Distributed computer architectures labeled “peer-to-peer ” are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and

The Resource Definition Framework (RDF) is designed to support agent communication on the Web, but it is also suitable as a framework for modeling and storing personal information. Haystack is a personalized information repository that employs RDF in this manner. This flexible semistructured data model is appealing for several reasons. First, RDF supports ontologies created by the user and tailored to the user’s needs. At the same time, system ontologies can be specified and evolved to support a variety of high-level functionalities such as flexible organization schemes, semantic querying, and collaboration. In addition, we show that RDF can be used to engineer a component architecture that gives rise to a semantically rich and uniform user interface. We demonstrate that by aggregating various types of users ’ data together in a homogeneous representation, we create opportunities for agents to make more informed deductions in automating tasks for users. Finally, we discuss the implementation of an RDF information store and a programming language specifically suited for manipulating RDF. 1.

Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. The method first classifies each line of the header into one or more of 15 classes. An iterative convergence procedure is then used to improve the line classification by using the predicted class labels of its neighbor lines in the previous round. Further metadata extraction is done by seeking the best chunk boundaries of each line. We found that discovery and use of the structural patterns of the data and domain based word clustering can improve the metadata extraction performance. An appropriate feature normalization also greatly improves the classification performance. Our metadata extraction method was originally designed to improve the metadata extraction quality of the digital libraries Citeseer[17] and EbizSearch[24]. We believe it can be generalized to other digital libraries. 1 Introduction and related work Interoperability is crucial to the effective use of Digital

Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours.

Currently, adaptive educational hypermedia systems (AEHS) are described with nonuniform methods, depending on the specific view on the system, the application, or other parameters. There is no common language for expressing functionality of AEHS, hence these systems are difficult to compare and analyze.

Semantic markup of Web services will enable the automation of various kinds of tasks, including discovery, composition, and execution of Web services. We describe how an AI planning system (SHOP2) can be used with DAML-S Web service descriptions to automatically compose Web services.

Intuitively, data management and data integration tools should be well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: they typically require a comprehensive schema design before they can be used to store or share information, and they are difficult to extend because schema evolution is heavyweight and may break backward compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by nondatabase -oriented tools that have little support for semantics. The goal of the peer data management system (PDMS) is to address this need: we propose the use of a decentralized, easily extensible data management architecture in which any user can contribute new data, schema information, or even mappings between other peers' schemas. PDMSs represent a natural step beyond data integration systems, replacing their single logical schema with an interlinked collection of semantic mappings between peers' individual schemas. This paper describes several aspects of the Piazza PDMS, including the schema mediation formalism, query answering and optimization algorithms, and the relevance of PDMSs to the Semantic Web.

We describe DAMLJessKB, a tool for reasoning with the DARPA Agent Markup Language (DAML) and performing inference on the Semantic Web. DAMLJessKB maps DAML's semantics into facts and rules for use in a production system, such as the Java Expert System Shell (Jess). This article presents our underlying methodology and provides a detailed example of how DAMLJessKB can be used to make decisions about DAML-encoded engineering design knowledge. We believe that tools like DAMLJessKB are needed to help realize the full potential of the Semantic Web and DAML.

In the Semantic Web context information would be retrieved, processed, shared, reused and aligned in the maximum automatic way possible. Our experience with such applications in the Semantic Web has shown that these are rarely a matter of true or false but rather procedures that require degrees of relatedness, similarity, or ranking. Apart from the wealth of applications that are inherently imprecise, information itself is many times imprecise or vague. For example, the concepts of a “hot” place, an “expensive” item, a “fast” car, a “near” city, are examples of such concepts. Dealing with such type of information would yield more realistic, intelligent and effective applications. In the current paper we extend the OWL web ontology language, with fuzzy set theory, in order to be able to capture, represent and reason with such type of information.

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN. 1.