Browsing by Author "Lenat, Doug"
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Item A Knowledge-Based Approach to Network Security: Applying Cyc in the Domain of Network Risk Assessment(Association for the Advancement of Articifical Intelligence (AAAI), 2005-07) Shepard, Blake; Matuszek, Cynthia; Fraser, C. Bruce; Wechtenhiser, William; Crabbe, David; Güngördü, Zelal; Jantos, John; Hughes, Todd; Lefkowitz, Larry; Witbrock, Michael; Lenat, Doug; Larson, ErikCycSecureTM is a network risk assessment and network monitoring application that relies on knowledge-based artificial intelligence technologies to improve on traditional network vulnerability assessment. CycSecure integrates public reports of software faults from online databases, data gathered automatically from computers on a network and hand-ontologized information about computers and computer networks. This information is stored in the Cyc® knowledge base (KB) and reasoned about by the Cyc inference engine and planner to provide detailed analyses of the security (and vulnerability) of networks.Item Searching for Common Sense: Populating Cyc from the Web(Association for the Advancement of Artificial Intelligence (AAAI), 2005-07) Matuszek, Cynthia; Witbrock, Michael; Kahlert, Robert C.; Cabral, John; Schneider, Dave; Shah, Purvesh; Lenat, DougThe Cyc project is predicated on the idea that effective machine learning depends on having a core of knowledge that provides a context for novel learned information – what is known informally as “common sense.” Over the last twenty years, a sufficient core of common sense knowledge has been entered into Cyc to allow it to begin effectively and flexibly supporting its most important task: increasing its own store of world knowledge. In this paper, we present initial work on a method of using a combination of Cyc and the World Wide Web, accessed via Google, to assist in entering knowledge into Cyc. The long-term goal is automating the process of building a consistent, formalized representation of the world in the Cyc knowledge base via machine learning. We present preliminary results of this work and describe how we expect the knowledge acquisition process to become more accurate, faster, and more automated in the future.