This paper describes an Object-Oriented extension to RuleML as a modular combination of three sublanguages. (1) User-level roles provide frame-like slot representations as unordered argument collections in atoms and complex terms. (2) URI-grounded clauses allow for `webizing' using URIs as object identifiers for facts and rules. (3) Ordersorted terms permit typed variables via Web links into taxonomies such as RDF Schema class hierarchies, thus reusing the Semantic Web's lightweight ontologies. Besides introducing the first sublanguage with the Positional-Roled (ASCII) syntax, all three sublanguages are introduced with the OO RuleML (XML) syntax. Their semantics are sketched and their implementation paths are discussed.

We have proposed and implemented AgentMatcher, an architecture for match-making in e-Business applications. It uses arc-labeled and arc-weighted trees to match buyers and sellers via our novel similarity algorithm. This paper adapts the architecture for matchmaking between learners and learning objects (LOs). It uses the Canadian Learning Object Metadata (CanLOM) repository of the eduSource e-Learning project. Through AgentMatcher’s new indexing component, known as Learning Object Metadata Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM. LOMGen semi-automatically generates the LO metadata by combining a word frequency count and dictionary lookup. A subset of these metadata terms can be selected from a query interface, which permits adjustment of weights that express user preferences. Webbased prefiltering is then performed over the CanLOM metadata kept in a relational database. Using an XSLT (Extensible Stylesheet Language Transformations) translator, the prefiltered result is transformed into an XML representation, called Weighted Object-Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO RuleML representation obtained from the query interface by AgentMatcher’s core Similarity Engine. The final result is presented as a ranked LO list with a user-specified threshold.

Ideally, e-Business application interfaces would be built from highly reusable specifications of business document standards. Since many of these specifications are poorly understood, users often create new ones or customize existing ones every time a new integration problem arises. Consequently, even though there is a potential for reuse, the lack of a component discovery tool means that the cost of reuse is still prohibitively high. In this paper, we explore the potential of using similarity metrics to discover standard XML Schema documents. Our goal is to enhance reuse of XML Schema document/component standards in new integration contexts through the discovery process. We are motivated by the increasing access to the application interface specifications expressed in the form of XML

Our weighted-tree similarity algorithm matches buyers and sellers in e-Business environments. We use arc-labeled, arc-weighted trees to represent the products (or services) sought/offered by buyers/sellers. Partial product descriptions can be represented via subtrees missing in either or both of the trees. In order to take into account the effect of a missing subtree on the similarity between two trees, our algorithm uses a (complexity or) simplicity measure. Besides tree size (breadth and depth), arc weights are taken into account by our tree simplicity algorithm. This paper formalizes our buyer/seller trees and analyzes the properties of the implemented tree simplicity measure. We discuss how this measure captures business intuitions, give computational results on the simplicity of balanced k-ary trees, and show that they conform to the theoretical analysis. Key Words Arc-labeled and arc-weighted tree, tree similarity, tree simplicity, balanced k-ary trees, e-Business, buyer and seller trees. 1.

The number of complex transactions in a power grid to determine economical ways of satisfying power demands given a set of power plants has increased significantly. Knowledge-based solution and intelligent computational grids can help in these transactions. This paper presents the application of the AgentMatcher architecture for similarity, pairing and negotiation in order to optimize the transactions in these virtual markets. We use tree similarity and agent pairing to obtain for each buyer (power distributor, a ranking table of candidate sellers (power plants). A similarity based on weight variance and a negotiation algorithm based on priority groups of sellers are also proposed.

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Many integration projects today rely on shared semantic models based on standards represented using Extensible Mark up Language (XML) technologies. Shared semantic models typically evolve and require maintenance. In addition, to promote interoperability and reduce integration costs, the shared semantics should be reused as much as possible. Semantic components must be consistent and valid in terms of agreed upon standards and guidelines. In this paper, we describe an activity model for creation, use, and maintenance of a shared semantic model that is coherent and supports efficient enterprise integration. We then use this activity model to frame our research and the development of tools to support those activities. We provide overviews of these tools primarily in the context of the W3C XML Schema. At the present, we focus our work on the W3C XML Schema as the representation of choice, due to its extensive adoption by industry.

This document describes KMIR, a framework which supports organizations in the successful implementation of Knowledge Management (KM). It follows the holistic approach of a KM introduction by considering technological, organizational and human aspects, as well as organizational culture. The KMIR framework provides recommendations based on Case-Based Reasoning (CBR) and Semantic Web technology. The best practice cases for a successful KM implementation are structured by the use of an ontology.

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In this paper we give an overview of the RACOFI (RuleApplying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms ST I Pearson, ST IN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at [http://racofi.elg.ca].