# Exploring Combinations of Ontological Features and Keywords for Text Retrieval

arXiv: https://arxiv.org/abs/1807.07966 · HTML: https://mlsanity.com/paper/1807.07966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Automating Attendance Management in Human Resources: A Design Science Approach Using Computer Vision and Facial Recognition](https://mlsanity.com/paper/2405.12633/) (2024)

## Abstract (arXiv)

Named entities have been considered and combined with keywords to enhance information retrieval performance. However, there is not yet a formal and complete model that takes into account entity names, classes, and identifiers together. Our work explores various adaptations of the traditional Vector Space Model that combine different ontological features with keywords, and in different ways. It shows better performance of the proposed models as compared to the keyword-based Lucene, and their advantages for both text retrieval and representation of documents and queries.