An Ontology-Driven Architecture for Re-using Semantic Web Services

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1 An Ontology-Driven Architecture for Re-using Semantic Web Services Carlos Granell, Dolores María Llidó, Rafael Berlanga, and Michael Gould Department of Information Systems, Universitat Jaume I, Castellón, Spain Abstract. As more semantic web services become on the Internet, it is feasible that users collaborate among them to save efforts in complex web solutions by sharing and reusing existing semantic web services, rather than building them from scratch. In this paper we focus on the problem of discovering and reusing semantic web services at a high level of abstraction centered on the concept of abstract pattern, that provides a logical view of a service composition and a great power of classification because it uses ontology concepts and relations. To support semantic discovery and reuse, we present an ontology-driven architecture that enables users to specify structured queries against to knowledge base of abstract patterns and services. Keywords: Ontology-driven architecture, semantic web services, abstract patterns, knowledge reuse. 1 Introduction The Model Driven Architecture (MDA) [8] has recently become one of the most emerging techniques which are applied to solve the problem of web service (WS) composition. In short, MDA defines three level of abstraction or viewpoints from which a service composition can be represented, namely Computer-Independent Model (CIM), Platform-Independent Model (PIM) and Platform-Specific Model (PSM). However, most MDA-based approaches for WS composition are focused mainly in the PIM and PSM levels [2][4][5], following a top-down approach in which initial PIM models are transformed into PSM models until reaching the required implementation, ignoring then the advantages of reusing CIM models. For example, the same CIM model can be applied to different contexts, though the generated set of PIMs requires different configurations according to each context features. In this paper we address the lack of reuse of services and knowledge presenting an ongoing ontology-driven architecture that provides a CIM s Knowledge Base capable of reasoning classifying, composing, and querying over services in an abstract way. Also we describe a querying-driven process at CIM level that achieves structured semantic answers given user goals or queries, underlying the importance of CIM for improving the reuse of services and knowledge between CIM and PIM levels. This architecture is being applied to emergency management scenario. In this scenario, a new PIM is usually required when a new emergency arise, and it should be R. Meersman, Z. Tari, P. Herrero et al. (Eds.): OTM 2007 Ws, Part II, LNCS 4806, pp , Springer-Verlag Berlin Heidelberg 2007

2 1216 C. Granell et al. designed and deployed as quick as possible. Thus, the reuse of already built models (at different level of abstraction) can be crucial. In the current implementation, final PSM models are WSBPEL processes over existing (composite) WSs. The paper is structured as follows. Next section describes the proposed ontologydriven architecture. Section 3 and 4 are devoted to the CIM level, describing the knowledge base structure and the query processing. Section 5 concludes the paper. 2 Ontology-Driven Architecture Figure 1 illustrates the ongoing ontology-driven architecture that enables using Semantic Web mechanisms in conjunction with MDA techniques to facilitate both Semantic Web Service (SWS) composition and reuse. We first propose the use of DLsemantic descriptions to deal with two key aspects of WSs: the classification of services (with respect to their IOPEs) and the classification of abstract patterns that can be applied over them. The former enables reason and compose properly candidate semantic service descriptions (concepts) which may form part of the target solution, whereas the latter allows us to validate possible abstract patterns that satisfy the selected service concepts. In our approach, both elements that are fully expressed as logic-based ontologies (with OWL-DL expressivity [1]) constitute the semantic required to build the CIM s Knowledge Base (see Section 3). At CIM level, user goals are specified in terms of semantic descriptions and abstract patterns, disregarding all details about concrete services, their specification languages (e.g. OWL-S [6] or WSML [9]) and execution platforms. Thus, the CIM s Knowledge Base (KB) is aimed at representing just the concepts that can be required for specifying goals, pattern structures (i.e. workflow) and the abstract services and operations they involve. Users will be able to define abstract requests in terms of these concepts to discover and re-use existing concrete solutions. The CIM s KB is populated with existing semantic services and their compositions. In this way, it is necessary to define the transformations from PIM languages (e.g. OWL-S and WSML) to CIM abstract specifications. Basically, these transformations must drop all PIM concepts not regarded at the CIM KB and classify PIM models according to CIM KB concepts. The CIM also provides a pattern matching tool able to retrieve all the abstract specifications that best fit user requests. Currently, this matching tool is based on approximate XML retrieval techniques [10]. This kind of techniques allows users to define the similarity function they want to apply to retrieve abstract patterns. The similarity measure can rely on a reasoner to check component subsumption with respect to the CIM KB. As this part goes beyond the scope of this work, we will not give further details. Once users have selected the abstract patterns of their interest, they proceed to build a PIM model, which can include concrete services and adaptors taken from the CIM KB. As shown in Figure 1, we regard three possible paths at the PIM layer. The first one consists of WSMO specifications, which use the representation language WSML. The second one consists of OWL-S specifications. Finally, the third path represents an ad-hoc platform-independent method for building composite services. The two first paths use the specific tools for matching and validating semantic

