# Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings

## TL;DR

This paper introduces Incremental Semantic Initialization (ISI), a novel method for incremental learning in multi-relational embeddings that improves query performance and reduces training epochs by leveraging semantic similarities.

## Contribution

The paper presents ISI, a new incremental learning approach that initializes semantic concepts based on related previously learned embeddings, enhancing scalability and efficiency.

## Key findings

- ISI improves immediate query performance by 41.4%.
- ISI reduces epochs to convergence by 78.2%.
- Effective on AI2Thor and MatterPort3D datasets.

## Abstract

Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution. Prior work has shown multi-relational embeddings can encode semantic knowledge graphs to promote generalizability and scalability, but only within a batched learning paradigm. We present Incremental Semantic Initialization (ISI), an incremental learning approach that enables novel semantic concepts to be initialized in the embedding in relation to previously learned embeddings of semantically similar concepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; our experiments show that on average ISI improves immediate query performance by 41.4%. Additionally, ISI methods on average reduced the number of epochs required to approach model convergence by 78.2%.