literature-based discovery is assigning words/concepts to different semantic types. A concept might be classified under one type or multiple types. For example in the Unified Medical Language System (UMLS) the term migraine is classified under the type disease and syndrome, while the term magnesium is under two types: biologically active substance and element, ion, or isotope. The typing of concepts hones the discovery of connections between particular classes of concepts, i.e. diseases-genes or diseases-drugs. System evaluation The evaluation of literature-based discoveries is challenging, and includes both experimental and in silico methods. Methods try to quantify the amount of knowledge generated by systems, that should be provided in an amount and richness that is useful for scientists. Evaluation is difficult in LBD for several reasons: disagreement about the role of LBD systems in research and thus what makes a successful one; difficulty in determining how useful, interesting or actionable a discovery is; and difficulty in objectively defining a ‘discovery’, which hinders the creation of a standard evaluation set which quantifies when a discovery has been replicated or found. A popular method used in LBD is to replicate previous discoveries. These are usually LBD-based discoveries as they are relatively easy to quantify compared to other discoveries. There are only a handful of such discoveries and approaches e tuned to perform well on these discoveries might not generalise. In this type of evaluation, the literature before the discovery to be replicated is used to generate a ranked list of discovery candidates as target or linking terms. Success is measured by reporting the rank of the term(s) of interest; the higher the rank, the better the approach. Literature- or time-slicing involves splitting the existing literature at a point in time. The LBD system is then exposed to the literature before the split and is evaluated by how many of the discoveries in the later period it can discover. LBD systems have used term co-occurrences, relationships from external biomedical resources (e.g SemMedDB) and semantic relationships to generate the gold standards. A high precision approach is to get expert opinion to generate the gold standard, but this is time-consuming, expensive and tends to produce low recall rates. The advantage of time-slicing in comparison to the replication of previous discoveries is the evaluation on a large number of test instances. This raises the need for evaluation metrics which can quantify performance on large, ranked lists. LBD works