|Opis:||In research we studied the notion of relevance in the field of information retrieval using the machine learning algorithms for binary text classification. We described the methodology, used in research, which included definition of problem and environment, methods, tools and research data, and hypothesis. We analyzed building blocks of dissertation, which included the notions of agent, information science, information retrieval, classification, communication, data mining and pattern recognition, relations, problem solving, heuristics and intuition, machine learning, world, artificial intelligence, artificial neural networks and knowledge.
For the key research notion, relevance, we defined concept’s origin and basic definitions. We analyzed different relevance models, including Mizzaro’s four dimensions of relevance, Dervin’s sense-making theory and Greisdorf’s conjunctive/disjunctive model. We studied the levels, types and conditions of relevance, premises and user’s context selection and relevance judgments.
We conducted two sets of experiments, where we (1) measured the performance of information retrieval and (2) analyzed the factors that influence the performance of information retrieval: the number of attributes, text records, types of vectorization, selection of machine learning algorithms, selection of attributes with predictive properties, the impact of selected lemmatization and others.
In dissertation we tested the hypothesis of replacing cognitive relevance judgments, created by human experts, with relevance judgments, created solely by computer algorithms. We were measuring the performance of information retrieval in order to satisfy agent’s perceived information need using open source and freely available data mining procedures and machine learning algorithms. We analyzed the root notion of relevance using systems-centered and user-centered approach. We conducted the number of experiments and showed that in comply with certain premises open source software can (fully and in part) substitute the human expert as a gold standard creator. Publishers can, if their integrated library systems technically and legally allow, replace parts of ILS with open source software modules for information retrieval, discussed in this dissertation, as s step towards replacing human experts with machine learning algorithms performing tasks of binary text classification.|