Knowledge Representation & Reasoning
Director: Bruce Porter
We develop methods for building and using large knowledge systems. Our research focuses on knowledge representation, automated reasoning, question answering and explanation generation. We have begun to explore ways of extracting knowledge from text.
People
Ken Barker Shaw Yi(Jason) Chaw James Jumin Fan Doo Soon Kim Bruce Porter Dan Tecuci Peter Zei-Chan Yeh
Publications
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A Unified Knowledge Based Approach for Sense Disambiguation and Semantic Role Labeling (2006) Matching Utterances to Rich Knowledge Structures to Acquire a Model of the Speaker's Goal (2005) Indirect Anaphora Resolution as Semantic Path Search (2005) A Question-Answering System for AP Chemistry: Assessing KR&R Technologies (2004) Towards a Quantitative, Platform-Independent Analysis of Knowledge Systems (2004) Project Halo: Towards a Digital Aristotle (2004) Interpreting Loosely Encoded Questions (2004) Mining Transformation Rules for Semantic Matching (2004) The Knowledge Required to Interpret Noun Compounds (2003) Using Transformations to Improve Semantic Matching. (2003) Representing Roles and Purpose (2001) Building Concept Representations from Reusable Components. (1997) Using Access Paths to Guide Inference with Conceptual Graphs (1997) Developing and Empirically Evaluating Robust Explanation Generators: The KNIGHT Experiments (1997) Automated Modeling of Complex Systems to Answer Prediction Questions (1997) Automated Modeling of Complex Systems for Answering Prediction Questions (1995) Robust Natural Language Generation from Large-Scale Knowledge Bases (1995) Automated Modeling for Answering Prediction Questions: Selecting the Time Scale and System Boundary (1994) Extracting Viewpoints from Knowledge Bases (1994) Access Methods for Large Knowledge Bases, (Unpublished journal-length summary of Liane's dissertation) (1994)
Projects
Project Halo Rapid Knowledge Formation The Botany Knowledge Base