Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by defining a set of possible semantic types for concepts. Word sense induction is performed by inferring a set of semantic types for each noun phrase. Synonym detection exploits redundant information to train several domain-specific synonym classifiers in a semi-supervised fashion. When ConceptResolver is run on NELL's knowledge base, 87% of the word senses it creates correspond to real-world entities. Examining the mapping from noun phrases to concepts, we find that 85% of noun phrases that refer to the same concept are synonyms.