Declarative data quality has been an active research topic. The fundamental principle behind a declarative approach to data quality is the use of declarative statements to realize data quality primitives on top of any relational data source. A primary advantage of such an approach is the ease of use and integration with existing applications. Over the last few years several similarity predicates have been proposed for common quality primitives (approximate selections, joins, etc) and have been fully expressed using declarative SQL statements. In this paper we propose new similarity predicates along with their declarative realization, based on notions of probabilistic information retrieval. In particular we show how language models and hidden Markov models can be utilized as similarity predicates for data quality and present their full declarative instantiation. We also show how other scoring methods from information retrieval, can be utilized in a similar setting. We then present full...