The Service-Finder Project aims to develop a platform for semantic web service discovery by: 1) Automatically generating semantic descriptions of web services by analyzing various sources of information. 2) Creating clusters of both users and services to improve search accuracy. 3) Providing a web 2.0 portal and APIs to demonstrate the technologies and support advanced search and recommendations.

1. The Service-Finder Project

3. The Mission Service Finder Semantics Knowledge Representation & Reasoning Web Services As a basic tool to implement a S ervice O riented A rchitecture Semantic Web Services As a means to realize S ervice O riented A rchitecture Web 2.0 User clustering User-Resource correlation Semantic Search Conceptual Indexing Semantic Matching Automatic Semantic Annotation Combining smart-machine and smart-data Service-Finder aims at developing a platform for service discovery in which Web Services are embedded in a Web 2.0 environment R ealizing W eb S ervice D iscovery at W eb S cale

4. The Consortium recognized leadership excellent understanding *** * Each member of the consortium is a recognized leader in some of the technical competencies required by Service-Finder and, at the same time, they have excellent understanding of all required competencies Technical Competencies Web Services * *** * Semantic Web Services * *** * *** Semantics * * *** *** Automatic Semantic Annotation * *** Semantic Search Engines *** * *** Web 2.0 *** * *

8. Improving the State of the Art Feature State of the art Improvement Tool Architecture for a novel approach to lightweight semantic service discovery Approaches based on a registration process or an editorial team Enables to scale service discovery with the upcoming increase of publicly available services No tools Largest and most accurate set of publicly available services Indexes of current document based search engines / specialized portals only containing subset of services Focused crawler able to identify services and related information Specialized crawling software, data set Automatic metadata creation enabling semantic search of Web service repositories Innovative; under-researched Metadata generation from Web 2.0 data and services Scientific report, evaluation data set and semantic annotation Web service Integration of formal and informal (textual) metadata for discovery of Web services Indexed textual descriptions Hybrid match-making algorithm for Web Services Improved OntoBroker to handle both textual and formal knowledge Explicit representation of context for enhancing service requests Context usually considered only manually and implicit Enabling context knowledge to be used for specifying and answering queries Integrated rules support for reasoning over service requests Automatic creation of both user and service clusters General-purpose clustering techniques exist but currently there are no algorithms that jointly cluster users and services Algorithms for clustering users on the basis of their profiles and behaviours; algorithms for clustering services on the basis of semantic annotations and users Software components implementing the user and service clustering. Innovative interface that combines Web 2.0 features and service related features Current Web 2.0 portals do not include semantic metadata. Techniques that enable handling of semantic metadata in Web 2.0 portals Web 2.0 Interface leveraging over semantic metadata and customized contents

9. Positioning Service-Finder wrt. other projects DIP InfraWebs ASG TAO SIMDAT Service-Finder Large-scale Web Service search engine Discovery through registry Discovery through distributed repositories Discovery through internal registry ----- Discovery through internal registry Discovery of previously unknown services through automated crawling and annotation Metadata Creation for Web Service Repositories ----- ----- ----- Ontology learnt from legacy data and source code ----- Semantic search of Web service repositories via metadata derived from Web 2.0 services and data; reconciling contradictory information; and employing a community process User and Service Clustering ----- Pre defined user groups ----- ----- ----- Clusters of both users and services are created automatically and adapted constantly with respect to the user behaviours Conceptual Indexing and Matching Semantic “heavy” discovery methodology Multi phased discovery Based on planning ----- Ontology-based requirement matching Combines formal reasoning and Information Retrieval to retrieve services Web-service Portal Industry specific use cases Industry specific use cases Industry specific use cases ----- Industry specific use cases A generic and highly innovative semantic portal collects Web Services of different kinds and provides advanced functionalities for human consumption

13. Service-Finder road-map June 2008 Jan 2008 Dec 2008 Dec 2009