Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Flexible Interface Matching for Web-Service Discovery
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Enterprise SOA: Service-Oriented Architecture Best Practices (The Coad Series)
Enterprise SOA: Service-Oriented Architecture Best Practices (The Coad Series)
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficiently finding web services using a clustering semantic approach
Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International World Wide Web Conference (WWW 2008)
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A world-wide community of service providers has a presence on the web, and people seeking services typically go to the web as an initial place to search for them. Service selection is comprised of two steps: finding service candidates using search engines and selecting those which meet desired service properties best. Within the context of Web Services, the service selection problem has been solved through common description frameworks that make use of ontologies and service registries. However, the majority of service providers on the web does not use such frameworks and rather make service descriptions available on their web sites that provide human targeted content. This paper addresses the service selection problem under the assumption that a common service description framework does not exist, and services have to be selected using the more unstructured information available on the web. The approach described in this paper has the following steps. Search engines are employed to find service candidates from dense requirement formulations extracted from user input. Text classification techniques are used to identify services and service properties from web content retrieved from search links. Service candidates are then ranked based on how well they support desired properties. Initial experiments have been conducted to validate the approach.