called SQUIREL composition engine. It uses Semantic Web Services (SWSs) technologies combined with the useful of Linked e-tourism Data concept to fulfill the preferences and constraints of the e-tourist any time. This purpose supports SWSs pre-selection through the valuation of the rewritten SPARQL consumer query at runtime that manages dynamic service dependencies extracted from Linked e-tourism Data and returns the SWSs endpoint. Then, SQUIREL catches this endpoint and makes the necessary optimizations to refine it to its relevant atomic processes needed to be composed using matrix computation. However, the experimental results indicate that this method owns both lower computation cost and higher success ratio of fine-grained discoverybased atomic processes composition.

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Process Model Ontology-Based Matchmaking of Semantic Web Services

Cited by 8 publications

References 10 publications

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

A semantic similarity measure integrating multiple conceptual relationships for web service discovery

SQUIREL: Semantic Querying Interlinked OWL-S traveling Process Models

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