Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Nan Yan,Subin Huang,Chao Kong

doi:10.4018/ijitwe.288039

Abstract

Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.

Highlights

An entity synonymous relation is a semantic relationship between a pair of terms representing the same entity in the real world with the same or similar meaning (Abu-Salih, 2021; Qu et al, 2017; Shen et al., 2019)
To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations
In order to address the above limitations, this paper proposes an entity synonymous relation extraction approach based on context-aware permutation invariance

Summary

Introduction

An entity synonymous relation is a semantic relationship between a pair of terms representing the same entity in the real world with the same or similar meaning (Abu-Salih, 2021; Qu et al, 2017; Shen et al., 2019). Is a pair of entity synonymous relation, since the “United States” and the “‘USA” both represent the same country: The “United States of America.”. Entity synonymous relations play an important role in many entity-based tasks, such as taxonomy construction (Abu-Salih et al, 2018; Huang et al, 2019; Huang et al, 2020; Wang et al, 2019), document retrieval (Kong et al, 2019; Liu et al, 2016; Wongthongtham et al, 2018; Yin et al, 2016), and topic detection (Padmanabhanet al., 2017; Xie et al, 2015). Extracting entity synonymous relations automatically is a crucial work for many downstream applications. Is a pair of entity synonymous relation, since the “United States” and the “‘USA” both represent the same country: The “United States of America.” In the specific applications, entity synonymous relations play an important role in many entity-based tasks, such as taxonomy construction (Abu-Salih et al, 2018; Huang et al, 2019; Huang et al, 2020; Wang et al, 2019), document retrieval (Kong et al, 2019; Liu et al, 2016; Wongthongtham et al, 2018; Yin et al, 2016), and topic detection (Padmanabhanet al., 2017; Xie et al, 2015).

Methods

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Discussion

Conclusion