[Show abstract][Hide abstract] ABSTRACT: EcoCyc (http://EcoCyc.org) is a comprehensive model organism database for Escherichia coli K-12 MG1655. From the scientific literature, EcoCyc captures the functions of individual E. coli gene products; their regulation at the transcriptional, post-transcriptional and protein level; and their organization into
operons, complexes and pathways. EcoCyc users can search and browse the information in multiple ways. Recent improvements
to the EcoCyc Web interface include combined gene/protein pages and a Regulation Summary Diagram displaying a graphical overview
of all known regulatory inputs to gene expression and protein activity. The graphical representation of signal transduction
pathways has been updated, and the cellular and regulatory overviews were enhanced with new functionality. A specialized undergraduate
teaching resource using EcoCyc is being developed.
[Show abstract][Hide abstract] ABSTRACT: Textbooks are increasingly moving into the digital realm, which presents an opportunity for them to evolve from providing
the reader with a static, linear experience, into an interactive application that can adapt to a student as well as to specific
learning goals. As a step in this direction, we present Inquire: Biology, an electronic textbook that provides question-answering capability.
[Show abstract][Hide abstract] ABSTRACT: In the fall 2010 issue of the AI Magazine, we reported the design, implementation and evaluation of a knowledge acquisition system called AURA. AURA enables domain experts in Physics, Chemistry and Biology to author their knowledge, and a different set of experts to pose questions against that knowledge. The evaluation results previously reported were from 50 pages each from science textbooks in Physics, Chemistry and Biology. The results were most promising for Biology. Based on those results we undertook a content building effort to capture knowledge from approximately 315 pages (or 20 chapters) of the same Biology textbook  and incorporated the resulting content in the electronic version of that book. In this demo/poster session, we will demonstrate the biology knowledge base (KB) created using AURA, the electronic textbook application Inquire, and discuss the knowledge engineering process we used to construct the KB.
Proceedings of the 6th International Conference on Knowledge Capture (K-CAP 2011), June 26-29, 2011, Banff, Alberta, Canada; 01/2011
[Show abstract][Hide abstract] ABSTRACT: 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 article 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.
[Show abstract][Hide abstract] ABSTRACT: The long-term goal of Project Halo is to build an application called Digital Aristotle that can answer questions on a variety of science topics and provide user and domain appropriate explanations. As a near-term goal, we are focusing on enabling Subject Matter Experts (SMEs) to construct declarative knowledge bases (KBs) from 50 pages of a science textbook in the domains of Physics (Giancoli 2004), Chemistry (Brown et al. 2003) and Biology (Campbell et al. 2001) in a way that the system can answer questions similar to those on an Advanced Placement (AP) exam. We will demonstrate the current state of a system called AURA that we have been developing as a contributing technology toward the goal of Digital Aristotle. The innovative features of AURA are that it supports knowledge formulation for a mixture of textual and nontextual knowledge, and question formulation using an interactive dialog based on simplified English. The nontextual knowledge may contain tables, chemical reactions, and mathematical equations. In an extensive usability testing of AURA, we have established the basic viability of the approach.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, July 22-26, 2007, Vancouver, British Columbia, Canada; 01/2007
[Show abstract][Hide abstract] ABSTRACT: The long-term goal of Project Halo is to build an applica- tion called Digital Aristotle that can answer questions on a wide variety of science topics and provide user- and domain-appropriate explanations. As a near-term goal, we are focusing on enabling subject matter experts (SMEs) to construct declarative knowledge bases (KBs) from 50 pages of a science textbook in the domains of Physics, Chemistry, and Biology in a way that the system can an- swer questions similar to those in an Advanced Placement (AP) exam in the respective discipline. The textbook knowledge is a mixture of textual information, mathe- matical equations, tables, diagrams, and domain-specific representations such as chemical reactions. In this paper, we explore the following question: Can we build a knowl- edge capture system to enable SMEs to construct KBs from the knowledge found in science textbooks and use the resulting KB for deductive question answering? We answer this question in the context of a system called AURA that supports knowledge capture from science textbooks.
Proceedings of the 4th International Conference on Knowledge Capture (K-CAP 2007), October 28-31, 2007, Whistler, BC, Canada; 01/2007
[Show abstract][Hide abstract] ABSTRACT: We present our experience in exporting a knowledge base (KB). Specifically, we discuss the translations of the representation of scalar, cardinal, and categorical values, tasks, built-in data types, and collections. These examples could be illustrative for others trying to use OWL in their work. We also raise design questions that could provide fodder for discussion at the workshop.
Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions, Athens, Georgia, USA, November 10-11, 2006; 01/2006
[Show abstract][Hide abstract] ABSTRACT: Capturing and exploiting knowledge is at the heart of several important problems such as decision making, the semantic web, and intelligent agents. The captured knowledge must be accessible to subject matter experts so that the knowledge can be easily extended, queried, and debugged. In our previous work to meet this objective, we created a knowledge-authoring system based on graphical assembly from components that allowed acquisition of an interestingly broad class of axioms. In this paper, we explore the question: can we expand the axiom classes acquired by building on our existing graphical methods and still retain simplicity so that people with minimal training in knowledge representation can use it? Specifically, we present techniques used to capture ternary relations, classification rules, constraints, and if-then rules.
Keywords: authoring tools, knowledge-acquisition tools