Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of Information Retrieval on the Semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search.

... we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

The Grid's vision, of sharing diverse resources in a flexible, coordinated and secure manner through dynamic formation and disbanding of virtual communities, strongly depends on metadata. Currently, Grid metadata is generated and used in an ad hoc fashion, much of it buried in the Grid middleware’s code libraries and database schemas. This ad hoc expression and use of metadata causes chronic dependency on human intervention during the operation of Grid machinery, leading to systems which are brittle when faced with frequent syntactic changes in resource coordination and sharing protocols. The Semantic Grid is an extension of the Grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via Grid protocols. The layering of an explicit semantic infrastructure over the Grid Infrastructure potentially leads to increased interoperability and greater flexibility. In recent years, several projects have embraced the Semantic Grid vision. However, the Semantic Grid lacks a reference architecture or any kind of systematic framework for designing Semantic Grid components or applications. The Open Grid Service Architecture (OGSA) aims to define a core set of capabilities and behaviours for Grid systems. We propose a Reference Architecture that extends OGSA to support the explicit handling of semantics, and defines the associated knowledge services to support a spectrum of service capabilities. Guided by a set of design principles, Semantic-OGSA (S-OGSA) defines a model, the capabilities and the mechanisms for the Semantic Grid. We conclude by highlighting the commonalities and differences that the proposed architecture has with respect to other Grid frameworks

Abstract. The emerging field of semantic web technologies promises new stimulus for Software Engineering research. However, since the underlying concepts of the semantic web have a long tradition in the knowledge engineering field, it is sometimes hard for software engineers to overlook the variety of ontology-enabled approaches to Software Engineering. In this paper we therefore present some examples of ontology applications throughout the Software Engineering lifecycle. We discuss the advantages of ontologies in each case and provide a framework for classifying the usage of ontologies in Software Engineering.

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The use of semantic knowledge in its various forms has become an important aspect in managing data in database and information systems. In the form of integrity constraints, it has been used intensively in query optimization for some time. Similarly, data integration techniques have utilized semantic knowledge to handle heterogeneity for query processing on distributed information sources in a graceful manner. Recently, ontologies have become a "buzz word" for the semantic web and semantic data processing. In fact, they play a central role in facilitating the exchange of data between the several sources. In this

To achieve better interoperability among intelligent applications, and to relieve knowledge engineers from the burden of developing ontologies from scratch, it is critical to reuse ontologies. However, there are two main reasons why the reuse of ontologies is rare: (1) current ontology repositories allow only simple keyword-based search facilities, and (2) even when a user finds an ontology, the information about the ontology quality and (re)usability is not available.

Abstract. Existing mature ontology engineering approaches are based on some basic assumptions that are often violated in practice, in particular in the Semantic Web. Ontologies often need to be built in a decentralized way, ontologies must be given to a community in a way such that individuals have partial autonomy over them and ontologies have a life cycle that involves an iteration back and forth between construction/modification and use. While recently there have been some initial proposals to consider these issues, they lack the appropriate rigor of mature approaches. i.e. these recent proposals lack the appropriate depth of methodological description, which makes the methodology usable, and they lack a proof of concept by a long-lived case study. In this paper, we revisit mature and new ontology engineering methodologies. We provide an elaborate methodology that takes decentralization, partial autonomy and iteration into account and we demonstrate its proof-of-concept in a real-world cross-organizational case study. 1

The manual development of large knowledge systems is a difficult and error-prone task. In order to facilitate extensions to an existing knowledge base the structural design of the implemented knowledge needs to be improved from time to time. However, experts are often deterred even from important design improvements since some restructurings are too complex to handle. In this paper, we introduce a framework that allows for automated refactorings. Refactoring methods are well-defined and are executed in a semi-automated way. In this manner, the developer is supported during the process of restructuring of even large knowledge bases. Refactoring methods are usually applied to improve the design of the knowledge base; in this paper, we sketch some design anomalies that identify poor design of the knowledge base.

Abstract. In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.