# Learning from Ontology Streams with Semantic Concept Drift

arXiv: https://arxiv.org/abs/1704.07466 · HTML: https://mlsanity.com/paper/1704.07466/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

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

- [Human-in-the-loop Handling of Knowledge Drift](https://mlsanity.com/paper/2103.14874/) (2021)

## Abstract (arXiv)

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.