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Context optimization of AI planning for semantic Web services composition

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Abstract

Web services composition techniques are gaining momentum as the opportunity to establish reusable and versatile inter-operability applications. Many researchers propose their composition approach based on planning techniques. We propose our context aware planning method which comprises global planning and local optimization based on context information. The major technical contributions of this paper are: (1) We propose an ontology-based framework for the context-aware composition of Web services. Context model, which are structured based on OWL-S, captures the Service-related, Environment-related, and User-related context and can be used in an unambiguous, machine interpretable form. (2) We propose context-aware plan architecture and thus is more scalability and flexibility for the planning process, and thereby improving the efficiency and precision. (3) We propose a hybrid approach to build a plan corresponding to a context-aware service composition, based on global planning and local optimization, considering both the usability and adoption. We test our approach on a simple, yet realistic example, and the preliminary results demonstrate that our implementation provides a practical solution.

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Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, 100080, Beijing, China

    Lirong Qiu, Liang Chang, Fen Lin & Zhongzhi Shi

  2. Graduate School of the Chinese Academy of Sciences, 100039, Beijing, China

    Lirong Qiu, Liang Chang & Fen Lin

Authors
  1. Lirong Qiu
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  2. Liang Chang
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  3. Fen Lin
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  4. Zhongzhi Shi
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Corresponding author

Correspondence to Lirong Qiu.

Additional information

This research was partially supported by the National Science Foundation of China (No.90604017), the national 973 Project of China (No.2003CB317004) and the Nature Science Foundation of Beijing (No.4052025).

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Qiu, L., Chang, L., Lin, F. et al. Context optimization of AI planning for semantic Web services composition. SOCA 1, 117–128 (2007). https://doi.org/10.1007/s11761-007-0010-3

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  • DOI: https://doi.org/10.1007/s11761-007-0010-3

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