OWL-S: Experiences and Directions, 6th of June, Austria, 2007

OWL-S: Experiences and Directions, 6th of June, Austria, 2007
A Semantic Web Service Discovery and Composition Prototype Framework Using Production Rules Georgios Meditskos Nick Bassiliades Aristotle University of Thessaloniki, Greece Hello, my name is George Meditskos and I am a PhD student in the department of informatics at Aristotle university of Thessaloniki. The paper I am going to present titled “” constitutes an effort to develop a rule-based prototype framework for Web service discovery and composition based on OWL-S profile descriptions of the capabilities. Intelligent Systems and Knowledge Processing group 1 OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Outline Motivation Why OWL-S ? Benefits of Rules and Object-Oriented Paradigm Discovery Composition Open Issues I will begin the presentation stating the motivation of our approach and the reason why we chose OWL-S as the description ontology of Web services. Then i will make a short introduction about rules and OO paradigm, giving the advantages that they offer in their application on OWL domain. I will continue by introducing the notion of the OO paradigm in the domain of OWL-S descriptions and how we can realize discovery and composition of Web services. Finally, i will refer to open issues regarding our approach. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Motivation Handle Semantic Web Services using a rule engine + robustness, declarativeness Investigate the usability of the Object-Oriented representation on: OWL reasoning Matchmaking Composition + modularity, “indexing” OWL-S Our motivation is based mainly on two factors. Firstly we want to develop a rule based framework for semantic web services exploiting the advantages that a rule system offers, such as the decrarativeness and robustness, features that have been obtained throughout the years of development. Secondly we want to investigate the benefits of using an OO representation of ontology information on OWL reasoning, matchmaking and composition. OO representation offers modularity during the development of programs and also it can be used as an indexing technique as we will describe latter. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

ProSeDisCo Production rules for Service Discovery and Composition The framework combines: Production rules for OWL(-S) reasoning Object-oriented representation of ontologies Underlying system: O-DEVICE (OO Rule-based OWL reasoner) Our effort has resulted in the development of Prosedisco, a rule based framework for service discover and composition. Prosedisco combines two paradigms: it uses production rules to perform OWL reasoning and secondly follows an OO approach. The combination of these two paradigms is realized by using an OO rule-based reasoning engine we have developed calle3d O-DEVICE Prosedisco is based on O-DEVICE, a production rule system for OWL reasoning. We have developed O-DEVICE on top of CLIPS that offers the opportunity to handle ontological information using an OO language. In that way, we have defined an OO rule based algorithm to perform service discovery and composition. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Why OWL-S? Quick implementation of a prototype Existing infrastructure for OWL reasoning Loading the ontology into O-DEVICE Implementing production rules to facilitate discovery & composition WSMO introduces a more complicated conceptual model OWL-S is a more mature approach Our choice was mainly based on the fact that we wanted to develop quickly a prototype. Since OWL-S is an OWL ontology, we just had to import OWL-S into O-DEVICE and to implement appropriate rules in order to facilitate discovery and composition. On the other hand, for example WSMO, defines a more complicated conceptual model for Web services, introducing mediators and it was more difficult and time consuming to implement a prototype framework on this model. Moreover we consider OWL-S a more mature approach which has been used in many research efforts. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Object-Oriented Rules for OWL
A transformation procedure of OWL into the OO model (CLIPS/COOL) A rule-based approach transforms: OWL classes into COOL classes OWL properties into class slots OWL instances into COOL objects Object-Oriented entailments Rules operate over the OO schema In O-DEVICE we have defined a transformation procedure of ontology information into the OO model. Using rules, we transform OWL classes onto COOL classes, OWL properties into COOL class slots and OWL instances into COOL objects. But since the OO model cannot capture by itself the complete semantics of OWL, we have implemented production rules that operate over the OO schema and implement numerous OWL entailments. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Why Rules ? Simplistic model for knowledge representation Domain experts Programmers Ontologies with rule programs Flexible and declarative programming Rule programming based on OWL semantics In general, rules offer a simplistic model for knowledge representation for both programmers and domain experts. Experts find it easier to express knowledge in a rule-like format and programmers usually find rule-base programming easier to understand and manipulate, decoupling computation from control. Moreover, the use of a rule engine to perform OWL reasoning gives the advantage of using ontological information into rule programs. For example, users instead of defining the KB using facts, can import an OWL ontology and after the materialization procedure of OWL semantics, can define rules in order to built expert systems. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Advantages of the OO Approach
Acts as an indexing technique Exploits the message passing mechanism (send [george] get-hasBrother) Single & multiple inheritance is treated by the OO environment Quick determination of subsumption relations Built-in functions for determining hierarchical relationships Can be implemented in any OO environment Moreover, the OO model we create gives a number of advantages. Firstly it acts as an index of the ontology information. We can exploit the message passing mechanism of the underlying OO environment in order to retrieve property values of specific objects. Furthermore we can use built-in functions to determine hierarchical relations of classes and in that way to identify subsumption relationships. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

