SWIM is a knowledge-based system for building Web Service Domains, which are collections or communities of related Web Services that are mediated and/or aggregated by a single Web Service, called the Mediator Service that functions as a proxy for them. When a requestor sends a message to the Mediator Service our system will select one or more of the Web Services to dispatch the message and will fuse the results returned by the selected services. The selection of Web services and the algorithm for fusing the results is defined by the administrator of the Service Domain using a declarative rule language, called X-DEVICE. SWIM system offers services for registering new Web Services and Service Domains. The main advantage of the SWIM system, compared to similar proposed approaches is that it allows the easy definition of arbitrary service selection strategies using a logic-based language. Furthermore, it goes beyond the mere conditional re-routing of Web Service requests by allowing combination of results of multiple Web Services leading to a simple logic-based form for Web Service composition.
Tomaco (Tool for Matching and Composition) is a web application for Semantic Web Service matchmaking and composition. It integrates its own, novel set of matching strategies, tailored to the SAWSD/WSDL universal standards, that ranked high on state-of-the-art comparison on both effectiveness and performance. Strategies can be applied on a dynamic repository of existind or user-contributed service collections and ontologies. Users are able to register to upload content and perform customized experiments, choosing between strategies, thresholds and parameters. Results are also accompanied by rating justification. For composition TOMACO employes a Blackbox AI-planning method.
VLEPPO is an integrated system for modeling and solving planning problems. It offers a convenient, intuitive and easy-to-use graphical interface, which allows design, comprehension and maintenance of planning domains and corresponding problems. VLEPPO accommodates for compatibility with standards, as most visual elements present in the system correspond to PDDL elements. Compliance with the PDDL standard is also achieved through the import and export features. VLEPPO provides increased flexibility in integration of external planning systems by employing the current technology of web services. The system was implemented in Java for portability and interoperability purposes.
VDR-Device is a visual integrated environment for developing (creating, editing, running, testing and deploying) defeasible rule bases for the Semantic Web.
O-DEVICE is a deductive object-oriented knowledge base system for reasoning over OWL documents. O-DEVICE exploits the rule language of an existing production rule system, called CLIPS and transforms OWL ontologies into an object-oriented schema of the CLIPS Object-Oriented Language (COOL). It is an extension of a previous system called R-DEVICE. O-DEVICE exploits the advantages of the object-oriented programming model by transforming OWL ontologies into classes, properties and objects of the OO programming language provided within CLIPS, called COOL. A complete list of all the transformations can be found here. The system also features a powerful deductive rule language which supports inferencing over the transformed OWL descriptions. Users can either use this deductive language to express queries or a RuleML-like syntax.
DR-DEVICE is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The most important features of DR-DEVICE are the following: * Support for multiple rule types of defeasible logic, such as strict rules, defeasible rules, and defeaters. * Support for both classical (strong) negation and negation-as-failure. * Support for conflicting literals, i.e. derived objects that exclude each other. * Direct import from the Web of RDF ontologies and data as input facts to the defeasible logic program. * Direct import from the Web of defeasible logic programs in an XML compliant rule syntax (RuleML). * Direct export to the Web of the results (conclusions) of the logic program as an RDF document.
R-DEVICE is a deductive object-oriented knowledge base system for querying and reasoning about RDF metadata. R-DEVICE, transforms RDF triples into objects and uses a deductive rule language for querying and reasoning about them. More specifically, R-DEVICE imports RDF data into the CLIPS production rule system as COOL objects. The main difference between the RDF and our object model is that properties are treated both as first-class objects and as attributes of resource objects. In this way resource properties are gathered together in one object, resulting in superior query performance than the performance of a triple-based query model. Most other RDF storage and querying systems that are based on a triple model scatter resource properties across several triples and they require several joins to query the properties of a single resource. The descriptive semantics of RDF data may call for dynamic redefinitions of resource classes and objects, which are handled by R-DEVICE. R-DEVICE features a powerful deductive rule language which is able to express arbitrary queries both on the RDF schema and data, including generalized path expressions, stratified negation, aggregate, grouping, and sorting, functions, mainly due to the second-order syntax of the rule language, i.e. variables ranging over class and slot names, which is efficiently translated into sets of first-order logic rules using metadata. Furthermore, R-DEVICE rules define views which are materialized and incrementally maintained. Finally, users can use CLIPS functions or can define their own arbitrary functions using the CLIPS host language.
Device is a system that integrates production rules in an active Object-Oriented Database (OODB) system that supports event-driven rules. Production rules are useful for several tasks of active database systems, such as integrity constraint checking, derived data maintenance, database state monitoring, etc. Furthermore production rules can express knowledge in a high-level form for problem solving in Knowledge Base Systems (KBS). Present active OODB systems traditionally provide event-driven rules, which are triggered by events, i.e. database modifications
X-DEVICE is a deductive object-oriented database for managing XML data. X-DEVICE is an extension of the active object-oriented knowledge base system DEVICE. X-DEVICE extends DEVICE by incorporating XML data into the OODB by automatically mapping XML document DTDs to object schemata, without loosing the document's original order of elements. XML elements are represented either as first-class objects or as attributes based on their complexity. Furthermore, X-DEVICE extends the deductive rule language of DEVICE with new operators that are used for specifying complex queries and materialized views over the stored semi-structured data. Most of the new operators have a second-order syntax (i.e. variables range over class and attribute names), but they are implemented by translating them into first-order DEVICE rules (i.e. variables can range over class instances and attribute values), so that they can be efficiently executed against the underlying deductive object-oriented database.
WebDisC is a knowledge-based Web information system for the fusion of classifiers induced at geographically distributed databases. The main features of WebDisC are: 1. A declarative rule language for classifier selection that allows the combination of syntactically heterogeneous distributed classifiers, 2. A variety of standard methods for fusing the output of distributed classifiers, 3. A new approach for clustering classifiers in order to deal with the semantic heterogeneity of distributed classifiers, detect their interesting similarities and differences and enhance their fusion and 4. An architecture based on the Web services paradigm that utilizes the open and scalable standards of XML and SOAP