Active Learning for Knowledge Graph Schema Expansion

Seungmin Seo

doi:10.1109/TKDE.2021.3070317

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Active Learning for Knowledge Graph Schema Expansion

2022-12-01

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DOI：10.1109/TKDE.2021.3070317

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Abstract

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Both entity typing and relation extraction from text corpora are widely used to identify the semantic types of an entity and a relation in a knowledge graph (KG). Most existing approaches rely on a pre-defined set of entity types and relation types in a KG. They thus cannot map entity mentions (relation mentions) to unseen entity types (relation types). To fundamentally overcome the limitations, we should add new semantic types of entities and relations to a KG schema. However, schema expansion traditionally requires manual conceptualization through a user's observation on the text corpus while assuming the existence of suitable target KG schemas. In this work, we propose an Active learning framework for Knowledge graph Schema Expansion (AKSE), which can generate a new semantic type for KG schemas, without depending on a set of target schemas and human users' observation. Specifically, a granularity based active learning algorithm determines whether a KG schema requires new semantic types or not. We also introduce a KG schema attention-based neural method which assigns semantic types to the entities and relationships extracted. To the best of our knowledge, our work is the first study to expand a KG schema with active learning.

Keywords:

Semantics

Task analysis

Learning systems

Gold

Feature extraction

Annotations

Transfer learning

Knowledge graph

active learning

knowledge graph schema expansion

relation extraction

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