OWL-S and OO Model Web service capabilities are defined as instances of the Profile class Each profile is mapped into the corresponding OO Profile class Result: An OO KB with object advertisements Requests are also Profile objects Matchmaking: Determine similar objects In our prototype implementation, we are based only on the input and output parameters of the Profile class of OWL-S ontology. Each Profile instance is mapped into a profile OO class resulting in a KB that contains OO objects as service advertisements. Moreover, each query is also an instance of the profile class and it is also mapped as an OO profile object. Thus the matchmaking procedure is based on the determination of Profile object similarities between a query and advertisements objects. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Semantic Discovery Goal Determine Web services that satisfy certain requirements Methodology Check input/output parameters Compute datatype or class relationships The goal is to determine how similar are two objects, that is the query object and the advertisement object of a Web service. The methodology we follow examines the inputs and output values of the profile objects and based on the referred values which can be either datatypes or classes, we compute a similarity score. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Datatypes Direct comparison of datatypes Three types of match Exact (e.g. xsd:int, xsd:int) Numerical (e.g. xsd:int, xsd:float) Mismatch (e.g. xsd:double, xsd:boolean) Different weights for each match exact > numerical > mismatch To determine datatype similarity, we follow a direct comparison of datatype in the objects inputs output parameters. We define three types of match: exact that refers to similar datatypes numerical that exists between numerical datatypes only and mismatch when there is not exact or numerical match between the datatypes. In order to define an order of preference among the matches, we give different weights to each match and thus the datatype similarity is determined by aggregating these weights and dividing by the number of parameters. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Classes Examine class hierarchy Subsumption relations are determined over the OO schema Using of built-in CLIPS functions subclass(A, B) -> TRUE superclasses(A) -> […] Advantage Quick determination The similarity between classes is determined by examining the OO schema that is created after the transformation procedure. We use the built-in functions of CLIPS in order to determine subclass relationships. The advantage of this approach is that class relationships can be easily and quickly identified. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Three types of class match
Exact The same class Equivalent class Plug-in or subsume Hierarchical relationship Fail There is not hierarchical relationship Disjoint classes Matchmaking: Checking advertisements inputs against a request inputs and request outputs against advertisements outputs We consider four types for class match: Exact when the two classes are the same or equivalent Plug-in or subsume when the classes have a hierarchical relationship Sibling when the two classes are not hierarchically related but they have a common superclass and fail when nothing of the above holds. In that way we are able to perform matching not based only on subsumption relations but based also on sibling relations OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Composition The algorithm is implemented with production rules Traverses Profile objects Finds appropriate objects (advertisements) that satisfy requirements at each step More than one composition plans Scores each plan based only on I/O matches The composition algorithm is implemented by production rules. The rules traverse the profile objects and select these that satisfy the requirements at each step of the composition plan based on the discovery procedure. The result may contain more than one composition plans that are sorted according to the matching scores of the query I/O against the composition plan I/O OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Composition Procedure
Selects Web services that satisfy all or some request outputs Creates all the combination sets of Web services that satisfy all request outputs Recursive algorithm in order to satisfy each set’s inputs The algorithm terminates when all the inputs of all Web services of a specific set are satisfied The composition algorithm follows a bottom-up approach: it starts from the request outputs and creates all the possible composition plans that achieve these goals based on the request inputs selects all the Web services that achieve some (or all) request outputs in order to determine the last services of the composition plan based on the discovery approach we have seen previously creates sets of web services where each set satisfy all the request outputs. follows a recursive algorithm in order to satisfy each set’s inputs. The inputs can be satisfied either by request inputs or by outputs of other services. Each time an appropriate Web service is found, it is added to the corresponding composition plan set. The algorithm for a specific set terminates when all the inputs of the contained Web services are satisfied OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Example Request(in:{Title}, out:{Price, Publisher}) WS1(in:{Title}, out:{Publisher}) WS2(in:{Title}, out:{Person}) WS3(in:{Title}, out:{ISBN}) WS4(in:{ISBN}, out:{Price}) OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Example (cont.) Two initial sets of Web services OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Example (cont.) Request(in:{Title}, out:{Price, Publisher}) OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Open Issues Non-functional properties Web service discovery based on non-functional properties Classification Classification of Web services based on their functionality Filtering of Web services during discovery Preconditions – Effects Filtering of Web services Mapped into production rules We plan to augment the discovery procedure of web services by considering also non functional properties such as the location of a service or the price of a product. In that way the algorithm for determining the similarity between profile objects should match not only datatype and classes but also actual values. Furthermore, profile objects can be classified into a taxonomy, according to the functionality of the Web services. In that way, we can restrict the space where the production rules are applied during discovery Moreover we plan to investigate the information of the Process Model during both the discovery of Web services as well as during execution. OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Open Issues Process Model Both on discovery and invocation Current Work Exploit more hierarchical relations stemming from the OO model on discovery sibling classes OWL-S: Experiences and Directions, 6th of June, Austria, 2007

Thank you for your attention…
OWL-S: Experiences and Directions, 6th of June, Austria, 2007

OWL-S: Experiences and Directions, 6th of June, Austria, 2007

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