In recent years, the amount of information on the Internet has increased exponentially developing great interest in selective information dissemination systems. The publish subscribe paradigm is particularly suited for designing systems for routing information and requests according to their content throughout wide-area network of brokers. Current publish subscribe systems use limited syntax-based content routing but since publishers and subscribers are anonymous and decoupled in time, space and location, often over wide-area network boundary, they do not necessarily speak the same language. Consequently, adding semantics to current publish subscribe systems is important. In this paper we identify and examine the issues in developing semantic-based content routing for publish subscribe broker networks.
Some systems @cite_9 @cite_13 use inference engines to discover semantic relationships between data from ontology representations. Inference engines usually have specialized languages for expressing queries different from the language used to retrieve data, therefore user queries have to be either expressed in or translated into the language of the inference engine. The ontology is either global (i.e., domain independent) or domain-specific (i.e., only a single domain) ontology. Domain-specific ontologies are smaller and more commonly found than global ontologies because they are easier to specify. Additionally, there are systems that use mapping functions exclusively and do not have inference engines @cite_3 @cite_12 . In these systems, mapping functions serve the role of an inference engine.
@cite_9 @cite_13 @cite_12 @cite_3
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Ontologies have been increasingly used over the past few decades in a wide range of application domains spanning both academic and industrial communities. As ontologies are the cornerstone of the Semantic Web, the technologies developed in this context, including ontology languages, specialized databases and query languages, have become widely used. However, the expressiveness of the proposed ontology languages does not always cover the needs of specific domains. For instance, engineering is a domain for which the LIAS laboratory has proposed dedicated solutions with a worldwide recognition. The underlying assumptions made in the context of the Semantic Web, an open and distributed environment, do not apply to the controlled environments of our projects where the correctness and completeness of modeling can be guaranteed to a certain degree. As a consequence, we have developed over the last decades a specialized standard ontology language named PLIB associated with the OntoDB OntoQL platform to manage ontological engineering data within a database. The goal of this paper is threefold: (1) to share our experience in manipulating ontologies in the engineering domain by describing their specificities and constraints; (2) to define a comprehensive classification of ontologies with respect to three main research communities: Artificial Intelligence, Databases and Natural Language Processing and (3) to present a persistent solution, called OntoDB, for managing extremely large semantic data sets associated with an ontological query language, called OntoQL. These objectives are illustrated by several examples that show the effectiveness and interest of our propositions in several industrial projects in different domains including vehicle manufacturing and CO (_2 ) storage. Ontologies are a crucial tool for formally specifying the vocabulary and relationship of concepts used on the Semantic Web. In order to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. Ontology translation is required when translating datasets, generating ontology extensions, and querying through different ontologies. OntoMerge, an online system for ontology merging and automated reasoning, can implement ontology translation with inputs and outputs in OWL or other web languages. Ontology translation can be thought of in terms of formal inference in a merged ontology. The merge of two related ontologies is obtained by taking the union of the concepts and the axioms defining them, and then adding bridging axioms that relate their concepts. The resulting merged ontology then serves as an inferential medium within which translation can occur. Our internal representation, Web-PDDL, is a strong typed first-order logic language for web application. Using a uniform notation for all problems allows us to factor out syntactic and semantic translation problems, and focus on the latter. Syntactic translation is done by an automatic translator between Web-PDDL and OWL or other web languages. Semantic translation is implemented using an inference engine (OntoEngine) which processes assertions and queries in Web-PDDL syntax, running in either a data-driven (forward chaining) or demand-driven (backward chaining) way. This paper describes a framework for ontology-based flexible discovery of Semantic Web services. The proposed approach relies on user-supplied, context-specific mappings from an user ontology to relevant domain ontologies used to specify Web services. We show how a user's query for a Web service that meets certain selection criteria can be transformed into queries that can be processed by a matchmaking engine that is aware of the relevant domain ontologies and Web services. We also describe how user-specified preferences for Web services in terms of non-functional requirements (e.g., QoS) can be incorporated into the Web service discovery mechanism to generate a partially ordered list of services that meet user-specified functional requirements. We explore how to support the creation of customized visualizations of ontology instance data through the specification of ontology mappings. We combine technologies from the disciplines of software modeling and ontology engineering. The feasibility of our approach is demonstrated by extending an existing ontology mapping tool, CogZ, to translate ontology mappings into software model transformation rules. The tool uses these transformations to automatically convert domain instance data into data that conforms to a model describing a visualization. After this transformation, a visualization of the domain instance data is generated.