{'fraction_non_alphanumeric': 0.0171990171990172, 'fraction_numerical': 0.000819000819000819, 'mean_word_length': 5.019704433497537, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Mining Patterns in the Presence of Domain Knowledge

One of the main difficulties of pattern mining is to deal with items of different nature in the same itemset, which can occur in any domain except basket analysis. Indeed, if we consider the analysis of any transactional database composed by several entities and relationships, it is easy to understand that the equality function may be different for each element, which difficult the identification of frequent patterns. This situation is just one example of the need for using domain knowledge to manage the discovery process, but several other, no less important can be enumerated, such the need to consider patterns at higher levels of abstraction or the ability to deal with structured data. In this paper, we show how the Onto4AR framework can be explored to overcome these situations in a natural way, illustrating its use in the analysis of two distinct case studies. In the first one, exploring a cinematographic dataset, we capture patterns that characterize kinds of movies in accordance to the actors present in their casts and their roles. In the second one, identifying molecular fragments, we find structured patterns, including chains, rings and stars.