James Fan
University of Texas at Austin
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Ai Magazine | 2004
Noah S. Friedland; Paul G. Allen; Gavin Matthews; Michael J. Witbrock; David Baxter; Jon Curtis; Blake Shepard; Pierluigi Miraglia; Jürgen Angele; Steffen Staab; Eddie Moench; Henrik Oppermann; Dirk Wenke; David J. Israel; Vinay K. Chaudhri; Bruce W. Porter; Ken Barker; James Fan; Shaw Yi Chaw; Peter Z. Yeh; Dan Tecuci; Peter Clark
Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the worlds scientific knowledge and be capable of applying sophisticated problem solving to answer novel questions. Vulcan envisions two primary roles for Digital Aristotle: as a tutor to instruct students in the sciences and as an interdisciplinary research assistant to help scientists in their work. As a first step towards this goal, we have just completed a six-month pilot phase designed to assess the state of the art in applied knowledge representation and reasoning (KR&/R). Vulcan selected three teams, each of which was to formally represent 70 pages from the advanced placement (AP) chemistry syllabus and deliver knowledge-based systems capable of answering questions on that syllabus. The evaluation quantified each systems coverage of the syllabus in terms of its ability to answer novel, previously unseen questions and to provide human- readable answer justifications. These justifications will play a critical role in building user trust in the question-answering capabilities of Digital Aristotle. Prior to the final evaluation, a failure taxonomy was collaboratively developed in an attempt to standardize failure analysis and to facilitate cross-platform comparisons. Despite differences in approach, all three systems did very well on the challenge, achieving performance comparable to the human median. The analysis also provided key insights into how the approaches might be scaled, while at the same time suggesting how the cost of producing such systems might be reduced. This outcome leaves us highly optimistic that the technical challenges facing this effort in the years to come can be identified and overcome. This article presents the motivation and longterm goals of Project Halo, describes in detail the six-month first phase of the project -- the Halo Pilot -- its KR&R challenge, empirical evaluation, results, and failure analysis. The pilots outcome is used to define challenges for the next phase of the project and beyond.
international conference on knowledge capture | 2001
James Fan; Ken Barker; Bruce W. Porter; Peter Clark
Ontology designers often distinguish Entities (things that are) from Events (things that happen). It is not obvious how this division admits Roles (things that are, but only in the context of things that happen). For example, Person might be considered an Entity, while Employee is a Role. A Person remains a Person independent of the Events in which he participates. Someone is an Employee only by virtue of participating in an Employment Event. The problem of how to represent Roles is not new, but there is little consensus on a solution. In this paper, we present an ontology that finds a place for Roles as well as a representation that allows Roles to be related to Entities and Events to express the teleological notion of purpose.
international conference on knowledge capture | 2005
James Fan; Ken Barker; Bruce W. Porter
Anaphora occur commonly in natural language text, and resolving them is essential for capturing the knowledge encoded in text. Indirect anaphora are especially challenging to resolve because the referring expression and the antecedent are related by unstated background knowledge. Such anaphora need to be resolved properly in order to automatically capture the knowledge expressed in natural language. Resolving indirect anaphora has been treated as a unique problem that requires special-purpose methods, and these methods have had limited success in precision and recall. In this study, we used a generic tool for finding semantic paths between two concepts to resolve these anaphora, and it achieved approximately twice the recall of the best previous system without loss of precision. A series of ablation study showed that the biggest increase in recall came from an abductive stopping criterion of the search.
national conference on artificial intelligence | 2007
Ken Barker; Bhalchandra Agashe; Shaw Yi Chaw; James Fan; Noah S. Friedland; Michael Robert Glass; Jerry R. Hobbs; Eduard H. Hovy; David J. Israel; Doo Soon Kim; Rutu Mulkar-Mehta; Sourabh Patwardhan; Bruce W. Porter; Dan Tecuci; Peter Z. Yeh
principles of knowledge representation and reasoning | 2004
Ken Barker; Vinay K. Chaudhri; Shaw Yi Chaw; Peter Clark; James Fan; David J. Israel; Sunil Mishra; Bruce W. Porter; Pedro Romero; Dan Tecuci; Peter Z. Yeh
international joint conference on artificial intelligence | 2003
James Fan; Ken Barker; Bruce W. Porter
national conference on artificial intelligence | 2004
James Fan; Bruce W. Porter
Archive | 2009
David A. Ferrucci; Eric Nyberg; James Allan; Ken Barker; Eric W. Brown; Jennifer Chu-Carroll; Arthur C. Ciccolo; Pablo Ariel Duboue; James Fan; David Gondek; Eduard H. Hovy; Boris Katz; Adam Lally; Michael C. McCord; Paul Morarescu; Bill Murdock; Bruce W. Porter; John M. Prager; Tomek Strzalkowski; Chris Welty; Wlodek Zadrozny
international conference on machine learning | 2003
James Fan; Raymond Lau; Risto Milkkulainen
international joint conference on artificial intelligence | 2016
Chang Wang; Liangliang Cao; James Fan