The term Semantic Web was coined by Tim Berners-Lee to describe his proposal for "a web of meaning," as opposed to the "web of links" that currently exists on the Internet. Toachieve this vision, we need to develop languages and tools that enable machine understandable web pages. The SHOE project, begun in 1995, was one of the first efforts to explore these issues. In this paper, we describe our experiences developing and using the SHOE language. Webeginby describing the unique features of the World Wide Web and how they must influence potential Semantic Web languages. Then we present SHOE, a language whichallows web pages to be annotated with semantics, describe its syntax and semantics, and discuss our approaches to handling the problems of interoperability in distributed environments and ontology evolution. Finally,weprovide an overview of a suite of tools for the Semantic Web, and discuss the application of the language and tools to two different domains.

Inductive machine learning offers techniques for discovering new knowledge from business, medical, and scientific databases. Most techniques assume that all the relevant information for discovery has been gathered and assembled into a single table or database. With multiple databases it is possible to combine features from several perspectives and thus move beyond the confines of an ontology that was fixed by the designers of a single database. We introduce WoRLD ("Worldwide Relational Learning Daemon"), a system that uses spreading activation to enable inductive learning from multiple tables in multiple databases spread across the network. We describe the paradigm and the system, provide demonstrations on synthetic data sets, and then replicate two real-world successes of automated discovery. 1 INTRODUCTION Inductive machine learning offers methods for discovering new knowledge from business, medical, and scientific databases. Although the need to learn across multiple tables has bee...

2.1.1 The Snark language................... 6

Contexts have historically been either ignored completely or else treated as black boxes, as indivisible atoms. About a decade ago, as part of our work on building the large Cyc ® knowledge base of human common sense and common knowledge, our group began to study and harness the internal structure of that “atom”. Each context was said to have assumptions and content; there was a theory of importing assertions across contexts; contexts were fully reified first-class terms in the CycL representation language; they were partially ordered by specialization to control visibility and access to content; and so on. That 1989-91 work turned out to be inadequate: it was too expensive to do nontrivial lifting (importing); to explicate the assumptions of each context; and to place each assertion/query into the proper context. Over the last few years, as the number of Cyc contexts grew into the thousands, we gained a better understanding of the problem – and a possible solution has emerged. There is a finer internal structure to a context than just those two parts, assumptions and content. There are a dozen mostly-independent dimensions along which contexts vary; conversely, each region of that 12-dimensional space implicitly defines a context. In effect that space is the space of assumptions, and each assertion can be thought to hold true in some region of that space. A

Introduction What is a Supply Chain? Utilising Process Tools Interlingua, Modelling, Simulation Scenario Description Scenario Element Descriptions Process Overview Supply Chain Objects People, Companies and Departments Role-Defined Relationships Dates and Times Business Objects General Objects Detailed Scenario Processes Replenish Inventory (Retailer) Scenario Text Analysis Process Handbook PIF Representation IDEF3 Representation Take Delivery (Retailer) Scenario Text Analysis Process Handbook PIF Representation IDEF3 Representation PIF Semantics Summary References About this document ... Abstract The goal of the PIF Project is to develop an interchange format to help automatically exchange process descriptions among a wide variety of business process modelling and support systems such as workflow software, flow charting tools, process simulation systems, and process repositories. As an example of such an exchange, a demonstration scenario has been created which describes the use o...

Cyc is the largest existing common-sense knowledge base. Its ontology makes heavy use of higher-order logic constructs such as a context system, first class predicates, etc. Many of these higher-order constructs are believed to be key to Cyc's ability to represent common-sense knowledge and reason with it efficiently. In this paper, we present a translation of a large part (around 90%) of the Cyc ontology into FirstOrder Logic. We discuss our methodology, and the tradeoffs between expressivity and efficiency in representation and reasoning. We also present the results of experiments using VAMPIRE, SPASS, and the E Theorem Prover on the firstorderized Cyc KB. Our results indicate that, while the use of higher-order logic is not essential to the representability of common-sense knowledge, it greatly improves the efficiency of reasoning.

Guarino for their prompt responses to my questions. Third, to all my colleagues and friends in the Department of Spatial Information Science I would like to thank you for sharing the good and bad moments of my study life. I feel fortunate for having being part of a friendly environment that made my Ph.D. program an enjoyable and unforgettable experience. iii Fourth, I thank the support and funding from the University of Concepcin, Chile, and the initial funding from the Fulbright foundation. Further funding from the National Center of Geographic Information and Analysis, the National Imagery and Mapping Agency, and Lockheed Martin are gratefully acknowledged. Most important, I thank the continuous support, love, and patience of Christian and Alicia. This long journey would not have been possible without them. iv Table of Contents Acknowledgments .......................................................................................................ii List of Figu

This paper is structured as follows. I first present the relevant social science research and draw implications for the design of interactive systems. I then characterize the collaborative manipulation approach in terms of three design principles and show how they are informed by this research. Finally, I show how these principles are embodied in the HITS Knowledge Editor, illustrate the effectiveness of the system using data taken from user studies, and argue that the effectiveness is due to the design approach.

We have begun to see the emergence of large-scale knowledge bases that house tens of thousands of facts encoded in expressive representational languages. The richness of these representations o er the promise of significantly improving the quality of natural language generation, but their representational complexity, scale, and task-independence pose great challenges to generators. We have designed, implemented, and empirically evaluated Fare, a functional realization system that exploits message specifications drawn from large-scale knowledge bases to create functional descriptions, which are expressions that encode both functional information (case assignment) and structural information (phrasal constituent embeddings). Given a message specification, Fare exploits lexical and grammatical annotations on knowledge base objects to construct functional descriptions, which are then converted to text by a surface generator. Two empirical studies -- one with an explanation generator and one with a qualitative model builder -- suggest that Fare is robust, efficient, expressive, and appropriate for a broad range of applications.

We present an ontology (named WaWO - Waste Water Ontology) applied to the domain of wastewater treatment processes. WaWO is built following the ideas of Uschold and Gruninger [14] [13] and is a hierarchically structured set of terms and a set of axioms for describing the real-world domain of wastewater treatment. WaWO is the manifestation of a shared understanding of the wastewater domain that is agreed among a number of agents: mainly, experts in environmental and chemical engineering. The WaWO ontology will be integrated into an existent environmental decision-support system (named DAI-DEPUR ). In the paper we describe the process of building the ontology, the intended uses of the ontology and what we want to obtain by its application.