Project Halo Update — Progress Toward Digital Aristotle

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 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.

Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?

“…The ambitious Project Halo (Friedland et al, 2004) was proposed to create a "digital" Aristotle that can encompass most of the worlds's scientific knowledge and be capable of addressing complex problems with novel answers. In this project, (Angele et al, 2003) employed handcrafted rule to answer chemistry questions, (Gunning et al, 2010) took the physics and biology into account. Another important trial is solving the mathematical questions.…”

Proceedings of the 9th International Natural Language Generation conference

“…The recent KBGen (Banik et al, 2013) task focused on sentence generation from Knowledge Bases (KB). In particular, the task was organised around the AURA (Gunning et al, 2010) KB on the biological domain which models n-ary relations. The input data selection process targets the extraction of KB fragments which could be verbalised as a single sentence.…”

Surface Realisation from Knowledge-Bases

“…The KB subsets forming the KBGen input data were pre-selected from the AURA biology knowledge base (Gunning et al, 2010), a knowledge base about biology which was manually encoded by biology teachers and encodes knowledge about events, entities, properties and relations where relations include event-to-entity, event-to-event, event-to-property and entity-to-property relations. AURA uses a frame-based knowledge representation and reasoning system called Knowledge Machine (Clark and Porter, 1997) which was translated into first-order logic with equality and from there, into multiple different formats including SILK (Grosof, 2012) and OWL2 (Motik et al, 2009).…”