Fostering text mining with knowledge graphs: An approach to support business experts in defining domain-specific document sets

Abstract

Text mining techniques offer an efficient approach to extract information from text-based data, such as online news, for strategic planning tasks like the scenario technique. Data selection and data preprocessing is a crucial step in this process, during which decisive search terms help to sharpen the text corpus for downstream text mining activities. The integration of business knowledge is crucial for the creation of high-quality domain-specific document sets. Manually defining search terms is a time-consuming task for managers. Knowledge graphs accelerate this vital process: Based on a few initial search terms, related domain-specific subgraphs are selected which yield additional search terms that cannot be retrieved via thesauri or mere semantic similarity comparisons. Furthermore, possible biases introduced by subjective expert assessments are avoided and a more objective data selection is realized. The concept is demonstrated in a use case on electric mobility including an online demonstrator (www.digital-scenarios.com).

Keywords: Knowledge Graphs, Information Retrieval, Search Space Definition, Text Mining, Natural Language Processing

DOI: 10.54941/ahfe1003128