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Software architects inhabit a complex, rapidly evolving technological landscape. An ever growing collection of competing architecturally significant technologies, ranging from distributed databases to middleware and cloud platforms, makes rigorously comparing alternatives and selecting appropriate solutions a daunting engineering task. To address this problem, we envisage an ecosystem of curated, automatically updated knowledge bases that enable straightforward and streamlined technical comparisons of related products. These knowledge bases would emulate engineering handbooks that are commonly found in other engineering disciplines. As a first step towards this vision, we have built a curated knowledge base for comparing distributed databases based on a semantically defined feature taxonomy. We report in this paper on the initial results of using supervised machine learning to assist with knowledge base curation. Our results show immense promise in recommending Web pages that are highly relevant to curators. We also describe the major obstacles, both practical and scientific, that our work has uncovered. These must be overcome by future research in order to make our vision of curated knowledge bases a reality.