Abstract. Abductive reasoning has been recognized as a valuable complement to deductive inference for tasks such as diagnosis and integration of incomplete information despite its inherent computational complexity. This paper presents a novel, tractable abduction procedure for the lightweight description logic EL. The proposed approach extends recent research on automata-based axiom pinpointing (which is in some sense dual to our problem) by assuming information from a predefined abducible part of the domain model if necessary, while the remainder of the domain is considered to be fixed. Our research is motivated by the need for efficient diagnostic reasoning for large-scale industrial systems where observations are partially incomplete and often sparse, but nevertheless the largest part of the domain such as physical structures is known. Technically, we introduce a novel pattern-based definition of abducibles and show how to construct a weighted automaton that commonly encodes the definite and abducible part of the domain model. We prove that its behavior provides a compact representation of all possible hypotheses explaining an observation, and is in fact computable in PTime. 1