The Research on Key Technologies of IOT in Agriculture Based on Semantic SOA

2014 ◽  
Vol 519-520 ◽  
pp. 1542-1545
Author(s):  
Zhen Zhao
Keyword(s):  
Environmental Factors ◽  
Web Service ◽  
Agricultural Production ◽  
Service Discovery ◽  
Field Data ◽  
Semantic Annotation ◽  
Growth Conditions ◽  
System Framework ◽  
Key Technologies ◽  
Soil Environmental Factors

Agricultural IOT is needed to regulate and improve the crops growth conditions, to monitor some of soil environmental factors. This article designs the agriculture IOT framework based on semantic SOA, proposes to use semantic SOA techniques to increase the discovery efficiency of service of agricultural IOT. It also introduces some related key technologies in semantic annotation of Web Service and the semantic issuing of annotation results using the SAWSDL standards. This system framework support remote real-time monitoring of the field data of agricultural production, and the efficient, accurate service discovery and service invoking.

A Study on Different Web Service Discovery Approaches

2018 ◽  
Vol 6 (9) ◽  
pp. 311-314
Author(s):  
Rahul P. Mirajkar ◽  
Nikhil D. Karande ◽  
Surendra Yadav
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Web Service Discovery

PROCESS MODEL ONTOLOGY-BASED MATCHMAKING OF SEMANTIC WEB SERVICES

2011 ◽  
Vol 20 (04) ◽  
pp. 357-370 ◽  
Author(s):  
D. PAULRAJ ◽  
S. SWAMYNATHAN ◽  
M. MADHAIYAN
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Web Service ◽  
Process Model ◽  
Service Discovery ◽  
Service Oriented Architecture ◽  
Semantic Web Services ◽  
Functional Part ◽  
Service Oriented ◽  
Concise Description

One of the key challenges of the Service Oriented Architecture is the discovery of relevant services for a given task. In Semantic Web Services, service discovery is generally achieved by using the service profile ontology of OWL-S. Profile of a service is a derived, concise description and not a functional part of the semantic web service. There is no schema present in the service profile to describe the input, output (IO), and the IOs in the service profile are not always annotated with ontology concepts, whereas the process model has such a schema to describe the IOs which are always annotated with ontology concepts. In this paper, we propose a complementary sophisticated matchmaking approach which uses the concrete process model ontology of OWL-S instead of the concise service profile ontology. Empirical analysis shows that high precision and recall can be achieved by using the process model-based service discovery.

Incorporating LDA With Word Embedding for Web Service Clustering

2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He
Keyword(s):  
Web Service ◽  
Service Discovery ◽  
Word Embedding ◽  
The Internet ◽  
Word Embeddings ◽  
Training Process ◽  
Web Service Discovery ◽  
Processing Data ◽  
Clustering Approach ◽  
Service Clustering

With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

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