3 An Ontology-Driven Architecture for Re-using Semantic Web Services 1217 services, which also include specific execution engines at the PSM level. They constitute two big infrastructures that regard all the stages for managing and executing SWS. Instead, the third one is aimed at combining independent components for the composition and validation of SWS that do not completely fit in with these platforms. More specifically, we have adapted our previous research works [3] and [7] to implement this path. CIM (discovery reasoning, reuse, repurposing) User Pattern Matching Abstract Patterns / instances Reasoner OWL-DL Knowledge Base (concepts, adaptor instances, service instances abstract PIM (matching, grounding) Goal PIM Goal PIM (1) (2) (3) WSML specific ation OWL-S specific ation MatchMaker SCG Graph PIMs Base with service instances (OWL-S & WSMO) Grounding WSDL PSM (execution) WSMX engine OWL-S API WS-BPEL process PSMs Base (WS-BPEL files, WSMO orchestrations, java programs, etc.) Fig. 1. Ontology-driven architecture for semantic service discovery and reuse The PIM layer also includes a knowledge base aimed at storing all the details of concrete PIM models that were omitted at the CIM layer. These details are mainly the concrete IOPEs that are specified in the concrete services and their orchestrations. Notice that PIM models can be expressed with different languages. Therefore horizontal transformations between PIM models could be necessary if parts of them are merged to create new ones. Although this can be partially at the CIM layer, concrete IOPEs specified at PIM layer will require such transformations. Regarding the third path of the architecture, SWS composition is performed through a Semantic Composition Graph (SCG) [7] whose construction is guided by the abstract pattern. The SCG relates CIM concepts with semantic service descriptions stored in the PIM base. From a SCG we can extract several execution plans, which must be validated with respect to the IOPEs of the involved SWS. Finally, the SCG is grounded, and by applying a transformation PIM-PSM an executable WS-BPEL is built [3]. Finally, if the resulting PSM is executed successfully, then the corresponding models at different levels are registered for future use. So, the abstract pattern (knowledge) is included in the CIM s DL- Knowledge Base, whereas the PIM model updates the PIMs Base.

4 1218 C. Granell et al. 3 CIM s Knowledge Base The CIM s KB comprises the underlying domain ontology (e.g. a geospatial ontology or a biomedical ontology), the semantic descriptions of the available services and adaptors, and the semantics of the composition abstract patterns. We assume that the KB is expressed in DL [1], which provides the basis for the OWL DL and also for the OWL-S [6]. Therefore, the KB can be exported-imported through the OWL-DL language. The KB also provides a reasoner which is able to classify concepts and services and check for composition consistency. The main concept of the CIM s KB is the abstract pattern. An abstract pattern represents a solution for certain goal, which requires the combined use of several services and adaptors. The abstract pattern is defined with the following elements: the goals it solves, the profile describing the inputs and outputs, the operational semantics and the pattern structure. All these elements are defined as DL expressions over the concepts defined in the CIM domain ontology. Notice that, contrary to current approaches that use multiple ontologies, our approach aims at providing a common and simple ontology for specifying all the abstract patterns involved in a scenario. Fig. 2. Structure and concepts for defining abstract patterns In order to simplify as much as possible the semantic description of abstract patterns, we only introduce a few concepts, namely: $FWRU, 6HUYLFH $GDSWRU 3URILOH and 2S/RJLF. These concepts have associated the following properties: KDV3URILOH KDV,QSXW, KDV2XWSXW, KDV2S/RJLFKDV3UH&RQGLWLRQ and KDV(IIHFW. The former defines the semantics of the services according to their inputs (KDV,QSXW) and outputs (KDV2XWSXW). The property KDV2S/RJLF expresses the relation between each service and the state concepts it requires (KDV3UH&RQGLWLRQ) and modifies (KDV(IIHFW). As an example, the following axioms in the KB describe a generic 6HUYLFH that provides nearest geospatial objects: 1HDUHVW2EMHFW 6HUYLFH KDV3URILOH3RV2EMHFW KDV2S/RJLF3D\PHQW/RJLF 3RV2EMHFW 3URILOH KDV,QSXW*HR3RVLWLRQ KDV2XWSXW*HR2EMHFW 3D\PHQW/RJLF 2S/RJLF KDV3UHFRQGLWLRQ8VHU KDV&UHGLW! The CIM s KB is completed with the abstract patterns. In general, workflow patterns serve as composition operators to specify how services should be combined. Since most current languages for WS composition like WSBPEL come from the

