The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Description Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper we discuss how the philosophy and features of OWL can be traced back to these older formalisms, with modifications driven by several other constraints on OWL. Several interesting problems...

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Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, CREAM, that allows for creation of metadata. While the annotation mode of CREAM allows to create metadata for existing web pages, the authoring mode lets authors create metadata --- almost for free --- while putting together the content of a page. As a particularity of our framework, CREAM allows to create relational metadata, i.e. metadata that instantiate interrelated definitions of classes in a domain ontology rather than a comparatively rigid template-like schema as Dublin Core. We discuss some of the requirements one has to meet when developing such an ontology-based framework, e.g. the integration of a metadata crawler, inference services, document management and a meta-ontology, and describe its implementation, viz. Ont-O-Mat a component-based, ontology-driven Web page authoring and annotation tool.

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies, and describe experiments that show the promise of the approach.

Finally, we describe a set of experiments on several real-world domains, and show that GLUE proposes highly accurate semantic mappings. 1 A Motivating Example: the Semantic Web The current World-Wide Web has well over 1.5 billion pages [2], but the vast majority of them are in human-readable format only (e.g., HTML). As Work done while the author was at the University of Washington, Seattle 2 AnHai Doan et al. a consequence software agents (softbots) cannot understand and process this information, and much of the potential of the Web has so far remained untapped. In response, researchers have created the vision of the Semantic Web [5], where data has structure and ontologies describe the semantics of the data. When data is marked up using ontologies, softbots can better understand the semantics and therefore more intelligently locate and integrate data for a wide variety of tasks. The following example illustrates the vision of the Semantic Web. Example 1. Suppose you want to fi

Usage patterns discovered through Web usage mining are effective in capturing item-to-item and user-to-user relationships and similarities at the level of user sessions Without the benefit of deeper domain knowledge, such patterns provide little insight into the underlying reasons for which such items or users are grouped together This can lead to a number of important shortcomings in personalization systems based on Web usage mining or collaborative filtering. For example, if a new item is recently added to the Web site, it is not likely that the pages associated with the item would be a part of any of the discovered patterns, and thus these pages cannot be recommended. Keyword-based content-filtering approaches have been used to enhance the effectiveness of collaborative filtering systems by focusing on content similarity among items or pages. These approaches, however, are incapable of capturing more complex relationships at a deeper semantic level based on different types of attributes associated with structured objects. This paper represents work-in-progress towards creating a general framework for using domain ontologies to automatically characterize usage profiles containing a set of structured Web objects. Our motivation is to use this framework in the context of Web personalization, going beyond page- or item-level constructs, and using the full semantic power of the underlying ontology.

The Resource Description Framework and the Resource Description Framework Schema Specification are supposed to be the foundations of the Semantic Web, in that all other Semantic Web languages are to be layered on top of them. It turns out that such a layering cannot be achieved in a straightforward way. This paper describes the problem with the straightforward layering and lays out several alternative layering possibilities. The benefits and drawbacks of each of these possibilities are presented and analyzed.

Peer-to-Peer systems are a new paradigm for information sharing and some systems have successfully been deployed. It has been argued that current Peer-to-Peer systems suffer from the lack of semantics. The SWAP project (Semantic Web and Peer-to-Peer) 1 aims at overcoming this problem by combining the Peer-to-Peer paradigm with Semantic Web technologies. In this paper, we propose a data model for encoding semantic information that combines features of ontologies (concept hierarchies, relational structures) with a flexible description and rating model that allows us to handle heterogeneous and even contradictory views on the domain of interest. We discuss the role of this model in the SWAP environment and describe the model as well as its application.

Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, CREAM, that allows for creation of metadata. While the annotation mode of CREAM allows to create metadata for existing web pages, the authoring mode lets authors create metadata -- almost for free -- while putting together the content of a page. As a

One of the research fields which has recently gained much scientific interest within the database community are Peer-to-Peer databases, where peers have the autonomy to decide whether to join or to leave an information sharing environment at any time. Such volatile data nodes may appear shortly, collect or deliver some data, and disappear again. It even can not be assured that a peer joins the network ever again.