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The flourish of data-intensive systems that are geared towards direct use by non-experts, such as Natural Language question answering systems and query-by-example frameworks calls for the incorporation of provenance management. Provenance is arguably even more important for such systems than for “classic” database application. This is due to the elevated level of uncertainty associated with the...

We show that a simple “reputation-style” test can always identify which of two experts is informed about the true distribution. The test presumes no prior knowledge of the true distribution, achieves any desired degree of precision in some fixed finite time, and does not use “counterfactual” predictions. Our analysis capitalizes on a result due to Fudenberg and Levine (1992) on the rate of conv...

BOOTSTRAP VOTING EXPERTS (BVE) is an extension to the VOTING EXPERTS algorithm for unsupervised chunking of sequences. BVE generates a series of segmentations, each of which incorporates knowledge gained from the previous segmentation. We show that this method of bootstrapping improves the performance of VOTING EXPERTS in a variety of unsupervised word segmentation scenarios, and generally impr...

The legal system should rely much more than it now does on statistical evidence. It should be cautious about the judgments of experts, who make predictable cognitive errors. Like everyone else, experts have a tendency to blunder about risk, a point that has been shown to hold for doctors, whose predictions significantly err in the direction of optimism. We present new evidence that individual d...

FOAF files are often used for publishing simple information about persons and about their community. Our proposal is to extend the user’s FOAF files with automatically generated user profiles, which are based on a histogram of the user’s interest and which are also semantically enriched using the GrowBag approach [1]. The main assumption is that such user profiles can provide good hints about u...