Learning novel relations from relational databases is an important problem with many
applications. Relational learning algorithms learn the definition of a new relation in terms of …

Abstract Extensible Markup Language (XML) documents are associated with time in two
ways:(1) XML documents evolve over time and (2) XML documents contain temporal …

Keyword queries on databases provide easy access to data, but often suffer from low
ranking quality, ie, low precision and/or recall, as shown in recent benchmarks. It would be …

Keyword query interfaces (KQIs) for databases provide easy access to data, but often suffer
from low ranking quality, ie low precision and/or recall, as shown in recent benchmarks. It …

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 …

Abstract xml query languages use directional path expressions to locate data in an xml data
collection. They are tightly coupled to the structure of a data collection, and can fail when …

A limitation of XQuery is that a programmer has to be familiar with the shape of the data to
query it effectively. And if that shape changes, or if the shape is other than what the …

Relational learning algorithms learn the definition of a new relation in terms of existing
relations in the database. The same database may be represented under different schemas …

Supervised and Unsupervised ML algorithms are widely used over graphs. They use the
structural properties of the data to deliver effective results. It is known that the same …

Learning novel concepts and relations from structured data sets, such as relational
databases, is an important problem with many applications in data management and …