Abstract. Open Mind Common Sense is a knowledge acquisition system designed to acquire commonsense knowledge from the general public over the web. We describe and evaluate our first fielded system, which enabled the construction of a 450,000 assertion commonsense knowledge base. We then discuss how our second-generation system addresses weaknesses discovered in the first. The new system acquires facts, descriptions, and stories by allowing participants to construct and fill in natural language templates. It employs word-sense disambiguation and methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other. 1

This paper describes the integration of an interactive visualization user interface with a knowledge management tool called Protg. Protg is a general-purpose tool that allows domain experts to build knowledge-based systems by creating and modifying reusable ontologies and problem-solving methods, and by instantiating ontologies to construct knowledge bases. The SHriMP (Simple Hierarchical Multi-Perspective) visualization technique was designed to enhance how people browse, explore and interact with complex information spaces. Although SHriMP is information independent, its primary use to date has been for visualizing and documenting software programs. The paper describes how we have applied software visualization techniques to more general knowledge domains. It is hoped that the integrated environment (called Jambalaya) will result in an easier to use and more powerful environment to support ontology evolution and knowledge acquisition. An example scenario of how Jambalaya can be applied to knowledge acquisition is provided. 1

Despite some successes, the lack of tools to allow subject matter experts to directly enter, query, and debug formal domain knowledge in a knowledge-base still remains a major obstacle to their deployment. Our goal is to create such tools, so that a trained knowledge engineer is no longer required to mediate the interaction. This paper presents our work on the knowledge entry part of this overall knowledge capture task, which is based on several claims: that users can construct representations by connecting pre-fabricated, representational components, rather than writing low-level axioms; that these components can be presented to users as graphs; and the user can then perform composition through graph manipulation operations. To operationalize this, we have developed a novel technique of graphical dialog using examples of the component concepts, followed by an automated process for generalizing the user's graphically-entered assertions into axioms. We present these claims, our approach, the system (called SHAKEN) that we are developing, and an evaluation of our progress based on having users encode knowledge using the system. Keywords Graphical knowledge entry, knowledge acquisition, components, composition, knowledge-based systems.

AI planning requires the definition of an action model using a language such as PDDL as input. However, building an action model from scratch is a difficult and time-consuming task even for experts. In this paper, we develop an algorithm called ARMS for automatically discovering action models from a set of successful plan examples. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of the example plans; that is, we assume that no intermediate states are given. This requirement is motivated by a variety of applications, including object tracking and plan monitoring where the knowledge about intermediate states is either minimal or unavailable to the observing agent. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, our ARMS algorithm gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a propositional satisfiability (SAT) problem and solves it using a SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness ofARMS empirically.

In order for intelligent systems to be applicable in a wide range of situations, end users must be able to modify their task descriptions. We introduce Tailor, a system that allows users to modify task information through instruction. In this approach, the user enters a short sentence to describe the desired change. The system maps the sentence into valid, plausible modifications and checks for unexpected side-effects they may have, working interactively with the user throughout the process. We conducted preliminary tests in which subjects used Tailor to make modifications to domains drawn from the eHow website, applying modifications posted by readers as ‘tips’. In this way the subjects acted as interpreters between Tailor and the human-generated descriptions of modifications. Almost all the subjects were able to make all modifications to the process descriptions with Tailor, indicating that the interpreter role is quite natural for users.

Electronic concept mapping tools empower experts to play an active role in the knowledge capture process, and provide a medium for building richly connected multimedia knowledge models -- sets of linked concept maps and resources about a particular domain. Knowledge models are intended to be used as a means for sharing knowledge among humans, not as carefully-crafted knowledge bases upon which machines will be performing inference. However, users must still confront the questions of what to include in a concept map and which concept maps to include in a knowledge model. This paper describes ongoing research on methods to provide content-based support to users as they extend concept maps by adding concepts and propositions, and as they select topics for new maps. The goal is to provide scaffolding for experts as they build their own concept maps, link their maps to others', and decide how to extend their knowledge models. The paper presents three approaches which start from a concept map under construction and mine related information -- both from prior concept maps, and from the web -- to propose information to aid the user's knowledge capture and knowledge construction. The paper begins with a brief summary of the concept mapping process and the CmapTools concept mapping software. It then presents three types of implemented suggesters, to suggest concepts, propositions, concept maps, and new topics to aid experts using the CmapTools, and describes preliminary experiments to assess their performance. It closes with a discussion of next steps for testing and refining these methods.

Making logic-based AI representations accessible to ordinary users has been an ongoing challenge for the successful deployment of knowledge bases. Past work to meet this objective has resulted in a variety of ontology editing tools and task-specific knowledge-acquisition methods. In this paper, we describe a Web-based ontology browsing and editing system with the following features: (a) well-organized English-like presentation of concept descriptions and (b) use of graphs to enter concept relationships, add/delete lists, and analogical correspondences. No existing tool supports these features. The system is Web-based and its user interface uses a mixture of HTML and Java. It has undergone significant

In our research on Commonsense reasoning, we have found that an especially important kind of knowledge is knowledge about human goals. Especially when applying Commonsense reasoning to interface agents, we need to recognize goals from user actions (plan recognition), and generate sequences of actions that implement goals (planning). We also often need to answer more general questions about the situations in which goals occur, such as when and where a particular goal might be likely, or how long it is likely to take to achieve. In past work on Commonsense knowledge acquisition, users have been directly asked for such information. Recently, however, another approach has emerged—to entice users into playing games where supplying the knowledge is the means to scoring well in the game, thus motivating the players. This approach has been pioneered by Luis von Ahn and his colleagues, who refer to it as Human Computation. Common Consensus is a fun, self-sustaining web-based game, that both collects and validates Commonsense knowledge about everyday goals. It is based on the structure of the TV game show Family Feud. A small user study showed that users find the game fun, knowledge quality is very good, and the rate of knowledge collection is rapid.

Interactive tools to help users author plans or processes are essential in a variety of domains. KANAL helps users author sound plans by simulating them, checking for a variety of errors and presenting the results in an accessible format that allows the user to see an overview of the plan steps or timelines of objects in the plan. From our experience in two domains, users tend to interleave plan authoring and plan checking while extending background knowledge of actions. This has led us to refine KANAL to provide a high-level overview of plans and integrate a tool for refining the background knowledge about actions used to check plans. We report on these lessons learned and new directions in KANAL.

Abstract: In this paper, we propose an approach to acquire cases in the context of project planning, without any extra effort from the end user. Under our definition, a case has a one to one correspondence with the standard elements of a project plan. We exploit this correspondence to capture cases automatically from project planning episodes. We provide an algorithm for extracting cases from project plans. We implemented this algorithm on top of a commercial project-planning tool and perform experiments evaluating our approach.