To scale the knowledge base of a Biology textbook from 50 pages to 300 pages in the context of Project Halo, we have formulated a knowledge factory process [1]. The process involves a sentence-based encoding strategy under which a domain expert examines each sentence in the text-book [2] and represents it in a knowledge base (KB) as best as it can be represented. While encoding each sentence, at least two hard problems must be addressed: defining what it means to represent a sentence and defining the necessary ontology primitives that should be used in that representation. The purpose of this poster presentation is to explain these problems in more detail, and discuss the operational solutions that we have adopted for solving them.

Our long-term goal is to build a query answering system that can answer questions on a wide variety of topics and explain the answers. In such a situation, a designer faces the challenge of how to specify the KR&R requirements that are needed to answer questions. In this paper, we introduce a categorization of KR&R methods, and apply it to specifying the requirements for answering questions in six different domains: Physics, Chemistry, Biology, Environmental Science, Microeconomics, and U.S. Government & Politics. Drawing from the corpus of about 500 questions that we analyzed, we consider an example question in each domain and show the analytical process that we used to derive the requirements in terms of the KR&R categorization. We analyze the effectiveness of the current KR&R categorization, and identify directions for future work suggesting how this categorization can be further evolved by community participation.

Wikis are a type of collaborative repository system that enables users to create and edit shared content on the Web. The popularity and proliferation of Wikis have created a new set of challenges for trust research because the content in a Wiki can be contributed by a wide variety of users and can change rapidly. Nevertheless, most Wikis lack explicit trust management to help users decide how much they should trust an article or a fragment of an article. In this paper, we investigate the dynamic nature of revisions as we explore ways of utilizing revision history to develop an article fragment trust model. We use our model to compute trustworthiness of articles and article fragments. We also augment Wikis with a trust view layer with which users can visually identify text fragments of an article and view trust values computed by our model

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Constructing an appropriate model is a crucial step in performing the reasoning required to successfully answer a query about the behavior of a physical situation. In the compositional modeling approach of Falkenhainer and Forbus (1991), a system is provided with a library of composable pieces of knowledge about the physical world called model fragments. The model construction problem involves selecting appropriate model fragments to describe the situation. Model construction can be considered either for static analysis of a single state or for simulation of dynamic behavior over a sequence of states. The latter is significantly more difficult than the former since one must select model fragments without knowing exactly what will happen in the future states. The model construction problem in general can advantageously be formulated as a problem of reasoning about relevance of knowledge that is available to the system using a general framework for reasoning about relevance described by Levy (1993) and Levy and Sagiv (1993). In this paper, we present a model formulation procedure based on that framework for selecting model fragments efficiently for the case of simulation. For such an algorithm to be useful, the generated model must be adequate for answering the given query and, at the same time, as simple as possible. We define formally the concepts of adequacy and simplicity and show that the algorithm in fact generates an adequate and simplest model.

Reusable ontologies are becoming increasingly important for tasks such as information integration, knowledge-level interoperation and knowledge-base development. We have developed a set of tools and services to support the process of achieving consensus on commonly shared ontologies by geographically distributed groups. These tools make use of the World Wide Web to enable wide access and provide users with the ability to publish, browse, create and edit ontologies stored on an ontology server . Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information. The Ontolingua Server may be accessed through the URL http://ontolingua.stanford.edu

We describe an interlingua-based methodology for translating encoded knowledge and present a formalism for 'The authors would like to thank

During the late 1960s and early 1970s, an enthusiastic group of researchers at the SRI AI Laboratory focused their energies on a single experimental project in which a mobile robot was being developed that could navigate and push objects around in a multi-room environment (Nilsson [11]) . The project team consisted of many people over the years, including Steve Coles, Richard Duda, Richard Fikes, Tom Garvey, Cordell Green, Peter Hart, John Munson, Nils Nilsson, Bert Raphael, Charlie Rosen, and Earl Sacerdoti. The hardware consisted of a mobile cart, about the size of a small refrigerator, with touch-sensitive feelers, a television camera, and an optical range-finder. The cart was capable of rolling around an environment consisting of large boxes in rooms separated by walls and doorways; it could push the boxes from one place to another in its world. Its suite of programs consisted of those needed for visual scene analysis (it could recognize boxes, doorways, and room corners), for planning (it could plan sequences of actions to achieve goals), and for converting its plans into intermediatelevel and low-level actions in its world. When the robot moved, its television camera shook so much that it became affectionately known as Shakey the Robot. The robot, the environment, and the tasks performed by the system were quite simple by today's standards, but they were sufficiently paradigmatic to enable initial explorations of many core issues in the development of intelligent autonomous systems. In particular, they provided the context and motivation for development of the A* search algorithm (Hart et al. [7] ), the STRIPS (Fikes and Nilsson [4] ) and ABSTRIPS (Sacerdoti [ 14] ) planning systems, programs for generalizing and learning macro-operators