# Matching of Descriptive Labels to Glossary Descriptions

arXiv: https://arxiv.org/abs/2310.18385 · HTML: https://mlsanity.com/paper/2310.18385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning](https://mlsanity.com/paper/2203.06875/) (2022)
- [RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network](https://mlsanity.com/paper/2009.08694/) (2020)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [Entity Linking Meets Deep Learning: Techniques and Solutions](https://mlsanity.com/paper/2109.12520/) (2021)
- [Background Knowledge in Schema Matching: Strategy vs. Data](https://mlsanity.com/paper/2107.00001/) (2021)

## Abstract (arXiv)

Semantic text similarity plays an important role in software engineering tasks in which engineers are requested to clarify the semantics of descriptive labels (e.g., business terms, table column names) that are often consists of too short or too generic words and appears in their IT systems. We formulate this type of problem as a task of matching descriptive labels to glossary descriptions. We then propose a framework to leverage an existing semantic text similarity measurement (STS) and augment it using semantic label enrichment and set-based collective contextualization where the former is a method to retrieve sentences relevant to a given label and the latter is a method to compute similarity 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 two datasets derived from publicly available data sources. The result indicated that the proposed methods helped the underlying STS correctly match more descriptive labels with the descriptions.