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Semantic text similarity plays an important role in software engineeringtasks in which engineers are requested to clarify the semantics of descriptivelabels (e.g., business terms, table column names) that are often consists oftoo short or too generic words and appears in their IT systems. We formulatethis type of problem as a task of matching descriptive labels to glossarydescriptions. We then propose a framework to leverage an existing semantic textsimilarity measurement (STS) and augment it using semantic label enrichment andset-based collective contextualization where the former is a method to retrievesentences relevant to a given label and the latter is a method to computesimilarity between two contexts each of which is derived from a set of texts(e.g., column names in the same table). We performed an experiment on twodatasets derived from publicly available data sources. The result indicatedthat the proposed methods helped the underlying STS correctly match moredescriptive labels with the descriptions.

Matching of Descriptive Labels to Glossary Descriptions