Knowledge Refinement via Rule Selection

In several different applications, including data transformation and entity resolution, rules are used to capture aspects of knowledge about the application at hand. Often, a large set of such rules is generated automatically or semi-automatically, and the challenge is to refine the encapsulated knowledge by selecting a subset of rules based on the expected operational behavior of the rules on available data. In this paper, we carry out a systematic complexity-theoretic investigation of the following rule selection problem: given a set of rules specified by Horn formulas, and a pair of an input database and an output database, find a subset of the rules that minimizes the total error, that is, the number of false positive and false negative errors arising from the selected rules. We first establish computational hardness results for the decision problems underlying this minimization problem, as well as upper and lower bounds for its approximability. We then investigate a bi-objective optimization version of the rule selection problem in which both the total error and the size of the selected rules are taken into account. We show that testing for membership in the Pareto front of this bi-objective optimization problem is DP-complete. Finally, we show that a similar DP-completeness result holds for a bi-level optimization version of the rule selection problem, where one minimizes first the total error and then the size

Coreference detection in XML metadata

Preserving data quality is an important issue in data collection management. One of the crucial issues hereby is the detection of duplicate objects (called coreferent objects) which describe the same entity, but in different ways. In this paper we present a method for detecting coreferent objects in metadata, in particular in XML schemas. Our approach consists in comparing the paths from a root element to a given element in the schema. Each path precisely defines the context and location of a specific element in the schema. Path matching is based on the comparison of the different steps of which paths are composed. The uncertainty about the matching of steps is expressed with possibilistic truth values and aggregated using the Sugeno integral. The discovered coreference of paths can help for determining the coreference of different XML schemas