Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding provides applications, ranging from crawlers over meta-search engines to service integrators, with a key to this content. Yet, it has received little attention other than as component in specific applications such as crawlers or meta-search engines. No comprehensive approach to form understanding exists, let alone one that produces rich models for semantic services or integration with linked open data. In this paper, we present OPAL, the first comprehensive approach to form understanding and integration. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines features from the text, structure, and visual rendering of a web page. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches for form labeling by a significant margin. For form interpretation, OPAL uses a schema (or ontology) of forms in a given domain. Thanks to this domain schema, it is able to produce nearly perfect (more than 97 percent accuracy in the evaluation domains) form interpretations. Yet, the effort to produce a domain schema is very low, as we provide a Datalog-based template language that eases the specification of such schemata and a methodology for deriving a domain schema largely automatically from an existing domain ontology. We demonstrate the value of the form interpretations in OPAL through a light-weight form integration system that successfully translates and distributes master queries to hundreds of forms with no error, yet is implemented with only a handful translation rules.