Documents play an essential role in everyday life by serving as a potential source for underlying knowledge discovery. These documents are a rich repository of information on relationships. Information is typically stored and distributed as text due to its high flexibility and universality. Such data may potentially contain a great wealth of knowledge. However, analyzing vast amounts of textual data requires a tremendous amount of work in reading and organizing the content. Thus, the increase in accessible textual data has caused an information flood despite the hope of becoming knowledgeable about various topics. Text mining is a technology that makes it possible to discover patterns and trends semiautomatically from massive collections of unstructured text. It is based on technologies such as natural language processing, information retrieval, information extraction, and data mining. Our reserach aimed to provide a solution for search recommendation support by knowledge miner-based text semantics analysis. In this report, we detail the evaluation of the proposed method presented in our previous reports. Specifically, we discuss the used metrics, measurement methodology, achieved results and their implications. Based on the findings observed, we conclude that Knowledge-Miner 3.0 can achieve what its predecessors failed to accomplish, especially regarding the similarities function. The proposed solution was proved to be practical for everyday use.
Pekár Adrián
2023-01-15
Támogató: DXC Technology Hungary