Semantically Aware Web Service Composition Through AI Planning (original) (raw)
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Semantic awareness in automated web service composition through planning
… : Theories, Models and …, 2010
Abstr act. PORSCE II is a framework that performs automatic web service composition by transforming the composition problem into AI planning terms and utilizing external planners to obtain solutions. A distinctive feature of the system is that throughout the entire process, it achieves semantic awareness by exploiting semantic information extracted from the OWL-S descriptions of the available atomic web services and the corresponding ontologies. This information is then used in order to enhance the planning domain and problem. Semantic awareness facilitates approximations when searching for suitable atomic services, as well as modification of the produced composite service. The alternatives for modification include the replacement of a certain atomic service that takes part in the composite service by an equivalent or a semantically relevant service, the replacement of an atomic service through planning, or the replanning from a certain point in the composite service. The system also provides semantic representation of the produced composite service.
An integrated approach to automated semantic web service composition through planning
2012
Abstract The paper presents an integrated approach for automated semantic web service composition using AI planning techniques. An important advantage of this approach is that the composition process, as well as the discovery of the atomic services that take part in the composition, are significantly facilitated by the incorporation of semantic information.
SEMAPLAN: Combining Planning with Semantic Matching to Achieve Web Service Composition
2006 IEEE International Conference on Web Services (ICWS'06), 2006
Composing existing Web services to deliver new functionality is a difficult problem as it involves resolving semantic, syntactic and structural differences among the interfaces of a large number of services. Unlike most planning problems, it can not be assumed that Web services are described using terms from a single domain theory. While service descriptions may be controlled to some extent in restricted settings (e.g., intraenterprise integration), in Web-scale open integration, lack of common, formalized service descriptions prevent the direct application of standard planning methods. In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semiautomated composition tools or directory browsers.
Semantic Web Service Composition using Planning and Ontology Concept Relevance
… , 2009. WI-IAT'09. …, 2009
This paper presents PORSCE II, a system that combines planning and ontology concept relevance for automatically composing semantic web services. The presented approach includes transformation of the web service composition problem into a planning problem, enhancement with semantic awareness and relaxation and solution through external planners. The produced plans are visualized and their accuracy is assessed.
The PORSCE II Framework: Using AI Planning for Automated Semantic Web Service Composition
2009
This paper presents PORSCE II, an integrated system that performs automatic semantic web service composition exploiting AI techniques, specifically planning. Essential steps in achieving web service composition include the translation of the web service composition problem into a solver-ready planning domain and problem, followed by the acquisition of solutions, and the translation of the solutions back to web service terms. The solutions to the problem, that is, the descriptions of the desired composite service, are obtained by means of external domain-independent planning systems, they are visualized and finally evaluated. Throughout the entire process, the system exploits semantic information extracted from the semantic descriptions of the available web services and the corresponding ontologies, in order to perform composition under semantic awareness and relaxation. 2 O. HATZI ET AL.
Context optimization of AI planning for semantic Web services composition
Service Oriented Computing and Applications, 2007
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.
Proceedings. Lecture Notes in …
This paper presents a prototype system that exploits planning and an ontology concept ranking algorithm for composing semantic Web services (PORSCE). The system exploits the inferencing capabilities of a Description Logics Reasoner in order to compute the subsumption hierarchy of the ontologies whose concepts are used in the OWL-S Profile descriptions as input and output concepts. The concept ranking algorithm is applied over this hierarchy in order to determine similar concepts based on different degrees of semantic matching relaxation, such as subclass or sibling hierarchical relationships. The domain independent planning system's role is to semantically search the space of possible compositions of Web services, generating plans according to the desirable level of relaxation.
A synergy of planning and ontology concept ranking for semantic web service composition
Advances in Artificial …, 2008
This paper presents a prototype system that exploits planning and an ontology concept ranking algorithm for composing semantic Web services (PORSCE). The system exploits the inferencing capabilities of a Description Logics Reasoner in order to compute the subsumption hierarchy of the ontologies whose concepts are used in the OWL-S Profile descriptions as input and output concepts. The concept ranking algorithm is applied over this hierarchy in order to determine similar concepts based on different degrees of semantic matching relaxation, such as subclass or sibling hierarchical relationships. The domain independent planning system's role is to semantically search the space of possible compositions of Web services, generating plans according to the desirable level of relaxation.
Semantic Matching to Achieve Web Service Discovery and Composition
The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06), 2006
In this paper, we present a novel algorithm to discover and compose web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. In addition, we integrate semantic and ontological matching with an indexing method, which we call attribute hashing, to enable fast lookup of semantically related concepts.