IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.
Abstract. Finding the justifications of an entailment (that is, all the minimal set of axioms sufficient to produce an entailment) has emerged as a key inference service for the Web Ontology Language (OWL). Justifications are essential for debugging unsatisfiable classes and contradictions. The availability of justifications as explanations of entailments improves the understandability of large and complex ontologies. In this paper, we present several algorithms for computing all the justifications of an entailment in an OWL-DL Ontology and show, by an empirical evaluation, that even a reasoner independent approach works well on real ontologies.
Abstract. In this paper, we investigate the problem of repairing unsatisfiable concepts in an OWL ontology in detail, keeping in mind the user perspective as much as possible. We focus on various aspects of the repair process -improving the explanation support to help the user understand the cause of error better, exploring various strategies to rank erroneous axioms (with motivating use cases for each strategy), automatically generating repair plans that can be customized easily, and suggesting appropriate axiom edits where possible to the user. Based on the techniques described, we present a preliminary version of an interactive ontology repair tool and demonstrate its applicability in practice.
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