5 An Ontology-Driven Architecture for Re-using Semantic Web Services 1219 workflow area, we have derived a set of abstract patterns suitable for WSs [3], which provide several advantages with respect to common control patterns available in other languages. First, intentionality, we have maintained the number of redundant workflow patterns to a minimum, in contrast to overlapping and alternative patterns present for example in WSBPEL, leading to simpler but complete set of abstract patterns. In addition, such abstract patterns are quite general to be independent enough of concrete control patterns used in composition languages. For example, the abstract pattern SEQ that represents a sequence of services can be transformed into the corresponding construct either in WSBPEL or in OWL-S. Although abstract patterns are applicable to concrete services at PSM level, they can also be semantically characterized to be used both within the CIM s KB for defining semantic queries and as part of PIM models. In contrast to other approaches, introducing abstract patterns at CIM level is a key aspect in our approach because it lets users describe more complex and structured queries. Users can not only specify semantic queries in terms of IOPEs but also determine how candidate services should be combined. Table 1 shows the axioms associated to each of these abstract patterns. Table 1. Semantics for abstract patterns. A and B are concepts denoting services, whereas c is a concept denoting a condition over state variables (operational logic). Abstract Patterns Axioms associated to abstract patterns 6 6(4$% KDV2XWSXW $ KDV,QSXW %LVVDWLVILDEOH 6 6HUYLFH KDV3URILOH 3URILOH KDV,QSXW KDV,QSXW $ KDV2XWSXW KDV2XWSXW % 6 $1'$% 6 6HUYLFH KDV3URILOH3URILOH KDV,QSXW KDV,QSXW $ KDV,QSXW % KDV2XWSXW KDV2XWSXW $ KDV2XWSXW % 6 ;25$% 7KHVDPHDV$1'6HPDQWLFVLQFOXGHGDW3,0OHYHO 6 $1'',6&$ 6 6HUYLFH KDV3URILOH KDV3URILOH $ +HUH$GHQRWHVWKHEDJRIVHUYLFHVLQYROYHGLQWKHSDWWHUQ 6 /223F$ 7KHVDPHDV$1'',6&'HWDLOVLQFOXGHGDW3,0OHYHO 6,)F$% 7KHVDPHDV$1''HWDLOVLQFOXGHGDW3,0OHYHO As an example, consider the following abstract pattern whose goal is to buy stock units of certain market if its value decreases after two observations:,)6(4$1'$$9doxh'liihuhqfh!'lii$ Here, $ is the abstract service that provides the stock value of certain market. $ is the service that permits buying a number of stocks of certain market. These can be defined as follows: $ 6HUYLFH KDV3URILOH KDV,QSXW0DUNHW KDV2XWSXW1XPEHU $ 6HUYLFH KDV3URILOH KDV,QSXW0DUNHW KDV2S/RJLF%DQN&OLHQW %DQN&OLHQW 2S/RJLF KDV3UHFRQGLWLRQ8VHU KDV&HUWLILFDWH; 9DOXH'LIIHUHQFH is an adaptor which takes two numbers as input and gives its difference. It is worth mentioning that adaptors are treated at the CIM layer in the same way as abstract services. They are distinguished at the PIM layer. Finally, 'LII

6 1220 C. Granell et al. denotes the input parameter of the abstract pattern that indicates the tolerance for buying stocks. Notice that this abstract pattern can be re-used in any situation where the difference of two observations is used as criterion for a decision. For example, we can use a similar abstract pattern over meteorological services for a fire scenario. 4 Transforming Abstract Patterns This section describes briefly how the logic structure of the abstract patterns at CIM level can be transformed into specific OWL-S control constructs [6] and WSMO choreographies [9] at PIM. Both approaches define different formal semantics for WS composition. While OWL-S declares a set of controls to define a procedural specification of the services involved in a composition, the representation of WSMO choreographies is based on a state-based machine that consists of states and transition rules to express how the states evolve. Applying transitions rules provokes state changes that directly change the values of concepts and relations between ontologies. Then, OWL-S control constructs are closely related to the semantics of the structural part of the abstract patterns (see Table 2), while WSMO choreography is rather concerned with the operational logic of abstract patterns (see Table 3). In Table 1, the semantic meaning of some abstract patterns englobe multiple OWL- S control constructs [3]. For example, SEQ pattern serves for the sequence, any-order Table 2. Relationships between abstract patterns and OWL-S Abstract Patterns Control Constructs in OWL-S Process 6 6(4$% 6HTXHQFH$%$Q\2UGHU$%UHVWULFWHGWRDFHUWDLQRUGHU 6 $1'$% 6SOLW-RLQ$%6SOLW$%ZLWKV\QFKURQL]DWLRQEDUULHU 6 ;25$% &KRLFH$% 6 $1'',6&$ 6SOLW$%ZLWKRXWV\QFKURQL]DWLRQEDUULHU&KRLFH$% 6 /223F$ 5HSHDW:KLOH$5HSHDW8QWLO$,WHUDWH$$OOFRQWUROFRQVWUXFWV LQVWDQFHVLWHUDWHXQWLODFRQGLWLRQFKROGVWUXHRUIDOVHVSHFLILHGZLWK DZKLOH&RQGLWLRQRUDQXQWLO&RQGLWLRQSURSHUWLHV 6,)F$%,I7KHQ(OVH $ % ZKHUH FRQGLWLRQ F LV WUDQVODWHG LQ D LI&RQGLWLRQ SURSHUW\ Table 3. Relationships between abstract patterns and WSMO Abstract Patterns WSML choreographies 6 6(4$% 3LSHGUXOHXSGDWHUXOHDGGGHOHWHRUXSGDWHIDFWV 6 $1'$% &KRLFHUXOHXSGDWHUXOHDGGGHOHWHRUXSGDWHIDFWV 6 ;25$% &KRLFHUXOHXSGDWHUXOHDGGGHOHWHRUXSGDWHIDFWV 6 $1'',6&$ 1RWDYDLODEOH 6 /223F$ )RUDOO UXOH ZLWK D ORJLFDO H[SUHVVLRQ FRQGLWLRQ XSGDWH UXOH DGG GHOHWHRUXSGDWHIDFWV 6,)F$%,I7KHQUXOHZLWKDORJLFDOH[SUHVVLRQFRQGLWLRQXSGDWHUXOHDGG GHOHWHRUXSGDWHIDFWV

