Title: PolarisX²: auto-growing context-aware knowledge graph

Authors: Yeonsun Ahn; Soyeop Yoo; Okran Jeong

Addresses: LINE Plus Corporation, 42, Hwansaeul-ro 360beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea ' School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, South Korea ' School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, South Korea

Abstract: Artificial intelligence requires advanced technologies in various fields. In particular, natural language processing consists of various tasks because computers need to understand and process human languages. Knowledge graphs represent common sense as a graph, making it easy to understand the relationships between entities. Various studies exist because knowledge graphs could play a crucial role in computers' understanding of natural language. PolarisX is an auto-growing knowledge graph that could especially cope with neologisms. However, existing studies have a limitation in that they rarely correspond to information containing numbers representing a cardinal, ordinal, or quantity and can extract only one relationship from one sentence. We propose the auto-growing context-aware knowledge graph, PolarisX², an entity extraction model capable of responding to numeric information, and a relation extraction model considering type. It also enables multiple knowledge extraction from a single sentence by applying the candidate pair construction model.

Keywords: auto-expansion; context-aware; knowledge graph; type information; named entity recognition; multiple relation extraction.

DOI: 10.1504/IJWGS.2023.131215

International Journal of Web and Grid Services, 2023 Vol.19 No.2, pp.137 - 155

Received: 12 Apr 2022
Received in revised form: 01 Sep 2022
Accepted: 28 Sep 2022
Published online: 01 Jun 2023 *

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