Entity matching (EM) is a critical part of data integration and cleaning. In many applications, the users need to understand why two entities are considered a match, which reveals the need for interpretable and concise EM rules. We model EM rules in the form of General Boolean Formulas (GBFs) that allows arbitrary attribute matching combined by conjunctions ( ö ), disjunctions ( ô ), and negations ( ). GBFs can generate more concise rules than traditional EM rules represented in disjunctive normal forms (DNFs). We use program synthesis, a powerful tool to automatically generate rules (or programs) that provably satisfy a high-level specification, to automatically synthesize EM rules in GBF format, given only positive and negative matching examples.In this demo, attendees will experience the following features: (1) Interpretability -they can see and measure the conciseness of EM rules defined using GBFs; (2) Easy customization -they can provide custom experiment parameters for various datasets, and, easily modify a rich predefined (default) synthesis grammar, using a Web interface; and (3) High performance -they will be able to compare the generated concise rules, in terms of accuracy, with probabilistic models (e.g., machine learning methods), and hand-written EM rules provided by experts. Moreover, this system will serve as a general platform for evaluating di↵erent methods that discover EM rules, which will be released as an opensource tool on GitHub.

We describe RuDiK, an algorithm and a system for mining declarative rules over RDF knowledge graphs (KGs). RuDiK can discover rules expressing both positive relationships between KG elements, e.g., “if two persons share at least one parent, they are likely to be siblings,” and negative patterns identifying data contradictions, e.g., “if two persons are married, one cannot be the child of the other” or “the birth year for a person cannot be bigger than her graduation year.” While the first kind of rules identify new facts in the KG, the second kind enables the detection of incorrect triples and the generation of (training) negative examples for learning algorithms. High-quality rules are also critical for any reasoning task involving the KGs. Our approach increases the expressive power of the supported rule language w.r.t. the existing systems. RuDiK discovers rules containing (i) comparisons among literal values and (ii) selection conditions with constants. Richer rules increase the accuracy and the coverage over the facts in the KG for the task at hand. This is achieved with aggressive pruning of the search space and with disk-based algorithms, which enable the execution of the system in commodity machines. Also, RuDiK is robust to errors and missing data in the input graph. It discovers approximate rules with a measure of support that is aware of the quality issues. Our experimental evaluation with real-world KGs shows that RuDiK does better than existing solutions in terms of scalability and that it can identify effective rules for different target applications.

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