7 An Ontology-Driven Architecture for Re-using Semantic Web Services 1221 and unordered control construst. Also, the conditions expressed in the abstract patterns are used for the control constructs conditions. Finally, abstract patterns can be nested in order to provide structured patterns, which can be viewed as OWL-S composite processes that maintain certain state and includes control constructs. These transformations can be implemented by means of XSLT. Whereas CIM to OWL-S transformation can be done straightforward as both are based on OWL, CIM to WSMO must be made through WSML templates, which are filled with the proper CIM elements. Notice that new transformations can be performed at the PIM layer to include concrete transitions rules and other details not regarded at the CIM layer. 5 Conclusions and Future Work The proposed ontology-driven architecture in a MDA-like framework allows us to take advantage of reusing CIM models for composing and reusing SWS. First, we have described a CIM s DL KB capable of reasoning classifying, composing, and querying over services in an abstract way, and a querying-driven process at CIM level that achieves structured semantic answers given user goals, underlying the importance of the CIM models for improving the reuse of services and knowledge between CIM and PIM levels. Future work includes rule-based reasoning to simulate the evolution of state variables for checking that all compositions are consistent with respect to the corresponding business logic conditions. References 1. Baader, F., Calvanese, D., McGuinnes, D., Nardi, D., Patel-Scheneider, P.: The Description Logic Handbook: Theory, Implementation and Applications. CUP, Cambridge (2003) 2. Gannod, G.C., Timm, J.T.E., Brodie, R.J.: Facilitating the Specification of Semantic Web Services Using Model-Driven Development. J. of Web Services Research 3, (2006) 3. Granell, C., Gould, M., Grønmo, R., Skogan, D.: Improving Reuse of Web Service Compositions. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web LNCS, vol. 3590, pp Springer, Heidelberg (2005) 4. Grønmo, R., Jaeger, M.C.: Model-Driven Semantic Web Service Composition. In: Proceedings of the APSEC 2005, IEEE Press, Los Alamitos (2005) 5. Marcos, E., Acuña, C.J., Cuesta, C.E.: Integrating Software Architecture into MDA Framework. In: Gruhn, V., Oquendo, F. (eds.) EWSA LNCS, vol. 4344, pp Springer, Heidelberg (2006) 6. Martin, D., et al.: Bringing semantics to web services: The owls approach. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC LNCS, vol. 3387, pp Springer, Heidelberg (2005) 7. Paraire, J., Berlanga, R., Llidó, D.M.: Resolution of Semantic Queries on a Set of Web Services. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA LNCS, vol. 3588, pp Springer, Heidelberg (2005) 8. Sigel, J.: Developing in OMG s Model-Driven Architecture Object Management Group White Paper (2001), Available at ftp://ftp.omg.org/pub/docs/omg/ pdf 9. Roman, D., Scicluna, J., Nitzsche, J., Fensel, D., Polleres, A., de Buijin, J.D.: Ontologybased Choreography. WSMO Final draft (February 2007) 10. Sanz, I., Mesiti, M., Guerrini, G., Berlanga, R.: ArHeX: An Approximate Retrieval System for Highly Heterogeneous XML Document Collections. In: Grust, T., Höpfner, H., Illarramendi, A., Jablonski, S., Mesiti, M., Müller, S., Patranjan, P.-L., Sattler, K.-U., Spiliopoulou, M., Wijsen, J. (eds.) EDBT LNCS, vol. 4254, Springer, Heidelberg (2006)