Problem-oriented updating of dictionary entries of bilingual dictionaries and medical terminology: Comparative analysis

Two approaches to the goal-oriented discovery of new knowledge from text data are compared. The first approach relates to the subject domain of lexicography. It focuses on extracting new meanings of linguistic units from texts to replenish the dictionary entries of bilingual dictionaries. The second approach relates to medical science and focuses on discovering new meanings of terms to update a disease's description in the form of its terminological portrait. The portrait includes definitions of terms with reflecting their dynamics over time, relationships between terms, contexts of their use, and links to sources of contexts. These approaches are compared in the following positions: the problem for the solution of which new knowledge is discovered, the purpose of its discovery, sources of concepts of new knowledge, the standard, comparison with which uses as the criterion of concepts' novelty, concept-source linkages, and concept dynamics. The purpose of the paper is to describe the outcomes of the comparative analysis of the approaches. It is proposed to position analysis outcomes as initial data for creating the conception of a human-artificial intelligence system for goal-oriented discovery of new knowledge from big data which is applicable in different subject domains.