Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence independence of several algorithms on existing benchmark and real-world datasets under (de) compositions.
Real-world databases often have extremely complex schemas. With thousands of entity types and relationships, each with a hundred or so attributes, it is extremely difficult for new users to explore the data and formulate queries. Schema free query interfaces (SFQIs) address this problem by allowing users with no knowledge of the schema to submit queries. We postulate that SFQIs should deliver the same answers when given alternative but equivalent schemas for the same underlying information. In this paper, we introduce and formally define design independence, which captures this property for SFQIs. We establish a theoretical framework to measure the amount of design independence provided by an SFQI. We show that most current SFQIs provide a very limited degree of design independence. We also show that SFQIs based on the statistical properties of data can provide design independence when the changes in the schema do not introduce or remove redundancy in the data. We propose a novel XML SFQI called Duplication Aware Coherency Ranking (DA-CR) based on information-theoretic relationships among the data items in the database, and prove that DA-CR is design independent. Our extensive empirical study using three real-world data sets shows that the average case design independence of current SFQIs is considerably lower than that of DA-CR. We also show that the ranking quality of DA-CR is better than or equal to that of current SFQI methods.