Web Services Discovery and Recommendation Based on Information Extraction and Symbolic Reputation
This work addresses the challenge of web services representation for developers and users, but it is incremental as it builds on existing methods like WSDL-based descriptions.
The paper tackled the problem of representing web services for discovery and recommendation by introducing a text tagging method to filter noisy descriptions and proposing a symbolic reputation representation based on service relationships. In experiments with real-world web services, these representations improved discovery and recommendation, though specific numerical gains were not provided.
This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the experimentation using real world web services.