Service Mining on the Web

Zheng, George; Bouguettaya, Athman

doi:10.1109/tsc.2009.2

Cited by 45 publications

(24 citation statements)

References 14 publications

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“…In our approach concepts and their roles are extracted from the OWL-S service descriptions using OWL API 3 and Pellet 4 . Stop words in the text descriptions of the concepts are removed using the Stanford Log-Linear POS-tagger 5 .…”

Section: The Service Transaction Matrixmentioning

confidence: 99%

Probabilistic Matchmaking Methods for Automated Service Discovery

Cassar

Barnaghi

Moessner

2014

IEEE Trans. Serv. Comput.

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Abstract-Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values.

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Section: The Service Transaction Matrixmentioning

confidence: 99%

Probabilistic Matchmaking Methods for Automated Service Discovery

Cassar

Barnaghi

Moessner

2014

IEEE Trans. Serv. Comput.

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“…Transformation of web of data to web of semantics and services is continuing [10]. A web service mining framework is proposed by George et al service compositions to emerge automatically in bottom up fashion is allowed by this framework.…”

Section: Related Workmentioning

confidence: 99%

Did you Find Any Public Web Service Description over the World Wide Web?

Abid¹,

Abid²,

Javed³

et al. 2013

JACN

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Abstract-Web service is an implementation of Service Oriented Architecture. It is interoperable, loosely coupled through the components over the internet, and widely used by the consumers. A web service interface is written in WSDL. To understand about the details of a web service consumer uses it description. This description is in structural and non-structural form. The structural description tells the consumer what a service look like, whereas non-structural description helps to understand about the functionalities of the web services. The number of web services in public web service directories are decreasing day by day then the numbers of web services crawled by the search engines. Moreover many public web service that don't have their descriptions and it is also alarming that a large percentage do not contains more then 1K words in their descriptions. To understand a web service there may be other related web pages over the World Wide Web that contains the information about the service. In this paper, the non structural descriptions of a web service are focused. We have tried to search the information about a web service by passing different set of queries to the search engines. The related pages that are collected from the www are ranked, this result is shown to the consumer after filtering at a specific threshold value.Index Terms-WSDL, web services, public web services, web services descriptions.

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“…Existing frequent pattern discovery, schema matching, text clustering, and community discovery approaches can be helpful references [16,23,26,34,38]. A fundamental issue is to define appropriate distances on various resources, e.g., the Euclidean distance between physical objects, the distance between concepts in ontology, and the distance between vectors of texts.…”

Section: Automatically Detecting and Uploading Resources Into Resourcmentioning

confidence: 99%

Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society

Zhuge

Xing

2012

IEEE Trans. Serv. Comput.

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-Classification is the most basic method for organizing resources in the physical space, cyber space, socio space and mental space. To create a unified model that can effectively manage resources in different spaces is a challenge. The Resource Space Model RSM is to manage versatile resources with a multi-dimensional classification space. It supports generalization and specialization on multi-dimensional classifications. This paper introduces the basic concepts of RSM, and proposes the Probabilistic Resource Space Model, P-RSM, to deal with uncertainty in managing various resources in different spaces of the cyber-physical society. P-RSM's normal forms, operations and integrity constraints are developed to support effective management of the resource space. Characteristics of the P-RSM are analyzed through experiments. This model also enables various services to be described, discovered and composed from multiple dimensions and abstraction levels with normal form and integrity guarantees. Some extensions and applications of the P-RSM are introduced.Index Terms -cyber-physical society, faceted navigation, non-relational data model, resource management, resource space model, semantic link network, cyber-physical-socio services.

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Service Mining on the Web

Cited by 45 publications

References 14 publications

Probabilistic Matchmaking Methods for Automated Service Discovery

Probabilistic Matchmaking Methods for Automated Service Discovery

Did you Find Any Public Web Service Description over the World Wide Web?

Probabilistic Resource Space Model for Managing Resources in Cyber-Physical Society

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