{'fraction_non_alphanumeric': 0.01253616200578592, 'fraction_numerical': 0.0, 'mean_word_length': 5.740259740259741, '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}}
Document Maps: Semantic Structuring of Technical Document Collections

Corporate knowledge management in science and engineering-intensive organizations involves tasks such as standard generation and evaluation, comparison of related cases and experience reuse in their treatment, and the ability to rapidly retrieve all relevant documents around a certain topic or project even if this project has extended over years or decades. No single information retrieval technique is likely to adequately deal with such tasks independent of the specific situation. In this paper, we therefore present a modular approach that allows a variety of techniques from clustering, exploitation of semantic structure knowledge, and visualization to be used in the handling of technical document collections for knowledge management purposes. Usable implementations exist for large parts of the approach. Two real-world usage experiences with projects in the chemical engineering and medical domains provide initial evidence for the value of the approach.