Can I read Learning Permutation-Invariant Embeddings for Description Logic Concepts on EtoBox?

Learning Permutation-Invariant Embeddings for Description Logic Concepts by Demir, Caglar; Ngomo, Axel-Cyrille Ngonga is a scholarly article available to read on EtoBox.

What is Learning Permutation-Invariant Embeddings for Description Logic Concepts about?

Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. This non-trivial task is often formulated as a search problem within an infinite quasi-ordered concept space. Although state-of-the-art models have been successfully applied to tackle this problem, their large-scale applications have been severely hindered due to their excessive exploration incurring impractical runtimes. Here, we propose a remedy for this limitation. We reformulate the learning problem as a multi-label classification problem and propose a neural embedding model (NERO) that learns permutation-invariant embeddings for sets of examples tailored towards predicting $F_1$ scores of pre-selected description logic concepts. By ranking such concepts in descending order of predicted scores, a possible goal concept can be detected within few retrieval operations, i.e., no excessive exploration. Importantly, top-ranked concepts can be used to start the search procedure of state-of-the-art symbolic models in multiple advantageous regions of a concept space, rat

Author

Demir, Caglar; Ngomo, Axel-Cyrille Ngonga

Published

2023

Language

EN