RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data.

Real-scale Semantic Web applications, such as Web Portals and E-Marketplaces, require the management of voluminous metadata repositories containing descriptive information (i.e., metadata) about the available Web resources and services. Better knowledge about the meaning, usage, accessibility or quality of these resources and services will considerably facilitate the automated processing of both Web content and services. In this context, the Resource Description Framework (RDF) enables the creation and exchange of metadata as any other Web data. Although large volumes of RDF descriptions are already appearing (e.g., as exported Portal catalogs or service descriptions), sufficiently expressive declarative languages for querying both RDF descriptions and schemas are still missing. In this paper, we propose RQL, a new RDF query language, relying on a formal graph model that permits the interpretation of superimposed resource descriptions. RQL is an OQL-inspired adaptation of XML query languages to the peculiarities of RDF but, foremost, is an extension of this functionality for uniformly querying both descriptions and schemas. We illustrate the syntax, semantics and core functionality of RQL bymeans of a set of benchmark queries and report on the performance of RSSDB, our persistent RDF Store, for storing and querying voluminous RDF descriptions.

... In this paper we show how consistency constraints can be expressed and checked. We describe a novel semantics for first order logic that produces links instead of truth values and give an account of our content management strategy. We present the architecture of our service and the results of two substantial case studies that use xlinkit for checking course syllabus information and for validating UML models supplied by industrial partners.

RDF and RDF Schema provide the first W3C standard to enrich the Web with machine-processable semantic data. However, to be able to use this semantic data, a scalable, persistent RDF store and a powerful query engine using an expressive query language are needed. Sesame is an extensible architecture implementing both of these. Sesame can be based on arbitrary repositories, ranging from traditional Data Base Management Systems, to dedicated RDF triple stores. Sesame also implements a query engine for RQL, the most powerful RDF/RDF Schema query language to date. 1

This paper presents the notion of Semantic Associations as complex relationships between resource entities. These relationships capture both a connectivity of entities as well as similarity of entities based on a specific notion of similarity called ρ-isomorphism. It formalizes these notions for the RDF data model, by introducing a notion of a Property Sequence as a type. In the context of a graph model such as that for RDF, Semantic Associations amount to specific certain graph signatures. Specifically, they refer to sequences (i.e. directed paths) here called Property Sequences, between entities, networks of Property Sequences (i.e. undirected paths), or subgraphs of ρ-isomorphic Property Sequences. The ability to query about the existence of such relationships is fundamental to tasks in analytical domains such as national security and business intelligence, where tasks often focus on finding complex yet meaningful and obscured relationships between entities. However, support for such queries is lacking in contemporary query systems, including those for RDF. This paper discusses how querying for Semantic Associations might be enabled on the Semantic Web, through the use of an operator ρ. It also discusses two approaches for processing ρqueries on available persistent RDF stores and memory resident RDF data graphs, thereby building on current RDF query languages.

The necessity for ontology building, annotating, integrating and learning tools is uncontested. However, the sole representation of knowledge and information is not enough. Human information consumers and web agents have to use and query ontologies and the resources committed to them, thus the need for ontology storage and querying tools arises. However, the context of storing and querying

In this paper, we introduce an approach that supports querying for Semantic Associations on the Semantic Web. Semantic Associations capture complex relationships between entities involving sequences of predicates, and sets of predicate sequences that interact in complex ways. Detecting such associations is at the heart of many research and analytical activities that are crucial to applications in national security and business intelligence. This in combination with the improving ability to identify entities in documents as part of automatic semantic annotation, gives a very powerful capability for semantic analysis of large amounts of heterogeneous content. The approach for supporting Semantic Associations discussed in this paper has four main facets. First, it generalizes these associations into three main classes based on their structural properties, allowing us to reason about them in a domain-independent manner. The second is the provision of an operator ρ for expressing queries about such associations. Third, it uses a graph data model for knowledge representation, allowing the semantic associations search techniques to be built upon the graph algorithms for paths, while integrating knowledge from the schema into the search process. The fourth facet is the use of a notion of context, which allows for restricting the search space and for context-driven ranking of results. Just as a Web search engine looks for relevant documents in the current Web, ρ can be seen as discovering and ranking complex relationships in the Semantic Web. In this paper, we demonstrate the need for supporting such complex semantic relationships. We also give a formal basis to the notion of Semantic Associations and give a brief discussion on our overall approach for discovering and ranking them.

This document is part of a research project funded by the IST Programme of the Commission of the European Communities as project number IST-2000-29243

In this paper, we introduce an approach that supports querying for Semantic Associations on the Semantic Web. Semantic Associations capture complex relationships between entities involving sequences of predicates, and sets of predicate sequences that interact in complex ways. Detecting such associations is at the heart of many research and analytical activities that are crucial to applications in national security and business intelligence. This in combination with the improving ability to identify entities in documents as part of automatic semantic annotation, gives a very powerful capability for semantic analysis of large amounts of heterogeneous content.

Abstract � Software product line engineering is a paradigm that advocates the reusability of software engineering assets and the rapid development of new applications for a target domain. These objectives are achieved by capturing the commonalities and variabilities between the applications of a target domain and through the development of comprehensive and variability-covering domain models. The domain models developed within the software product line development process need to cover all of the possible features and aspects of the target domain. In other words, the domain models often described using feature models should be elaborate representations of the feature space of that domain. In order to operationalize featurebased representations of a software application, appropriate implementation mechanisms need to be employed. In this paper, we propose a Semantic Web-oriented language, called Semantic Annotations for Feature Modeling Description Language (SAFMDL) that provides the means to semantically describe feature models. We will show that using SAFMDL along with Semantic Web Query techniques, we are able to bridge the gap between software product lines and SOA