# Semantic Search by Latent Ontological Features

arXiv: https://arxiv.org/abs/1807.05576 · HTML: https://mlsanity.com/paper/1807.05576/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2018.

## Built on by (most cited first, 2 of 2)

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- [Applying Time Series Deep Learning Models to Forecast the Growth of Perennial Ryegrass in Ireland](https://mlsanity.com/paper/2511.03749/) (2025)

## Abstract (arXiv)

Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.