METEOR-S Web Service Annotation Framework with Machine Learning Classification

Oldham, Nicole; Thomas, Christopher; Sheth, Amit P.; Verma, Kunal

doi:10.1007/978-3-540-30581-1_12

Cited by 56 publications

(47 citation statements)

References 4 publications

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“…The METEOR-S Framework [14] is able to assign semantic concepts to web services by considering their WSDL descriptions but without taking into account the unstructured data potentially available within the documentation tag that can give more information about the category the web service belongs to. Instead of attaching a category concept to a web service, SAWSDL-MX2 [15] evaluates the similarity between a pair of web services based on both structured and unstructured information included in their interfaces using support vector machines.…”

Section: Discussionmentioning

confidence: 99%

Automatic Service Categorisation through Machine Learning in Emergent Middleware

Bennaceur

Issarny

Johansson

et al. 2013

Formal Methods for Components and Objects

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Abstract. The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system's interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project.

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Section: Discussionmentioning

confidence: 99%

Automatic Service Categorisation through Machine Learning in Emergent Middleware

Bennaceur

Issarny

Johansson

et al. 2013

Formal Methods for Components and Objects

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“…A number of similar approaches exist, particularly in the field of web services, such as [11,7,4], from which we can draw guidance. However, their aims and context often differ.…”

Section: Discussionmentioning

confidence: 99%

Inferring Affordances Using Learning Techniques

Bennaceur

Johansson

Moschitti

et al. 2012

Communications in Computer and Information Science

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Interoperability among heterogeneous systems is a key challenge in today's networked environment, which is characterised by continual change in aspects such as mobility and availability. Automated solutions appear then to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to achieve interaction, working from the highest application level to the lowest protocol level, come at a substantial computational cost, especially when checks are performed indiscriminately between systems in unrelated domains. To overcome this, we propose to use machine learning to extract the high-level functionality of a system and thus restrict the scope of detailed analysis to systems likely to be able to interoperate.

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“…Duo et al [10] present a similar approach, which also aggregates results from several matchers. Oldham et al [11] use a simple machine learning (ML) technique, namely Naïve Bayesian Classifier, to improve the precision of service annotation. Machine learning is also used in a tool called Assam [12], which uses existing annotation of semantic web services to improve new annotations.…”

Section: Web Service Annotationmentioning

confidence: 99%

Bootstrapping Ontologies for Web Services

Segev

Sheng

2012

IEEE Trans. Serv. Comput.

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Abstract-Ontologies have become the de-facto modeling tool of choice, employed in many applications and prominently in the semantic web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as web services, must address the problem of multiple, largely unrelated concepts. In this paper, we propose an ontology bootstrapping process for web services. We exploit the advantage that web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. Our proposed ontology bootstrapping process integrates the results of both methods and applies a third method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. We extensively validated our bootstrapping method using a large repository of real-world web services and verified the results against existing ontologies. The experimental results indicate high precision. Furthermore, the recall versus precision comparison of the results when each method is separately implemented presents the advantage of our integrated bootstrapping approach.Index Terms-Web services discovery, metadata of services interfaces, service-oriented relationship modeling.

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METEOR-S Web Service Annotation Framework with Machine Learning Classification

Cited by 56 publications

References 4 publications

Automatic Service Categorisation through Machine Learning in Emergent Middleware

Automatic Service Categorisation through Machine Learning in Emergent Middleware

Inferring Affordances Using Learning Techniques

Bootstrapping Ontologies for Web Services

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