SAWSDL-MX2: A Machine-Learning Approach for Integrating Semantic Web Service Matchmaking Variants

Klusch, Matthias; Kapahnke, Patrick; Zinnikus, Ingo

doi:10.1109/icws.2009.76

Cited by 52 publications

(26 citation statements)

References 7 publications

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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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“…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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“…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. This approach is the closest to ours but is clearly not scalable especially when considering environments where services may continuously be discovered.…”

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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“…Our proposal has been applied to their proposed OWLS-MX variants, further improving performance results, especially on logic-based ones as discussed in Section 5. Moreover, similar solutions have been also proposed by the authors for WSMO [50] and SAWSDL [51] service matchmaking.…”

Section: Related Workmentioning

confidence: 99%