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Ai Magazine | 2004

Project Halo: Towards a Digital Aristotle

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

Representing roles and purpose

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

Indirect anaphora resolution as semantic path search

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

Learning by reading: a prototype system, performance baseline and lessons learned

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

A question-answering system for AP chemistry: assessing KR&R technologies

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

The knowledge required to interpret noun compounds

James Fan; Ken Barker; Bruce W. Porter


national conference on artificial intelligence | 2004

Interpreting loosely encoded questions

James Fan; Bruce W. Porter


Archive | 2009

IBM Research Report Towards the Open Advancement of Question Answering Systems

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

Utilizing domain knowledge in neuroevolution

James Fan; Raymond Lau; Risto Milkkulainen


international joint conference on artificial intelligence | 2016

Building joint spaces for relation extraction

Chang Wang; Liangliang Cao; James Fan

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Bruce W. Porter

University of Texas at Austin

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Ken Barker

University of Texas at Austin

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Dan Tecuci

University of Texas at Austin

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Shaw Yi Chaw

University of Texas at Austin

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