Zi Yang
ABSTRACT
As the scale of available on-line data grows ever larger, individuals and businesses must cope with increasing complexity in decision-making processes which utilize large volumes of unstructured, semi-structured and/or structured data to satisfy multiple, interrelated information needs which contribute to an overall decision. Traditional decision support systems (DSSs) have been developed to address this need, but such systems are typically expensive to build, and are purpose-built for a particular decision-making scenario, making them difficult to extend or adapt to new decision scenarios. In this paper, we propose a novel decision representation which allows decision makers to formulate and organize natural language questions or assertions into an analytic hierarchy, which can be evaluated as part of an ad hoc decision process or as a documented, repeatable analytic process. We then introduce a new decision support framework, QUADS, which takes advantage of automatic question answering (QA) technologies to automatically understand and process a decision representation, producing a final decision by gathering and weighting answers to individual questions using a Bayesian learning and inference process. An open source framework implementation is presented and applied to two real world applications: target validation, a fundamental decision-making task for the pharmaceutical industry, and product recommendation from review texts, an everyday decision-making situation faced by on-line consumers. In both applications, we implemented and compared a number of decision synthesis algorithms, and present experimental results which demonstrate the performance of the QUADS approach versus other baseline approaches.
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Index Terms
- QUADS: question answering for decision support
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