Podobnost

Podobnost is a term used across disciplines to denote likeness or resemblance between objects, states, or concepts. In everyday language it describes how alike two things are, while in technical contexts it is often formalized as a numerical score or a relational statement that depends on the chosen criteria and features.

In geometry, two figures are similar if there exists a one-to-one correspondence between their parts that preserves angles and proportional side lengths. Similarity transformations include scaling, rotation, translation, and reflection. The similarity ratio expresses how much one figure is enlarged or reduced relative to the other; shapes are similar when this ratio applies consistently, even if their sizes differ.

In statistics and data analysis, similarity measures quantify how closely observations or distributions resemble each other. They are used in clustering, nearest-neighbor methods, information retrieval, and recommendation systems. Common measures include Euclidean-based similarity (often represented as the inverse of distance), cosine similarity for vectors, Jaccard similarity for sets, and Pearson correlation for linear relationships. The choice of measure depends on data type and the problem at hand, and different measures can yield different interpretations of similarity.

In linguistics, information retrieval, and artificial intelligence, similarity underpins semantic comparison of words, sentences, and documents. Semantic similarity may rely on lexical resources, distributional semantics, or learned representations from large corpora, where proximity in a vector space reflects perceived likeness. Across contexts, the concept of podobnost is thus contextual, method-dependent, and central to tasks involving categorization, matching, and inference.