NOEM$^{3}$A: A Neuro-Symbolic Ontology-Enhanced Method for Multi-Intent Understanding in Mobile Agents

TL;DR

This paper presents NOEM$^{3}$A, a neuro-symbolic framework that enhances multi-intent understanding in mobile AI agents by integrating structured ontologies with language models, improving accuracy and interpretability.

Contribution

The paper introduces a novel neuro-symbolic approach combining ontologies with language models for multi-intent understanding, along with a new evaluation metric and efficient on-device implementation.

Findings

Ontology-augmented models approach GPT-4 accuracy at a fraction of the resources.

Semantic Intent Similarity (SIS) effectively captures semantic proximity.

Qualitative results show improved grounded, disambiguated interpretations.

Abstract

We introduce a neuro-symbolic framework for multi-intent understanding in mobile AI agents by integrating a structured intent ontology with compact language models. Our method leverages retrieval-augmented prompting, logit biasing and optional classification heads to inject symbolic intent structure into both input and output representations. We formalize a new evaluation metric-Semantic Intent Similarity (SIS)-based on hierarchical ontology depth, capturing semantic proximity even when predicted intents differ lexically. Experiments on a subset of ambiguous/demanding dialogues of MultiWOZ 2.3 (with oracle labels from GPT-o3) demonstrate that a 3B Llama model with ontology augmentation approaches GPT-4 accuracy (85% vs 90%) at a tiny fraction of the energy and memory footprint. Qualitative comparisons show that ontology-augmented models produce more grounded, disambiguated multi-intent interpretations. Our results validate symbolic alignment as an effective strategy for enabling accurate and efficient on-device NLU.