This paper describes USI Answers -- a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation.

This paper describes USI Answers — a natural language question answering system for enterprise data. We report on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

The paper reports on the progress towards the goal of offering easy access to enterprise data to a large number of business users, most of whom are not familiar with the specific syntax or semantics of the underlying data sources. Additional complications come from the nature of the data, which comes both as structured and unstructured. The proposed solution allows users to express questions in natural language, makes apparent the system's interpretation of the query, and allows easy query adjustment and reformulation. The application is in use by more than 1500 users from Siemens Energy. We evaluate our approach on a data set consisting of fleet data.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

In the winter, 2004 issue of AI Magazine, we reported Vulcan Inc.'s first step toward creating a question-answering system called "Digital Aristotle." The goal of that first step was to assess the state of the art in applied Knowledge Representation and Reasoning (KRR) by asking AI experts to represent 70 pages from the advanced placement (AP) chemistry syllabus and to deliver knowledge-based systems capable of answering questions from that syllabus. This paper reports the next step toward realizing a Digital Aristotle: we present the design and evaluation results for a system called AURA, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. These results represent a substantial advance over what we reported in 2004, both in the breadth of covered subjects and in the provision of sophisticated technologies in knowledge representation and reasoning, natural language processing, and question answering to domain experts and novice users.

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 system's 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. 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.

Project Halo is a multistaged effort, sponsored by Vulcan Inc, aimed at creating Digital Aristotle, an application that will encompass much of the world's 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 system's 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 pilot's outcome is used to define challenges for the next phase of the project and beyond.