Process mining has been successfully used in automatic knowledge discovery and in providing guidance or support. The known process mining approaches rely on processes being executed with the help of information systems thus enabling the automatic capture of process traces as event logs. However, there are many other fields such as Humanities, Social Sciences and Medicine where workers follow processes and log their execution manually in textual forms instead. The problem we tackle in this paper is mining process instance models from unstructured, text-based process traces. Using natural language processing with a focus on the verb semantics, we created a novel unsupervised technique TextProcessMiner that discovers process instance models in two steps: 1.ActivityMiner mines the process activities; 2.ActivityRelationshipMiner mines the sequence, parallelism and mutual exclusion relationships between activities. We employed technical action research through which we validated and preliminarily evaluated our proposed technique in an Archaeology case. The results are very satisfactory with 88% correctly discovered activities in the log and a process instance model that adequately reflected the original process. Moreover, the technique we created emerged as domain independent.

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Obtaining models of cultural heritage that guarantee information interoperability and, at the same time, maintain a high degree of fitness to the problem at hand is not a trivial quest. This paper proposes a two-step approach to attain this, where particular models for each problem at hand are derived from a common, standardised Cultural Heritage Abstract Reference Model (CHARM) by using specific rules that guarantee abstract interoperability while allowing for as much specificity as necessary. This is illustrated through a case study involving three different communities, each with a different conceptual model of cultural heritage, which still generate a seamless object model.

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